diff --git a/machine_learning/Numpy/Numpy.ipynb b/machine_learning/Numpy/Numpy.ipynb
new file mode 100644
index 0000000..9c61382
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+++ b/machine_learning/Numpy/Numpy.ipynb
@@ -0,0 +1,1167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Learning Numpy Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0. 0. 0.]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "numpy.float64"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "a = np.zeros(3)\n",
+    "print(a)\n",
+    "type(a[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = np.zeros(10)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10,)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.shape "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.shape =  (1,10)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.],\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.shape =  (10,1)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = np.ones(10)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0.])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = np.empty(3)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.,  4.,  6.,  8., 10.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = np.linspace(2 ,10, 5) #from 2 to 10, with 5 elements\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 20])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = np.array([10, 20])  # ndarray from python list\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4, 5, 6, 7]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_list = [1,2,3,4,5,6,7]\n",
+    "z = np.array([a_list])\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[9, 8, 7, 6, 5, 4, 3, 2, 1],\n",
+       "        [1, 2, 3, 4, 5, 6, 7, 8, 9]]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b_list = [[9,8,7,6,5,4,3,2,1],[1,2,3,4,5,6,7,8,9]]\n",
+    "z = np.array([b_list])\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 9)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `z  # run it, you will detail info about it` not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "?z  # run it, you will detail info about it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `z.shape # run it, you will detail info about it` not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "?z.shape # run it, you will detail info about it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z.(press tab for all funtion to show)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([9, 8, 7, 6, 5, 4, 3, 2, 1])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[0,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[0,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[0,0,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[0,0,-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[0,1,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[0,1,-5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 0, 3, 3, 7, 9])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "z1 = np.random.randint(10, size = 6)\n",
+    "z1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z1[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z1[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from skimage import io\n",
+    "photo = io.imread('my.png')\n",
+    "type(photo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(577, 433, 4)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "photo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2e57f5735b0>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.imshow(photo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2e57f6207f0>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(photo[::-1]) # Reverse image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2e57f685190>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UPGvBYwUwFlBOtMr8zGy0HB6wZFJDtXFrXNA4nrBSWyp87Q35UDdeHIlQ5+htHN4FayO/lvspDK41HBgiFRy0iqWZ6Q5Sl7ak4lznYTpwpHzvAKUKhFot7Iy4lmoKByRtx766EmJW/RqNcQr/vrFZ6hKhEI2V66zLa+1uSpOqrlsFjDQA4ebVeJYDzInRKnlaCbOx2o02rVsaI+ik2Y/gLYVYbZ0sbEvTSE8rq55KkDOyqoeZQKuhpCGZWjy7U+s7M+zDTA6Y5+EBS21wKoBwidX5UcVtDo4616/+wj6uXveZg8RSJWyhoAoxTyHUumSYNSjijFoLM5wV6tsI9gsVKSDOs6IuVB4eRMJMq0IdaK3l1B5JI6ITvVbfqeW21L1erwIo+/TfoQFLwcGt01EFsUiF0+WSxFUVKldtbl9H9ZrqRWoBSD2/tvu2prTGbQFa6xzGzEnRlnwOGBrZTtHv2wutpmsj/JlAudM6O1KlUs0a4yvw7ETK+2j6R5ta0hfX0xPsu6dVRP6ZiKyKyIvOsxUR+byIvJ79Ljvv/lH2heJXReQn98u/SJf/SXlTrPVygKJI7Zq207+y8rNfN68qz69fF+WSg1DK65orePqJjdW8KmmllJzl+/QwvXypTuWvnl+tlTLzX7OcKZrn1DL3DbVKtdW5Hr0tC+X8VdLVpMG0elWZUm3MD9iMevpZ4SAbwP8F8FO1Z78GfMFaex74QnaPiDxG+uGix7M0/0f25eJ9K12VGuWD+jtVDIy7Qjh74gDF7b16pxcDlUdRJYFn7Wg33Kc8csEhLdQnlcjuoNrifiZC3LbsN6J3RPX7Z9Houyn1FeX8Sdq3ymmrC4w6QOrjUpTr5Jv3YxsQp4JpBqNw0x807AsWa+2XgPXa458F/mV2/S+Bv+U8/11rbWitvQhcIP1y8eyQEVv5zazmUvkCRLV3aWfYYgBbnF/VXqvPp4hz4QCmmj9FmY3BKfKzlRd1Ttckvja4v3t/baGGw9Y/aund+tLyvt6YNsxPrUt+3ULklb5sq+c+7ZjaPjk4YN6pzXLCWnsdwFp7XUSOZ8/PAF934l3JnjWCOB9gPX32nPscAFVMLuYvcoJ1JlXSBM7zLGIDai0DXhno7CZTXFuXxLS1AYrFeW6n39HKFdrS3UnqOyuj7fm+/oYpedVftebTll5qcR2jp27D1R0BjfpOMeAbjpxpILXp+ct5v8/q+e+1gd9WVmsfuh9gfeIDT9qCe9e5UUGF+Tof6wAnK7IotQUoUnMESPG4GjfrzWnGaAVsDtDcfNsBOj2zypt9Zo+nBplxe8AMD1pu4Tly09Xv6/EPUGZlR3WGhjanQT7pWG+jrUSYkm5KqEyevkuu45siciqTKqeA1ez5vl8pnhrqBFg0wlL5VksGlGlzHW0EW0ioGmKknqgx8OWDmXqvYytVpYo0Ru2OJM47QM9B0xxkmiJnKLNS7WcP5JOIs+K6kjX/nbbSuW1lQZ0RTp0eqEmhujesyv2a4eAnvFXDHwG/lF3/EvCHzvNfEJGuiNwPnAe+eZAMc2XKozTisxegbGk8trZGCgmSrxJSAio3DssJmTJFjqAaUNoM0boxqgQ8qZ44U6tNpd/r92V7HcvFiXQQz8y0cCdLU6br8jkzkmaaKe2ZVU5u7B+oTrW6VfqlVudiV21L3zWcDC151sdov7CvZBGR3wF+DDgqIleA/xn4deD3ReRXgLeBvwNgrf2OiPw+8BKQAP+VtVYfrJtqFc8lTQ6CQvK0SRRLaSxXuXmT6KRaWvWnLnNaKtTkZGU59bkft9QpL2q1qiZ4Z6Ei3UT2n5mr12VqvKqUuaNJ9Ebqg5RfprD5d1TqCxxsy1qylnIr+1pqEQUOtEltX7BYa//ulFefnBL/HwP/eP+iyyA4zL+NeHM1Z0pi135xGWLzs3dQWdsgUi2nUmb9gbMpqQ0tVOM0ADel7vuF7wIzTiYpYFx6aeEhTjgYDPar24Ftln3iWewUANvGgs2ZFZDpoDiIpDw0M/ipSkMFJSUpt39yufrVqyoRi+TPW4i21sFNF6mtIkamdKa0XNr2KFPXRU0JdwoSl5haJuYbTGhWQW1MuDXZjErO9AjaZrz28myjihVnwBQbsOHNtGUZMmVADsIeDg1YXGosDMvWAa6rT1KmmaI/1NNPlSbFjbg/M6s87eE7lQjT0s364OrMfGaqftPf7AeG/cjrQERcuTxgj7mfj3DzqXnCKp/ncMcki1dXy1xtdVo4JGDJN6GWCyIbrl03bkUtsyW4cPqvEA4ujxNHikglbhVBtX3gTqhIGGsbB0m60jC/K7K/I1WnqeJNfzc9ZX1/5cHCd6uGVd9UVb9m3mn/tD+v3zQkvG2vbZvnqvB41XTRgx6qcUjAkoZSODTFbxEnJ+YKYKQg8naAOVHycloUhXq6fSVQax1d0M+ojxN7v1Cx0feTdu9QpLWpQu9UOtYTVjSflgZPk0I1jetAZTUyaauHQwuVBZgyO7tDBZY0SOP/4k2F69sKUEqJ4bKN9K+ybIKa6lYvutJhNZVNZg3gnW2lFZm+h35aKOtrG2pDvS3N2uwPy3agTWnxO0ClZOmm1sQ9+C7732blzizOllWrOzBaP5tB9RswImCmfVbNCYcCLAKIKqekSqDkEqaq2VaaL/UOdq+r1F2mzYbAVr1nlXSNvJhitd4J0ZRDadtk/wxivVPadB2775Cs37VwIInRoILZ+bWdATJVlLXkqw7ATA4FWCCj+doRmmXDa2qX1IhnmvjcR6yK88krKf5rV7+mZP89De2EMd1WeUcq10HSTI3zvQdRRUWbUtS+n4OQ6aBqWwnQ6j4/ACoPDVggq7x1pIlUibgSz72fqR3U7R+HU0sm6AuQzBLeed7T1ZvGAsHW2k7Lt4zdJhOq7apbqFSpbgbzOFA4qMW7X/51nM/ItvG6VoepoJoVZ8acSvX1FB90LRwasLh1VW7lnWUsRXBkbsWrVSMocU5RLxFha/aHq+Q1QfLdnLnVqLdTziyx0KYKtodyotEai8Wmn5uz9Xq35/XuKlt3Hg5qZbVKEOddeX0w93YFNDPSHBqwAEVLS1frPsOZuTXaDeWWtHX1rRbtoOpXW/o7SjtlPdnBHrqhHNit7S0uvHGRRx85z2Q8YWl5icD3K/HaSv2eAqau38wyTmYgo1VNct/NSNtQrXjngrIeDg9YHPWh6Ov8dDWxrZ1QmXlvVdWEYv4mL4OWtC2/00LTl/MOyC1f5zQzD1sb5aoSb12KEoiimN/813/Ahz70BEeWF/nIRz7EkZWVNIrM8tR9N3CZRdbvPOynzTU/Alv78F3uZre1Fragxjm///vIZmk/uYFi1qlCzc7OyBbRUEa1ZR40ojXetZPUflrytPd3zrVzD1nF9rHOc6XS62wW22iNiBCGIb/7e/8vTz3zGq+88hbHjiwQRyE//VP/Ab7v4/teUduD1/+Oal67lVbXdoVW76DYNhWrfF5VooQWELVIrVJJO3hFDg9YXAlQ3tbeu0EcD1p6X0Zx51acjAo1rCpt2iRTrVo0o+wHhRngb+RRjWusIY5irt24wbe/+Qxrt9fwPI9z585w+doqC/ND4iRBlLCyssjNazf5k0//BXs7E8Z7IZsbO3zus3/B+97/OPfdc2/JPeufofteSMnW8E4AeFCLxYnb4gSoSIrqi8plAygHqPKhAUvBJTKlVKR2Dnve2NJOL58XUibPy+KuQ5HM9SwVQJX5lwvrc+O/htRcUa6wKVvNbybiao2o3btjPh6PuPTWJf7kM3/OM0+9gK8SJLGsre+xEyaEGnxPMNZiLCzODxCdsLU5xrMWY6DX77C6usanP/2nfPjDH+S+e04zGAzwfZ/BYIDne3cIixLU+6fLCbnJ62antTPu6rWwlXFs79UsQWV7Ql1/c7WK/et4SMCSqhblXMqUrnJZRvXrm2Uad1lppg5I8bjWOTieNvdM0FpWBReyTvZ1/pWvzJsKmllc3DKehEzGI778xb/g05/+ItcurzLwhXGiCbDEI80o1OxEMfPdgCgxaGOJxzEdsekmNQvGwtb2mMlowm//1mf5zd/9Ux575B4WFuYYjUf8+I/9ID/3s3+T4XBYNFKceuwX9pdFd24H1cl9Vuo6pBpAsS79uJRkC3XWupHrGc/ogkMCljRoneApL7fps1By9Jz/A1SWjQoFMCpGfCEpqgrzbFAWGbaPu3Xe4753kDXr6z9FPraoQRhFaJ3wneee5Yt/8gWuvrlKGBkim7q/jTHEBuLE4GPxsdnXeQ1YCLXBE0EphbKgtcaKIk4SRlrzzadew/c8lua7vPHGNdbXtviP/sOf5N57zqKU2p+8D0z77Wrl7H747iK42rbNtIVqLVzFy9Y0t7Yvhk0v79CARScRa6s3OHH6TDFPUKzZtaUKVXB2qTbRNe9yUElLBwg1RDlP6+LZtm4em2YUm0bMOp6an7q3WGOJJiO2Njf48z/6PNcvrhKFCaMwRikh1pZJHNMPfMJEMwg8MBatNYESsIatOGbgKYZegLEWaw3aQGINGINBmGjD2pbB9zz+9e/8Ka+88ib/0//433D69CmnD6vSrrxs2gGF6jrTrpwRbBn/TuRQ0Ze2DvGWObLCGWLJPap1p0BBEdawX/0PBVisMbz60tMIHkeOHSOMIvq9Pr7vk8/ku4Zb9eOZjoQouHWue1V11HTxoq0+rosjBxLYXLq4XVxb1FcgYHZHN95mD6JowpVLb/GlP/8Kzz7/BuNRzE6YEGmDMsJmGGEsJGh8SVtnrUFbQ0d8Qm1SgAA9AY1FW4NnBWtsMcHrS0oQYaSJk4TvvHSRF154mTOnTzn1qYjlJvG1teoO8HGAXrnDaPukzwDQaq64lJN/p+/74TN5SRLz3FPfZmXlCMeOHWMSRZw5fQ6xdZXMlQw1A1zKt/U0kNJ76+enazhx1wK7It4xlmq1r6tjNYw6PoACeFl+xhj2drb5w099lq98+Tk2tsbExrA1iTFYjLVMtMZTikhrRAk+CmMsiTbgWToi9H0fAUJj2Y0StDEkgC+KricoUWgLoTVoC/2Oxw9+8GHmhv2CwTRadofqUau8ncVHpgix9LaZoLAui+VQjkY8tb62PNeN+kRCntCkmswBZi4PB1jimLlBl7/80ld4+lvPcP7RB/nZ//gX8IMgs9hUpgpYrCikts0tnch3wWMLO6bamYWIqT7LEVb0a0Vekeu7BUm59lQlZAPa6s7Os87lU1rM2uptnnvmVcJxSGwto8QQWpN5u1Ky0dYSWosSRWQs2hgE6Eiq/EWSGva3xiGhMShRKGvp+zDw0m1Qe9qmJ+f4gtaaV964wvUbN9ne2WZhbr7sl6aGWg1TDJwptDo97EebjeX6NZDkz00p8WeVYRv2Svo0lzoHOX7jUIBlEk4IwzGegjdevsTxY8cByb5SK1iref3Cy0zGW9x//6MsLhzBts5KN/XYuhpVqFc4NoktoWFzZ0EBov3KcB43orgiURytJf0/jmMuXnyDYS/gujaMooTQlMPmSXpimrYWI5AYy26s0cbQ9RRRpoJFWmMR9pIEbQURDVYRihAAHaXwRBBribVBa8OFizf5X//33+atS5f55V/8Tzh+9DjYlpkXt7kZUUnO0u/E2NgvNMDhlll76MTNFKhaXi22ST1SbSwOohEeCrBs7+zxmc99AxsneJ7H0mKHN1/7Do88/iQ72xtoHfHi80/z9qWLvP8D6/zQJ36cbm+QJq76d8l7ymZSxQVHJZqQSajsuVSNfOumyyRXg5Dq8zEuz3PXo0ieV3XwPE84dvIEp84e443LtwmNJtKZjZHJSqXSchNrMcBekhCI4ImQGIOHpWMNoU1P5c/nIBJjiLQQKyFQqZSKEkNkLEaExMDq+g6/+XtfYK7f5R/8yi8RBMG+jryKtVjRL7/7kEvb/M7WX7Yoe1JoDM674qeqY1eX+tuSaVbKmN6WQwGWSZhwbS3EJAk9X/G1r73A0vIJHnzkcW5cv8QzT32TK1ev8PRzV7h5c8yT7/8wnW6PnDyg2l2pVmXrtAk4zoFC9Lb5uBwuV0gE91kWq7VzJQORk1eLMLLWsrmxzqf+zWd4+pkLGGsRUVg0HhCIFEtb0uZYIpvOFa7+gB4AACAASURBVPgiWKNZ8BWnPYXvCzvGsq0NOoufAGNrmMSGnUQyX0VaCWPBWo1nFTt7IZ/+7Df4+b/zt1lZXoY2iT2D+VZNhumTlgeFk51VWGUUbPsb6762lctc284fFDsmizSza3kowGKMRXU6bO5F7IaGexM4e+Y06+u3uXz1LZ5+4QKrtzbZ3ovZ2Njh5Zdf5qOfOFLaKdk31QQqc5LVgcsFc0Z8Nbdw285FmbJ3txG3YjPZNmyQe83ysbJYgk7A3/97P8fjjzzN7//hl1jdDen6gofFaE2SEZ+2liSTTIEIXRH6WE55wilfONpR9L0UYNqkSnykLdcjy8VIc03DtpTObWMMGsFgwAirtza5cuV6CpbaIRyFUGy14JsMvxLF9ZAcEC2ttkOzux1M1EGT/deiTVhIOYXz1AWLtaZ9B2sWDgVYEHjg7BL9XsCVqxucOHWcznDAs898m7evXuf21oRrt8ZoDVdXN3nj4kU+8pGPk375RRyVSorGF1wkL6Deqdb1rJVx63KmVNtS4ORZpqZNzVHgJE3pq6Q6qeUnFqzWRNGIC2/f5O2b2ySm9N74nocxJiVemwImEBgqxQlfcdKDUz7cN1Tcv+BzbKAYBiqdX0ks2yPNjZHm6C68GVlejAybBjoKREkqpQARRRzFfOWr3+aJ9z5CKq2dfmpctNxPo6+mHjU95N2XaQQFCFuStwGl0CrqqlaRTZ5vFbiCzfYC7R8OBVgy5YBHHjrBjdVtxrEFUezubvPSy2+wurpNGBuwwu44Ye32NlE4ptcflHQ6ddVy9doFg22JW5EKNW+aIBnXdZWPqiLS+FR0lnedtoxJT7X98lef5U//4mnWd0aF2uVnG7h8EQLPI7AWiWN8YNkXnugJ75v3OTfvs9JXHFv0GQ58REBriCNDz1cEgcLzBH8nYRfh5cggSuiJYjcxRDatoqeEJBxjjEnPhrZlcw5sxB9Uz2pJVCF5h/FIC3G35eFWN8+wjfzTrG1RTln69xFYLPDa27dJ3rxFrA0vvX4N0XDPydOsLLxJFK9iUyZLIrC1uUk0GdPt9rITp8WhW8cEtY67N/+ib83tXDnV0uYSI8+nBIOj6iKScWUnrxIjlnJOIC+kTnlpvpPxhK9/6zuEUYK1pHYLFm1t5gkTuh70Ebri4wMnfeEHljyePNFhacFn0PMIOgrxMpaTpG3qJbAgQpIBb80m3DJCKBA7tcj74MEH703V2GJit9SrpFL3tvFrYfvT9LYpObQ+LTpwih7o3laHsV2qVV6UILSZLVh3YNbDoQCLIGxtx0RJqkNv74x447WLHD+9wtLikOXFPjdvj9Da4nsKJencgTUGrHIMagExtcMsHLsmi9JqhuaSw6lV/rxBLvVN827+OCpYLphs+rpSrjUkcYiOYrZ3J3jYdHlKFidQgrWGJDEMfA/fEzoCcx4sdz2OHekwN+dnB2anp96LACoFs/IFzxe6vjDf8binD5eimD2EPRQ7XuqKBlheHPLoIw+k/aly0eI0v3bVrh01lR/Xs1UPU+BRlSyVeKYRNb/Ik1QdjrYAcfo8BYQ732JNubhSJyHR3iZaR601g0MCFoAg8BClMIklmsSs3lrnkfc+xMc+9hGee+UyaxsTjDEcX57nwx98jC998ct86EM/wPGTp8rG53iwLqMR8qn6Uo3KImRcN42Yr3pu8tGqElVyXZsVWihwuUQqEOKOnRSTlanRbEniiEkYpx4uldYwyQZcYekHioGkwnMtjOmL4HcVw77Q7Sq8jsJqi0lSSrEIRluMtinhY+n6irk+nALuDzWvh5YQmAs8Hjm9xPYk5r/4xZ/h7NkzmX6YtsMag82lZPqtjynOIput5avQKWAwrmetlrjs9jZlOA0mS1dR1jJk5M+Ksc/dwDkwKvHdUnMiUaVjRsDzPHQcYvX0jz4c5JMT54B/BZzM6v8b1tr/TURWgN8D7gPeAv5Ta+1GluYfAb8CaOC/tdZ+blYZSoRu4NEJgNjiW8Pn//wvWdtcpz/XJUksC/NdtrZDHjt/D1E44ZlnX+b973tfJl0yApVMCS/IN9sXk0222XxEc11LypiV/20OLko1On8izqRc9rQEaWndV6K4Vn9WjjGGrY0ttrd3EWvw8i+XWouIJVBCIIIouDWOmWjDvV2PjywHnD/WIZuYR3mCNaB1ynlzF7HyFCIGUZZeV7Es8EgSIL7hwsSSeB7vf/QUj773Mf7GT/81lFJZYkuiY0abG+nyGj9Aa8NgYRGUR03pKltn8r5y9SFnRZ91lSmnv43FWFPJK+tVB0j1r7nVy5fy0MJsbPN1gHWwlNInZYxWMpGkPOZWTuEFXaaFg0iWBPjvrbVPi8g88JSIfB74ZdIvFv+6iPwa6ReL/2Hti8WngT8TkYdnfadFBLoiJNqCUsSJ5vLtTba++jwfeOJRHn7gEcJoxGuvXODmtWu8NrDc98A5dve22d7ZpNvp4vs+ylMZVy4HbRJOSJKYzc0N5ucXWFhaRrLP3xaeYcdukaxCNq9YhWumQKoCgUKoNA56c1lt/ZAKa9FxgjKWnqdKxqvSCcmOKCaJ5lasEWt5b8/jbxwJ+OGzXRbnPDxPpXacAjxBxE/vPVBB6mLuIxBZbGyY6wecQugNoDOGm3PzfPInfpQnPvgk3V4PjCHfDpfujbEobREb4SmF1kn58aZCW7JVZqJzSpRMEqXr30QEycCY2weQcvOM1Mklf96BxQJah9gdC7FJRPkYVCRNbYlL8Swfj3w803/GtOTrhIN8n+U6kH9sdUdEXib9qOrPkn7kCNIvFv974B/ifLEYuCgi+ReLvzatjMQYRmGCZyFBiJLUI+RZGCufcyeO4kvC6vWrbO2O+MtnLmDlbf7sK8/R7fU4cXSZH/3ED/Dxj34YTwQ/O9UkjiM++2df4GvfepGd7REP3n+KX/7FX2BleaWmgjnKVyF18uU2tHjAslCoGTlXy4eywfqcD7Xmxr/l5OlT3H/uGOtbeyTaYGw6V+IrRWQMo1jjWcv5fsCPLfv85PkBywspU7AW0OlgxwjruwnXboXsRNDzhSN9xVyg8D2FpyHwPBYRRFuOKsEsDnjkvU/Q6fZT3T0TDzoas/bqM9x48RUGi8eIwxjd7aMCw/kf+XGC7gBXuojLbMQheFKCz/cnFf2VrfPLtKVSZ7aOJLBZCbUub1fkqqGYKC6Eii2XClK+K1W1XLewGG2m5JqGO7JZROQ+4EngG3yXXyx2v1bsBV0SY/BFpUvJPcFaYXdvwle+9hTyjWeZG/ZAxyRRQiRgTchNm6pwF966yYsvX+T27S0++cM/yMLCIsrzuHrtCp/6zFe4dH0LY4Sra3u8733f4Uc//lF83y/mSRytzBEGaUfmDCh/V9esssiOPUQZ07GhrJsui7K4tMI/+NWf58r/8n9x+cZtUiEh7MSpHdNRwpGOz5wn9HxFt5fq2drk26QhCg1v35zw9nrIzbHh5sSwFhs8gXNDnyeO9pgLFL0gPc93NzG8vp1wYe0mzz77Ep/4oY+ktGMMiEX5HeZOP8TcrZC3//iLeH2f+/76j3Dk8YcJOr1CmuTOkJw288Y1PWOOeBDH/rNl2nZvWq3TasuFsnnoEkjiJE91UeJ4wurVN7l55S3ueeg9LB+/N9MSUtWrGEKbSUutq6KsFg4MFhGZA/4t8N9Za7dnHD7X9qJRA/drxZ3+nDU2VSE8LJ6fL89ID41Da/TeCOWpdCWuCNoKVpF6xpSwEcb8289/lUceOsdZgfn5efr9AUtzfS7ZbbSx3N4Y8c9/+zOcv/8+zpw5VUgGK9m6oULtklrN66qXZKNjHK4qxexwymxL/cQdc3c9mohw/uHzfPTDj3H9j7/MyY7wA8tdXt+NeHozYi7wiIxhR4S1UJPovBJpHibR6FBzYt7jyKDPre2Et7c0z23GbIUJa7sxr2vLmTmf5aHlemj4+mbM1zcidiz8P5/6Mz78oSfp9rpFRS1Cb+kI9/zghxn/2dfpP3wvZz72A0R7W0y2NuguLGX0NHX8awNfevjcSd0qLbi91FS2TDwhiiI6vX6qZqu0//P1fBUPF4ARovEuX/ij3+HV519iFMXc99AL/Nzf/y9REnD17TeZWxiyuHQUv9MvVDZjp3vC4IBgEZGAFCi/Za39g+zx9+yLxQIkWjOxFl+lxlnXE7p+uuVVk6oxcaQRBZ5Nt9gOuz4LSlJpJILd2eWrX/wK995zmkfe8yhHjx/jZ37qEyzMPcvu3hhtPT764feTRBG7OzuA0O310k1mzuaW3G5xVbCKrp4PY25M5g1xuFwhQHIJ5X6BtOCSacQPPvkenvrC1/h7J7s8viJ4gx5/dGHEX6yGrMaWyCo2YkOiJXVWZHp/EmvC0HBrK2ai4dU9y7+/HbI6STijhB873uGBox2GQ4/dxONTN0K+tBEiojg16PDJTzxJEPjY2hIQAPF8+oMO/vyQVNHb5sbVy5wZPJmqUo7nkLI1zn3FWCjeGqMpPVHZc2sIwzFKKTw/QIqPJhqSOOLLf/IpLr/9Nh/94R+i3/foDueZXzqJ53UyddkgNmG0t0en3yfoDrhy8RWef+o77O1MiEXYWB9z/dIF3njlNZ766jcg8Ljvofv5+I98goWVUyhruXn1VUa721Pp9CDeMAH+KfCytfafOK/yLxb/Os0vFv+2iPwTUgN//y8W23SjUpxoTLYnPEkMsUnPu/KUwveEwANB0fUVYwSbWLQy+J4w5wtowzPffIVvPPU6wee+zt/+mz/KB558nFPHjuKLz2Bhkbn5Rca7u2ysriJAf36BheXlGseTVKI5LufckM2BxKxrVT2TXRp6c/o0vVecf+g+PnnfMveYXc4+NIffE/5zX/joqS5/+MYer+9Zup6iEyjy44yMscSxYWs7Zndioe/RW/C4fmXEUd/jRABnjnY5dyqgM/BZ2xUiJWilGMWacysLfOzjH0wnQiuTf9m1TtBa43sK7ATFHuOb10juexSvM8xczDlzMZTL+1MPk8VitcmYTtp5cbjHc9/4Cg89/j6G84vcuvwW/qDPxtotnvrKVxnOdxgurbC8eIRzDzzI+upNRqs3WXvjFXaubfLl1T/A92EngU6nx9GlPnsJJKFmbiBEkz2iRHHP2Xu4ubHJ5sYEbSx4cOXKLf7V//mbRJMJJkrd4uPNF9i6eIHjK4sQdNnau83G+uZUMj2IZPkE8IvACyLybPbsf+B7+cViSdc+eb5XeCQ0wihMXYpKGbqBh6cEXxkCI3SUZLv/LJPEMNKpU8BPLGuTCXptxP/925/lka8/y5OPP8DSyhL/7kuf4dypY/ytn/gYrzz7Il2/y/2PP4y1EHQ8Ot0unt/NPhulM1rwC6PDAIjgkRnsDvUXurL7mwXXQVABTCatlo8e4eNPPsTgtZdSFaPjQRDz6IrHA8eW+a3ndugh9Dte+WkOYwkCj6NLwsKcJTbgb2t+bqXLVmR4cMHj6CBdkSwizHU9Hl3u8/yORolwz6k+16+9wc7OBoPhPL1ewPb6GuFkTKfTZb7fZeljZ1kNt3n1+ZdYv/waVvpc+vSnuP89j9IbLDC3uEwQ+ISTEF9BHI6JRnuoaA8fzatvXsWK0OsO2Nze49qVK1x++yqvPvs8i90O6+sbbMeGre0RSid0e4qd0asEysMLfPQkYjEQ+sBDQ4+bScQosezsGCbRHtGmx3ZoSCLLoK+IsEhsmd/Y4dJegrGpfaeNRU80azpk2IUgG4O9seaVyztcXd1jrqc4cnKYgmtKOIg37CvFSDfD9+SLxXmjEp0tO5BUBHtKISJMYk2sLZ5SBEqIgowARbL5BiEyBg8YiE/H97LOiPj2i29z8cYmvqe4eHmdmzduEY+3eO3lq7znnuO8fOUCcRjR63ZYPnac9773MY4eOcogsKyv32J5+QRvXHyDcDzingceptPrY+IIYy1z8/NYren0BggWHceI0aAUnt/F8zwSozE6IQi64GWeLJMeJGFFYcUShyGdkyewr7/M5HbC9Re36S8E9D0YLAZ8aCWgK+lqZJWpc6rjYa1N3eWThGhX08HyyKKH5wWcXvJZmvfSM8JU6jh5z1KH5WvCUq/DShDz737/U+yFlq7yuPfoHHs7u+kqAiWcOT7HX/vh81x+7jqvfutlTvgGYyzro5AXvv0CgnB6sUev73NtfUwggkli0JpHVzzOn+yw99Y4dSp4ilduR+xpyyixXL1wjU1PsRlrjE53dM4HgiQGpdLTacJxwn3zPmNjiY2wG2vuG/q8vRnR63vsBYJSsOgLR+d9bkWGvT3N0UDRS6eDSGKNKEWiDXGSSuKuL9k5BeB76aLTNa2ZWMN8Yov5q7ZwOGbwbap2FSae5N49XRzVM441IoaO7yM6wQBBtpcjc25gjGXiWbqBh1LQ9RSxhRur28Q6Vacu39zm8uo2GLhwa4dHz8zxxJkhW3sjLl/Z4Orrb2D8HqdW+vQI8fwur15dI1CKt198BeVZwihmTxsW+10W+x5j62Hw2NscsdRTdHzF0lyfQb/DhZsjup6lNxwwImBxZYGtjW0wmmPLcwRKsbZ6A7O+xYOxZTiydIcBZx4Zsns1xOsKJ+d8SAxWm9SolXR7sAQKryd4HYUKNIOhRWuLUop+T9HpZ948DZ4H9y74PHZinrXY0NHC9m7EXmi4Z9Enub2OwnK0I8QJDLXGRBG7uxPC29usB0JiLKGFaJLwyJGAc4HltZsRJoQdndqR9ywolroeyrM8sOIz3/NZizRvrhm8rkoJVRt0YljyBd+H80sBHdIxXI81Yw1Jx/LwgsdaaLi5q+khnFvwODYYcHU7Zn0CYWKYGEgiA6HhREd49EhARyDYMnQ8GMUGayxiDUcGcGxeEVthrC2x0RwbehiEQV8YdgVPTZMLhwUsOB4kINYm3QilQJkURKIknYuIk3RCLktjbHogg7Hp3vRJYuhb8Lx0Ug8LKEWidZpnYtI5HE8xDjVv3djjSNej2wsYerCkDLfHO7x1dYdBIGhtMQl4vuXG2iZzviJKDLuJZW9nwlULiaRq0GRiWOh5KUHElrmOMIosXQ/wYDu0eJJOj/gW7h169DKi3xvFDGI4pi2+76Fj6M0F6NAw7CnCsSFJNMYIyktVMOUpvG66BCboKqKxJZwYjKP0Gm1RXtp/ywP40Kk5vnlrgjKgE8OxnvDgosdWbOgqxXtOdkg8y+BEj8AzDP10XmQ80XQDYegLj5wIeOxsAEp4KPIwc8KlzYRjKx73H+8QxYY4Nlzb1SzEsBdqhh3Fbmw43hHmlXCq67EdGY7Pe2wl6XFNRzsevZ4wjg0Gy8kjPseM5f44wGpDQNqPCx3FUkcxigw3dxMW+4p7ljyGXaGjUxf5o0sBz28kqb2oYBD4KLHoQJiEFt+HaGSJlEFry3y/x8ZmlE6MTwmHAiyW9LSS1COZ+0gMnngkJj2m1PcUHc8nX0ChjSERUOKRH8/qez6JNkwiTeClTksFKC/NUdv8F9DpRFxshFdvTBh2YzwPwpUu25EmNHByucNonDAKDcNuQNdXjLy0cw2kxra1BF7mCFDpgXfaWEZjgzEKVDp3sjs2bE8sYmChA15XuBFqzCjd8osB0R7v0YaVBZ/VNyd0fGFuqOh1BB2m0tfzBGMsfkdhY5O627sK8QWTWOKQ9Lij0BB0AjpDD2sFv6OYm/d4IFZ8Z8ujBzww57GbWN7ajotVz+fPdTh6Ysjw7Aq3Jx4EHkHfZxnNUmDBF44NhV5Hcf1GxHxPcWzJ5/y9XXp9heeB9YUrqxFv3I44MfQ5OudxduARWUUSWU4MPRYHHkcTiCPNmcWAvdigRTg232F3rOl0hNiDvT1LryN0ggCrhLX1iN7Qw4qlnwhHu4qOspxY9ul3FOO9hMRa7h966K5HJEKMYRJqxMDmKMGIZaGjON3tMNaGjZFmrtdhEiYtkxxlOBRggVSqaGOK7QYdL9XJvWwmXeucKNP958pPj/cB6HRSx0AYZzqvSR0GIkJiLZ5JdXud6GKexlMegVKExrI+TtiLNUoJ63sxkyTdSbgzSbDWEnQU4ySdND0yH9D3hElk6Igw1/GIM6t9kB7ORa+jiCOL8mEcWxJjiWJb+PM9P1WMo8QQxpY4MfS7ilXf56ZojvuKpWWfeKJTtUrSxZQ6ydalKUs0Sgh6Cs8YRGVLXXTqgUpiTeBDkmiG813GowQ9ifAJsNsxkoDv+5w/1uH12zHj0LDUTectdvtLHL3vITatIewNGS7v8OiT97B38SaLesJwqJjvK7SGY0sdtLWMRdgZacLNBN9L1UJPPB5/aEA4NuyGhrnFACWpb3F+zic2BtEw3x8w1jGyq/G7irG1BMtdbt0eMw4Nc72ARBSesXQ8wZ/3CZP0hM7B0GepA77KjoHaSlL7xKRr5k7MeYgnbIYW01F0fI8zCx6eL5AYfI/UAQL4CwE6VPxWb7rRcijAIpmr1leK2KRu1ijRBJ5ivh8AMI40fgDWCMqm7uRJ5hDQSXbsq033sfsKsJJJpZTYREjz14ZEW8Rqur6fuqvFoI2iE3hoI4SRZZIk3PBg2FNInKA1GC3c3I748KkBp/uKni90e4q1saGjFCMsngUfy2IntaX24lRiBtbS7Qr9riKJLTrJ3cmWTkfhA8cWA24Y4TFl6fkK04E3r4w4u9RBiWCypSEqU1dNbFIXXTaZGE0041F6zthwGDDZi7n51BrGCn1fmKD46m3L9URhhj12thMmYhgOFdoDCQTv5D28+dYG12+s8tGf/BmufeFrnH3PQwyeGPL6t1+gs2Wx6wknjvR4+NyQztAn6AasX52Q+AbVyVRerbm5PmF3oukFim7Po99R7I4SunjsjjWL8x3Es+xtRHikYxbHln7Xpswp8Fia91lfCxFfOLqUnoOGTg8QnOwmiAG/72WOAQsizA88kthgJJX2kmg6gbDYFfA9Qm3oDT2QtO9H2wl+mNATi+99n9gsOSHky6wDlW5xFbEMugo/UESRJYpMRvypDz/JFvAl2tD3VLpVNkn3U8c6/fW9gMKDKxRHCClPQFmMgkmc4GcRfF8Ra8soTOdxMKnqpzTMzwWcWuozSizdjsfpoU+/C7d2JiTaEhjLgyuK7d2EE31LRLqgcNAVPJ0eSDExsKdhY5TQ76RtOT6neO89K1y5NWJyeZ14V/P2ZsRK10slrJB5C7PlJhasgWSiAUEnFs9TeIFPZGFPC7ciQQvs7Wi+vp1wOejhKeHqbsJo19BV0OkLe2EqEU/pDr6CKzseiQpY1z7nV46yffs2t0aa7TBOPUbzXY6Kz40rOzx4boWTDxxhsjtia22P0ShifT2d/FwaeMz3POYXAkQnnDndZ36xRzjWmIkGNCeXArq+x/ZugjWGyYamJ4owMmxtRCzO+Rhr2d6O2R0nbI4Sjq906AJ+R6F1uliz31NYkzoQwtjQ7yviSHNsLsjOS0ttxsCXzKso6LEmHGmu3QjxfCGJp3/j+1CAxZIa57nRnmpi2fE9cZIeeK0UYViqMpFOD53LdxYqUYikxwsJCiPpKtqup1IAZkcMdZRHgkGRHi/UyWbnrTX4no/RBiElOrEQRRrtKfp+phMbw3fCHv2zT9Dr97Be6qrVnscwCtNFicayTYKJYsx4m8BakightBqlE+xkhPY6LB47Riex9JUmiUI2jOUbuxN6/QWCaMT7BmOOdrtEUape+r4qXJsi2bIPbGbQp5+buLST8OrmBKsNR7seb23FXI0soRVWLVwyI86sDHn42Dy3N8Z0Oh6iNT1gpRcw7A9ZPnWSuZVjiCiefPJx5ucWuPn2DeKRIYktBIrbtyb8mytX2Bkbjr60gx94+InGM6laJMCZxS6dnmJtFLO6NaHfVQw2Y7wbE5SxDCx0+x5e38OEBh1bFIpNnfDSjYgecGyQagzWg7dWJ4TG0u2lO0N9k0rv8Vjj+cLigocVSGLDZKyJQk23I/QHHgvHuoSRJtyJ2V5PMCrts+Vln+PDLqrncez0EP8L3weSJV9t4knqBxfJpYqks8iSqlBKhG7HT3dVZvZLFMck2tDxU86iJF1TprD4KgWLsek8jlJpGm3Td/kCcUnZNV6Wh9E6VQ9VCrSIlHuFsebimzdIRhFL8326vYCg22FuYZHh4iJzC4vML80jKJIkxowGDPp9ksQwiWLAEoYTJmFCMFimG8DW5jbroy1u3rjN2vUb7Gxs8XMLhh885SHGMJ4YohjA4CtVuMqLVdE2Bcq1Tc3nL+2xEVs+cqTLsa6iv9zhnhh2jfBmDDe2E26FmnP9ARueoTsIOKmEo3sJ/SDt86tvXebCG5c4enSFCy++xPziAkoU95+d5+ZOxKvXx+B5jCLhzPEFnnhwiVcu3UYZxWLfY6A1G1shK8sBg17A85fGaGuJEs3C/IC3397GV/BTHzjB8XMrhGHI9VdvM/Q9jpyZx25FPODFSBizMPBYOjJkZ6xJbt3i5s4Eb+KxPN9h6XiPJAqLrQFB10fHlg6aXt/S6QbMDcHGCZO9mKAf4A2g1/OzxTSaXk9IYsvyQJBwQhAccptFiTDsBRibuo1NojM7IyXldLdrauznm7skI3ZfKRLlZfp/ulpZKSFQqW3je6kjQBlBlCnUMCX5GiuwOgWWKIN4ClGCSdKJT98TOp6HQogSTcdTDH3F3sYW4+1dJtbS7QacPaO5b2mJ9bU1Xn/5VTa2xyz2fXZuraGCAI1w/Mwx9CREJwmREU6cnrC3scG3v/kSl1bX2Z1E5J9N2LpvHqRP0BESnbqOw9jiexaloFCtRbAajIZwnHAyUBz30/33UWgw2uJpGEdwZWIZJRo1CdneCjmz0Gc+8Fj2FGeO9tkdj5mfG9ANLI/178MqRdxfIJhbZG33TZjrs72bsJloFjuKH/rAWY4OO1y7vc3WRKPHEQPbYXU3bceRk12eeWWLb14ZMbYGz1doPeKxc4scH3Z51C6clQAAIABJREFU/kbCC1duMtlLtygsdjqc3NthaaHLiRPzvH55m62xJdSwsDTg4fNH+KHjXV69uMVkTXPjZshwaJlbCBA0/qCLpxVma48jJ/usa4/L63ssezBej1hYFuYGPjuhT7QdsjLXYUcb1iYJly5O6HdSx8W0cCjAAhSL+brZV6k8lRr9otJdgImWwtbwlE1PPfHTbbVHBh3GUYJJ2SwqM5qthV6Qek+shcQqbLbJzNr0SNR8YaQ1qU6LyjxWgWQTnalLU0Shs7maSEO/49PxLHGoOXbkOD/xyY/xpa89zXvOP8TNKzfw9rbYvT1hb3eMp9KVBjujTfqBYmsvRjxhffsWtzbGTHb3OD3X5VIcM4pNyiiQYoVCr5sS++5E46lMEtp0ohGx6ATi2LLQ9fjwccWw5+MpodfxEfEIE1gZGQLjsXpjDzPwIUrYuzkh6gir4YTXTdofRx68jVWa7d2EI2cNDOaZRCEXV3d47oUr9LuKlYEimcS8cvEWPZVw4dqIm9sJHYSeePQSy61JzD/93FUiK4wEJhqYGMTA+nqIHwmdbkA4ioiSmNujhO3xmI4nLPcD7j27wL1nlrm1tsOVzYjzCx3O3Nfj+IrPyVMrbL64hxf5BCsJ9uQccaQxo5BbV/bo9RRvXNnl+bd2iY3h9HKHD79/hfvODTFJwnzosRMq+olmfRIQJhGnH+5CEqC+y52S73qwZMazKLDpOjBrU1VKRIhNeqavUpKdEV5yV98XFjqKY4Mem5Eh1BaMpR94hdfIekK346EtjBKDSLorLs5ULchOaUwMiTHpeQCSLpfwPQ8xFs9PPV7YdBdhusfPcnQQ4O1t86XP/jljrVlfmycZ7RKEE5IwZN6Dowse/Z7H9hiGcz79OZ8kgdjo1OffDwiSGH9pyOsbe0SJppeLDpsa//MDj3FsGUUmA4tCa5sBGuIYPLHMdxW+snS6QpwkjCcxk0QxToSxsQx6ih0bc+P2GlobOp6kH0uyFt/zCI3m4ltXefvqGvfce4Y//sLTPPLwfUTaME7SPS97IWiTcHFtwnwHdiPDXmKJRXFlL+bI0OO2Ea7vZLsrvZT5HZ3vYCOLDS27nYTd7YjHHzzGKI54/TurWCwdH0bG8Mxr61xZ3eMD55e59+QcncSws6GZTBKGwzl6jyyQhCMiT5ib6+FHIWbQY8n3CTzDzdc2CaN0mmB9O2F7K2a8EhIMAqSbsNhT+BKwfXXMvcf7aKXSdWH52ruWcCjAki/2lsxdle9Ys5Ia7oGXnuubxsv/kYKi5xNkADJYIp2emZXkvzZVpzYnMXFi8TxBWUk7JtstJyq1U4pVjjadCFUIygiJNXQ9n0HXI9E6tZMig9dJ3Y97oxCFIUksly9cYm9jBxWG6eQhgAGlFJMkoZsYtnYTxpFmFBnevLmNimM+fnaOp2+MODbss7ozYjlQ5XF3Ygk8mOspNkYJuyHEiaWX6ddpW4ROR4FNJeRolOb/2nrE6yPLqoVbErA98NBiiCJDIIowgdimxyPpKCFODPeePs3c/AJzwwHve+JBFhYWCRPNKNGM47yLUjtwY5yullCiCK3l2k7EjT35/5h7sx/L1vO87/dNa9h719DV1fMZycPJpChSJDUFgmJJUWQHshQPsuPAipHASW6SqwC5CZC/IkCQuwRBLgQhkgEZVhDBSuIojGNZog1xOOQ5PGOPNeyqPa21vjEX71d1Di0ehoYUoxfQY3VX7679De/7vM9A6zSdlQ09M4bXHsxJofD+buDJFHhx4Ti+0eAaGDaBdmZYjZHVOrHfwtFey3JI/ON//gxVMnmIvPnOimIUL93pePXegqQVi3lDe7blu6+fcLn2nKw8X/rCbX7iJ1/iiz4ybxvCZsON2x0YxTRMxFDoDywBWBw4bIYxTgRnKDF+5Dp9LjZLqfQUbTSTT7Ip9FUasfQdi8bijCgdrsyzZ87QXtu2KoapGmtT0M6ybw3TFOvsRv6OUbBoLUNIrMZ0Tbu3WlcT7owqBaMEgStFXkdMMPosuSjAQCKmQr/nmLWGZ5cTukBcneK03D4pZVqrOR8zpQWswodEipnVxjOGQqstuxI5WQcOu4b1xnNr1nHYfqDLv0IIrYbWCqQ6hcTgBdQwRibnTV2cKYGPha3PPJkS3xoyK20ZmsIbj9fcP+rpDXK4ZJEzD0kQt8dPn3J+tmL/8Ag/RS7XGy7WF6x3E9sxMe+s0FlqaauUQl8hiFrTa3kNqylglWbRGlqt2W4iqyEyhkTvDKtNYLsNlKJ47eM3mFLkW+94xkkIqWMQao8qhT96/ZTXDjr2TQFTGM82vHGyoT1ouf9gQX9gOeocMw2vPphzfGC5c/8Q61oUE+OlcOq6xQy/KRif2G48OYFJifZmx7xtMPtHtIv5R67T52KzgJDwqL1E4+w1Vb+2IficaayhM4qZMzgjoi+tNEXBzmf2GoNL4gu8C5HWiojIXEkAkFN4uZtwTnM4b6TWhfqtsno/tEhV7ZOmkIhJyjmjIYTEapSIiMYa1oPnoHcYpNluG8tmity+s8d+p9isPZspshoKl9vEgxsNjy48rnHoDO+MgsQ1zpFUZh1rX4V8AUoSSDpFgcRDEq+CXZChqi+FeaO5NbdoI/qgPaP5FIa1ibyL4TtTZjV4hmeJF49mLGaGkDNaKw47g1KK9x4/5O13lxwdbbl93PGdN9/lN/7+lncfXVZtiwyEtYNhSuSYWcwaKIXWKiyKlEWnA8ILSyYTllGskZRiDJkxeCEtqoGJzDffviDGQm/MtbDs/l5Dbwv7qrDaTgxjZL9R7FLCodg+3bBnC8eLQ0zX8soLezSusBk9D998xOnZjhdvN5CEjj873NG1msbJTW1bg0qZab1huCyU0y1xt/vINfqcbBaJQ5DTXRp6rdX1jZProsmIQCaETJsVB61hNSas0xzNG6yRDMYhZlY+MvmIqa4ii8aQcqExhpCylDAFjo87TsdEKJWhSsGiMEY25OAlJVhVIwyroTMae6VnQSjl9/Y7cs7sdQarNRfbwF5nabTi5HRHzgVnFefbhI+JXdLssiGoTOsslsRhZ9gazclm5PdPI18+Mty1CmukJOxRTFGxugxsA9InlMKuKJ6OUh5+8iBx2FYQwhmGmOm0eImd7CYJPwqJ04sRYzramUHX2ZUGvv2dE3wqlHLO7//jP5Le5P0TSlHMe800ZXKWSTsJXjlqeXBjxnLjmaLcUlL+ZoxB9DdFhHohyq18c7/jU6/c4Ox0w8Vq4vJs5IWjOfsHc05P1tw66kkhstl4+tbQmsKNDj7xYI/HTwdOLyKffmWPtjFs1oHHTwb6ueXpsy2PHq55tg4UpRhi5ttvahqjSJVXd/+4YXGjpZ1bHrxwwHoz8s6bFxirsE4ycz7qeS42i1KKxipSypSipY9QisZ+oJTU9YQfI/hQGIOURkXBGDKxRI56y15vhTelNE2rhUiJ3CwLJ+jSbOYEbdOKk51nmDJ9Z+kqU7l1BoPwvKxW9M7gnGHeWnqnySnT9ZbOStkYcyH7yAvHPSoXtlNkr61anPWOvVZ8h1NtxmOGh+vE5ZDodcapQs6K5S7StJbWWb49en7z7ZH/8JM9B0YsuzqtOVCFKRimTWK5SzweEtui2GbYFPjOkGiNCIE7I/mau1J4O0bOhgkArQqdKqRtYEwF3Wp09SoTgqniYjWyWo9MKdM2Qs4UbzeNQjOzwrI+dMLPWl4xvp3i8MBxvvasfSBmMfrrtZE5WBH+2ptvLcmxCK8vFV55sMeLL95ic2+fd98752Q5oshsExgLeSicXnpQir/8cw944W7H0ycjk1fEyfPO+xu+9WRLCpV6VMGa1U6hyBw0hr6znG4irz/ckoD5Ny6IObLdeHots7fN7jmf4Cuk/yiNwLq66t9TyuQsk2tlRQSm0NW4QWolZzRKF3ah4HPhZIh1Ig8GRTEapwsHVjaK0frKdBFF4cHCMXSgrGE5iAy2ayyNUjinWW4mbu11cuMYRYxX9rDySXwUqBet2O4iTkvv0KG42E7oRrPXG4yxnKwDZ5vMkAwDQrDchcii0QRgHCNdkdIyovn904FPLBS/+GpHKz5RdK3mwXHDwTwLSTBkHg+Jy6LwKAakX9mUQqolUSyFdcr4VOP1qoiuxMxBKky+MNmaVVmZFI3TxJzlxyiy4RRLRRIzMSLQvZZecwyJW3PH4Z7jYvCsBwmSLbXsKh2UCGSYUuHBzTlNS73VNPfuHXF6tuFseclmGJl3hnGMGK24HDOXqTDTmp/6zAEv3p1RYuDxexuSgvVU+OOHA49XkVIy+62m03KbpAyHM0FZT1cT751lFo1mipmTi4BPhf1WxHTrMf/ZlJL/uh6F+Pu2RjMGmc7LCxfKrTZK6PhFkVKktaZ+UzROsxoz4xTJiB9y31ickTc0J4GUTSosOs12Siw6w2pI3Jg5qaHHwJSVMJdV5tIn8R/OhdYJFK1KIcQkVJkInTHcPmg5W01Q4GzrBc5WiqQUbj6DvZ631p6Hp1uSNhjX4hrD6dMT5l0j/9ecOOwblDPspiDmczkxm3X8w6eR9bTjS/caXr3haMgopzjqLV9ciHT4Dx6N/N5F5FGUWt4oYeGuM/gix8qUxFldK2EwjCmDERTwpcbwJBW2CowWD+TJJ7pW4yr1P+eMMqYeYnL7RDIno8es4MGdnhtGY3rL09MtPmZ6a/BZRHnrXeRw0RK9DCeLLgxTopTC8c2ONx+e8OTZllwmIhmVFdYU1kPi5txyvkkcLyy6sZwuE9sx8H+/vyMpw9YX3llFYtSVqZE4crDvCgcLCwWmUFhPiSlkFkYzhcyQCjNnCKlwsgn4+KH4lu/zPBebRSu5BZpKbtvmTK4WnK7KR0XcVeit4eZei1MS9aaU0OVlgYqZXMyZKWYapZl8JNfmYq8V0wcXC1OGISnymNhMmWdrT0JyUXR1xZSGVMoIU5nL+5297mVyzoxj5ubc0XeWEUM0DlzHZkqcjoHlWyt2w0QpMJ8bSoqMw0RKmd040mjL5TAyM7BonSgufaLNme0UGY3lH51EHm4zXz5O/OTLHb0RBmXj4Pae4e7M0F5GHljFC1YA5+/GwutTwsfMvDGkDFkprBL6T6zohTGKeau5PJ3QM6GvKy3sBuc0PgjrwVl9PVy1RrMdpI9b+8DqJDBzmlePesI6UBK05ipiXbGrG69vNdsE5xuP1YqShBm8fRYZx0ummOka2AVBGu8ctCxaxYYCHWwpvLn0nPtLHp0PPNpERj8BhbaB3mkabbnbKz597OhtYrn1XO4SvoI8yiiaRnHsHJdT4mwbuN1qWsCn6zTX7/s8F5sFFIvWoihyEhUIFb1ySLkUk4ifVGXaHnQWZzRTKgxR6u2uCrRGn8i5sBoTfWPJwFgy51PmVoIpZHa+sJkS80Ya/sYaYinMWuldmtYyawzDFGi1Yt4a9lqh9PuY2ZsLPQejMbOWyfYk23FyseP00bJuCHkdoqEp+EnTNAaSAA+j95hG/v9PNyOvGM2NzjL4wN1OcbNxLJVlGOG9IWFPPUed4gsvtmhksLqNhW+uEodG8VpnOTSKR1PmaS44Mo2CfSthrUZJ76eBu53h88c9L+w3LCNsY2avIlg5yZDYeyk7ndW4OrtKSQ6veWuqA2chI4zody8HWqVJIP4ClVQ67zQHC80YAk8vA7rAdoxoJdC132RJR9Dgk9xsVmt2Q2I0ipilErgxb3lnObIJGlLg7p6ADBrFqArLSXH3oGeRE2ebCaMiU8pEK/w+Zw1P1pF5UzhoIJfM0dyQs+J4ZrnTabrXn3tuGISa954qAhZjwmrx8518FhYwstBVgZAMVgm8etA3tM7gY2IzRnyIzFsr0LGGi60nVXp7iBmtZJrvc4YgfY/TikaLiGwMSbQqVmGx9FbhFFAyTssNYYwm6Iap2+MiFpYXAxeXl2zWO1IMOKXQqtQeSfLngw/Yyh62xpCSZT1O7HcNmyHgS+bBUc+bp5fszQytLixXIzEmnNYsA5xuI34Urtp6yHz9ycQUCz86t9xtxHPgPMJMJW4ZxZ4x4rWmVIXbFYdO8bP3Z7x20IBWfO3hBFrTOYs2GasLPn3AEBAVq0gmUsykIlp1U0GX1op6k6wYcmIIIp6bOekVnFVyE6E4nDkJaRIsQZKUW+lVjYb9ztEFmW+FlNn6StbK8O13VqiSGFeKIyeMhQJgFCHA5SZwoBAGQwgCCJXCqOHxJvH4MpGKRq0j9+eKXomj0I29lp/87BH3Xtzjf/zDP/rIdfpcbJaYC1tfUCoJn8saOiMOLTsfReWYSg37kVJtioWsBVK2qtAgb4pqDRcpM0VYp4itTi9kae6NrkSVnGmM6ERaa1i0hinKolYKFp1j0Rrme5phijit6Bs5LZ3ThNmc0ezxztMVy/MtJSXGMRCDJ+dE4yQ7xRip3XdToijwPtJ2Bmc0SSs8MPpAa0Uj/s6TS3Y+MgY5ydc+YUupOn/FFCCMhWGIPF1Fsi98Yd9w2EiZdDkl5gYOlOJVp3krZU5DxmnFzdbQaMNnDiwf23ecDZGub3ivzoAO9D6H84bsN7gkzitaVYZzFkl044S0qhENf2Mr06D2QxkIWU7tZBV9L2UxKAafmTuLRXPnsGM7Bs43E2MW55i+sQxjkoEngFJMQTaROLIkXMkEpdmmwkGnWflCyonWag51wRbPxa7QI8rPXS6sS6ZtFS/dalhPimnMrIPicGb4/CcXfOGL9zk66klhwjzv4i+jFYe9Y4qFKWb5ovlY5cGKySeg0For17UwKnEW+lYIlZsp0VrD4aJhPSXxzEb6GmsMIUQ6K45RSsG8tVij2A4BW0/JxoiRxby1hCSnlk8FoxU+yOwnKQNun9NNYrk6Z7vzjMNU9TditBDLFT1TWNSLzhGibPRUZNHNLLisKdmQUqJzhpPNROsEOl6FTChXojgpdxTy9QlB6Dh9o3mgRcOjlQwpY9Ecx8zYKo6i5uEgB0pnFLd6x55TfOxGy+Od8LkUkVWQfvByB/PDI46O7zJsloTdJXmcyEXmUwWp+1OWBt8ZkTikVAR1ozAGkYCrqlHyFaUUSbTCk5ly5uTSM8XEZkr4XGiMAAc+FULdXDHL59UUfMn0GnprOJ8ih61FmcKkZPZ2NNOMuTCVwnmGzSbSGpi1CqyiN3D/wOLbnq7p6a1iHgY+89IMFRKrk6XQauKfIZ/lX8cjtInC1svVuWcaYpETSivFGAL7fcPRrMEnUbrllCsDV7HzIhV2RtzkhXecry1TrVbM5y0HswZTm9cpZpEua1i0hoPesvOJgsLWKf5yK6pAizAM/JC5cWfByelONrDR+CkIUgTXScEpZ1IWN8eU5FQ+aGQjUgq9yWwnzypEQjWjjknKzuXO0ztLQTIfrapDUiVNc8qF7ZQwShbCrLVYq/ABGlNEV64inYGQCl9PWTZMKWxj5oVFyzoUnm4jrx42fOMyEItstnE78c47j3n2rOfm8T73X7jNdHnK5uyUHAMhFQFVEK4eRYLCUhFmhNG6Hi4iuTa6kjSDMKMp4lRvteJ8M3EVHuV04dZBQyqZEsAkhTWG6BMpFNpKfVKI/dGh06ynjFNwu9PskmG5y6wCLH1hGTLLraCZNzu4uWdoG0tJhcdPdhwcKnQqfPGVBetgWD/ZcfdBx+zGgo+2yHteNguwHmVxSRMpDZ4z6pq/VUrh5tzJbaMyRRk5tSZZZEZrpphYTZnWKRplGYKIxXonw8lSMncPe85WnsFLBF1jdRU9CcfMR+F/OSPN8HqIdE4z6xqmqHh8NsiArrVkakxB1fmXnMhFNk5IctMlCp2G2630LueD5zxYttESKWhd8CGQEQcTn4X9O2hDzFEYx0CrYKYVc6exVtN3iq6rjXcobLeJkBUuFFprmDUanOFBX/iTt9b4VHi0mdhzhimJqw1K82grB4TRBqUNMSa2mx3jMHJ+vuLevSNuv3rIxeP32Z6vSTmLeK4eTsJDS3SNxaeMTwhUH5DhrpW5UdYFMoQIRQs40TUGW8V8Q4iiu9eiBh1jIqbMvEH8DlAUUxjGyMv7DU93ibMpc7fVnG0TyynzdJdonKZrNK0raDKHe5a9XgCO3QiHreNwBu1ixlOv2Z7CJ+50RCzf/taS729dLs9zsVmcUXziwR5aKVLI9J1jFwuhwHefZFbDSAGOFg3aKt672LILiZlrKLEOK5XCWI2PCWMMRuXKfDX0TksTX3XYIcqtJOWFZvSR7VjomqvbRcKAjNE4Zzg47PExE3aebOT1Ns6w2sUryaIMQbUiJjHeAGmMjcrYElnuEttY2O8bOq3RXpFywscauKqlvFy0YreprYGY0BTmRtMBMyOojWsNB0cW1ytKVEznvnoKC6XGaIO1hfYLr/L0f32TWMmSMSleX+642Ts+ezzj4S5xNmZiimh9ZTOlqvIyMWxHHj48YxgOOD66x835PhdPnpJ8IGWYNwqs8OBiPeBKiGwn6Xdam9ibOxlMTkksmbSAAbPGkEqd30SFqjarIURmjcWUwlGreOWGZY7iYpNAKW7OHOtJSuKnO3j2LDIVGUqPqTDlREQiBhslFcF6EGQ1TAmtYQgR1XnaWc/dQ82zVeTZMvP0yVAVuN//eS42S2M1R3PHNCVm8w5jhRD5zXcua+8gBnsnqxFfCme7iawVwzRRovQv1hpsMfiUUF5ggMO5YdEYcil0rbmelcw7Q9jJ6WarRl8bGIPwtlpn6JxhGxJoy8Zn5tPEXMOqQNvKcM77gNGirSmIBVHJkZizNKQ50mjNclQcdo4fudOQQ+B0zMSQcdZQxlItn2DRWXY+srAKq6UniR5mGvaN5oWF4d7MkLNEeKsAufLWrky6tVLYRrF47QHqKz/G49/4OgVBw3IubGIh7CLzS4+u6KMqAv+q2qRrxfWGCZNnubxkHBsODubce/UVNk+fMGy2dFbJTKRcvY+KRdLsNYXdlQ9clh6uFGFVpCxMiJBrXIcS5562kQMtIuzuQ6c5ahXZZ1ZFcR4yLkoq2hgzE4WlzwxeXrPPgszkXPABxilImBOKrc+0FiGBAmmT2e8L4SIwLDOnncL1LW3TygH4Ec9zsVlKKTy5nNjtInduKEwLj053tM6glOjWFYpHlyPUhi5GIUMaqyhRaPddZ9kMHlUyzlrmlSGcE+zGyK3DlhjlVMqVHTBrDHcOGpariWlKOCOQ5xATl2OkyYrkBz557HhzK2+WNoriPQcmcjl6diHWZj7RO0sqmc3kscpgjUUZhzKG86kw14ZMxMeAdc11vEspcLOBTywcF0PgoIX3x4RTmoXR3O0Unz1y7M0qLcVX/X0G6zS9VlBkVqHv3eTm3/qbvP3+E7Y+E69QRPVBmMOjjRfgo/YYoRT5QtWkApDJe06JGBIjnpIzm7Xh/u07JHPCbrVmSoWYJcTIpMSDg4Zf//lXefHTd3j0aMN/+z//Ce9d+Cq002QlN3NJomZNuRoWKuHM+VAkoVlrlrvMaKE3ml1UbHxkZqQXOpsy66nSdyRzhJTzB4K+oljlREiKziqGKmwqRSQg6+CZUmGzKbSNouszd45UlXh8/+eHiZzogP8DxHkG+M1Syn/95xnAOvjE0+VIazTn64myDVzuPCkrDDJ1vtJeKC1al6ni77PGobViv3OgBfJ1deKcBTkg5kzwib1gmSahxQhKZUk5c7nx1xPthOJijAwhEYpCeY8uhTFbdlkIkfsp0JeR13cTYxTWANXQzxrD3DWMQW6YWKTM2qbCuE0snKIx0kPlJI72grpl7nSKn36h4Xe+OfLeWcAUxaJzHNrCV44d9/Y05WqzIt4ESmm0VbQWlFVwY5+Dv/Y3aR98jPTeQ1nEWl9b3CpgyrlO0BNOKYYMIUcWTjMzmpDrLEVJn1eSuDy2rWU3TLz7qHCwf4P2oGH15ARbjTxeuTXn13/58/zKv/MXcM4w+cLdl1/it3/vG/z2//ZtUi7VWDCJQXeUW8dYI+VoEkZzzmKUOCrFZpe4tTCsQ2I5JIKTA3AMAmA0RpOy9J62KGZNI8YiPuKTGICHIgho78R2ZmY1U0j4lInOcLJKzEMkqokfUIX9UDfLBPxcKWVTQ43+T6XUPwT+Kn9OAay5SJM4bwwKRYhCd0lI+XBj1glSUnsDWxROaXQWVGPhLHudAa1pnaG1ms0otfO8tcxby6gi622suhSR8m6nSEExmzdsLycSis0YpZYGGqNpVGHwkWfbSMoNeyXS+x2vL3coZ+msJRdNqo29VkpMLqxhjNI8l1IJjVnUnIdGM2st61EMMAalGGPim+cTt/cMtxYNvdGstoHPffIen/AbPnucaKp/GYB1QlURGYNCGU25sWD+V/4G7cc/j0qBk/OxypDl1KV2JTFLmdQozc1ZQ8oKnYEUySR61xCKGGZL1qLEN4yjZ77oWF3s8N5zdLTH4a1bnD5+xsxp/su/97P8zI9/DK0TJQcaV/jxzx3ziZd+hrffP+efvX7CzFn2e8MUE0OIiJ2ukFNjlqyYeSv2rAkIKC6nTEjQNw6fMiEmplRIJaO1pnGKWaNrX5YZg4wOcqESW2XW41Nm0YozkAyGFV5BtoZLXzh9vCX8AHLYDzDYl6fIs6m/dHwQb/ErSPAq9cdfrT+/DmAtpbwFXAWw/sAnJmnwrL0yyMtCFynyJuci+HtKQm5sjGbPWe7MGu7vNxzMLLspME6yJxedY95ZQlU2Omvqla/oG8t6iGymxPku8P5yYu0zp+uJbYisQ2LjIzcaxQtzK1SOVaCURDvteHO54/2NZ98p7swsnZFhnFIKp7kWpcEHIa9KwCd8Kux8YdGKnamrsRpaa95eTfzW6yuWWfFsTNyZW37+F7/ET//ln6LtHE1vcK3BdQbbWczM4Obiu6Vv77H4lV+j/9RXQMkNdHJ6QUaADLgaygqRUgELq7nVO5S2LLqGT96c0znHxeiJccAp0egvLJiZN9XKAAAgAElEQVQSCVPNumwd0+A5O12RTctsb8FPfvYeP/75mxDPKHFFSROqRFRZsdDP+Le/8oCmzrNSri4+tYeJFfm6YnI3Wjb0ZpKSydc/L8NJKdecVvTOyozJaIaQ2E6RV1445j/41Z/h1Qc3q35JXw84tRYS7TYWLqfMyZB4uA7sQmIXEkEMrD9yjf6wMXkG+GfAa8B/U0r5J0qpP1MA6/d+fmnyx5DIm4lQ2aBT8LTOcr4N4vNVRXQqC6Hxzl5LXzfX2WoSlnJ1b8kl0zmZlE9RNoJCjOliykwpk7XMLU7WU/XIlVLviobxqQPHS73i2dZzERJ7fiKoxHsbQZ9aBa8eWv5kGdlEgY+tkcVvatzf1YDgygExVzrIvDF0Vug8WmuclVt1SpmQRfm4dzjncz/1Ffb29/nOt7/FXbvBpgzVDFzKMWA2p/+lv077ya+glIEMJQ68+92H38MLbE0d9GlRUt5uNb0z5FHKsxsO2oVjEwq6BPaYSDgihkXt43brgfl+TwyRaZhYXiiOupZ/6ysfw4U1OWzR7YxCI7dqjFx8/T32zi4B6pwoyiapZeGic0wxyibP4i9mtaKrjOCrkrvwgXVtqd8JKEOVdRR+4kdf4z/5O79K08z4xtv/QMDKymAffax7QdWZkdxoGaFc5fy94X7/8vNDbZZaQn1BKXUI/JZS6nM/aO1/v0/xp/7Qh9KK26bjaN7U00WgV6Xgzl5LYw1WizakcTJLaTS8tN/wsTs9RSlWQ+ZsK3ywmIrQU6zGGdhNER/lVIqpfoF9IhSxdg3VrE8pqk+ywSoRWi2cRBnceQpDBB0jj2NkzNAaARle3LO8tU5VYiCMZYVsFF19z+rlKGBDnVGMXjZMCBLaBJXxjObrp54ZhYOXXuDopY/huo69n/kFnv3ub3H/tsDCyoh0Qe0f0v3CX6V99cdAWfny50D2E7nGZbs6szJKpogmV6a30jwdpTSTW05xf655c1XQSXHgNLsQyCYTi0U7x8oHgne0vSP6gB8mNknxT//B17jz7Z6P/6UXuPmKE0sXDNvlxFf/l7fZnEwYpGQcY2KKiZBFH9Mmg1GinxHiqdzUuYYQXcUk5lwYgix4q3RF7MTCSSkpufp+RkgZHyRuZAgZE7McgpXLlIvopBTia5CiHCBXG/Gjnv/PMux7VnwpF0je/S9RA1jrwv9XDmAtpfx3pZQvl1K+3DYN89ZwY9FKpALiurLoLAe9pTWaeWO5u9fy6uGMlw4krrqkwjiJQGjeintjzplFawghYetpcdXYplJhTCVlWaxip5SlL5liQmlFYyU9bBcLZ0Pmy/dnNGRSEcuggtBNtkFsYBut0Og6yJTTXqMwqrqvFCF/NlrIjErBFOssQAttJOYPEgNKFsOMT3zm49i2A215+ed/Hv/xz/HoWSIWjek1+v5dZr/86zQf+zJoKwO1lCk+Mu5G3nrvVHrAkoWmUqQvALnpxgLLIBP5nBW+wI3eMHeWVYLTMfFyp/jcvmFPJ5oSaXXB70asAmMN1B7hcsi8/s4GZjNKnshxkOn9tONJMbhSmGvq/03mLU5Bb+1132Wu5AMZclZsaxmWa7+x8fX2AUplTYhuU9dqApbna37z7/8jHj87ExFblVyYagEc6zwHKh0pS8xixYL+bJtFKXWr3igopXrgF4Bv8UEAK/zpANa/pZRqlVKv8kMEsOZc2IwRpRXzWUPfOZwz7M0dndPcu9FirWY5BA5njnuHHWMs0ozlwjQlLjZBvL9SIQTx3Uqx8v0L7FeqS8qFWeuIuVxf0T6L+wdKFo5WgsJNGV4/DxzNHQunUDlzoxGpc8yFlU9s66DL1kUvsxz5HLoa4l2xnXMRhrPRoidpraYzms46jNLXzGuKlHMvv/oyqkhCse1nfPbv/Pvsjl/hfK3g459h9pf+Lu7+p1G1P1IFSkjkGHn3X/why4uVRHnUZjfkq76qUn4wJCVlY0KxjYpWK/ZaK3EeSnO/N3Q505XEfRc5MAmdsnyNqw2qKoXvRvjqJjMmXTduJoUBO5+zUpohFRbOcNQZjnvLp2/v82MvH/GJu/vMGnGAMUoGuz4mdlOs/tUyC5piZopSohmtxb6qSP86RJF2+5hRSg7Ks4ttFYLJYSjvtaBirorYQFgIVqvrW+oHPT9MGXYP+O9r36KB3yil/I5S6qv8OQWwxpyZQoTcoArMWouzCmcNjiLy1XoDnG48m0nzyu2evf2Gk6XnYgxsQ6aTJcMQhR0WaqzDzBmMEUXldhdYLbegNLEUnLWoSvy7yqZQ9fYIuXC6i7wyNWituN1aDjrNPz+T17wOmXVINFrR2WpMrsQI0BqNqTLeDIwxwwQzJ9T9kCSeorcaq8W0wijR7hglyM/xzRs1cFW+tXuHfObX/gb+7BmzL30R3V9xmSo1OIrq8uydb9L2huVmukb2plSukwd0PRA8FtQHXtCnHs4mCfpRSrNLhdMp8/ZGpuQvaHjQaYZtwEdLu9cQRk0Mnife8sKrL8DsJbx2sji1wevE3Zfu4VTPp27AlMUSVys4mDkufcSd7RiDWPbGlPFR3mtVOXMpZeaIHCDEWG9sUa22dcMUFJ2Fz3z6Nb70I5/mn/7J/8C8bWkbK+UvcqPkIrdRqe/xlbdDQQbbWv8Z9CyllH8BfPH7/P4Zf04BrFopOiO0lK2P7IbIp17eZ4xweTkRgwybUioMXtFax6K3FKW4HAQmPOgtqzGRivQgC6eY1aRetOJ851G50GiF18IVUkpT0AK9aoVxkgeZBw8lMyY4GRLvXEa2IXNvprnbGzqjucyJMWXWodb8NfQT5ET54MSS0/yq6Z87TTKK8yBmDnMn5UcS3ApViZzzvq2m5BPKFsCglWb/pY/Bq6+Ra7FXqMP7nKEEVo/fopt3fOvrG56cb+XjyHe5VM7alRpUXfVq8rp3CR4PwrJurWYXA++PouH/5MIyhsyre5aljzyNE2WS27MxmsZoPvdv/EXO+x/jYlQSZWEseU/xs3/7pznq4Jcq8kehmpoLC/uqhwA5/SuDiJQSMYSKisrHc841SUHCmq79qkv1VLh7k77r+K/+i/+U7egr0FKuDRVLkWjFIlfNtbFjqXrav/sf/72PXKfPxQRfShO4WE9sQ8IozfuPNqyCpHk1WmYVrZHUXYPiybOBqDTvLz2aIrHXSrPXWBqjKFEGgLkUViGy2k5YoymxcGPm2MXMeozkkjHWCqxqBJUar96MXNiEyOkQmQp883Li1sxyo7VcemlQz3YJ22gJWDIflF2d1cSorxG4zmgOWs2eVYwZTkpmiokjZ7FaEbPCGSkhOm04PJyjtCX5CaMyaIfSMn9QtZyQshGE6pwYz56hNSxu3ePRk3+ELzKL7+ppGeSviF+00uSqnIRqklEKz8bMUSsx4uc+sEyKl3vLyzPDox2stxM/9+o+/9M3twybgVgKB62BnPnuG2/xE1/4nEzoa0nbdg17e46Dg/aa3iKPEhDgw1Ct+vBP1fVGv1rYpYiPgLoS2Sgtn/PqU1xBZCXxmcV9MBalK2fhwxj+h58P/hHIhfms+8h1+lxsFqUE1nRGcXwwIxZ452zHs43nzqJj3jVs/Vb8ixMcliLhQiVzb7/BmSujAjBWoZ3mZDux18oGsKWwmDWUUiSR1lrG6Fl0jjHKbMc5i7WaHOK1jxZKrE2XY6Q3mqdD4fcebsW8XClygfMh8tq8hYX4ZW1ClpSwoiB9oJOJKVFCxFlx1AfFxifuzS2dlVN/f2bZbTMzo7h35xhrG+IYsLZQkgfTXy8UbZTQU7T8GFZLwrhifucF0rTjj//oDRJya1/Fa/iUaY2oRJMytFfrqC7MAlx4KVkaa+TmjgXV1VwYBSfbxI+YzIv7jm+dyWYZcGhjeP/hY0gjzna0jcM5cFpYCphce6v6hmuEqwNIoVoX8dWf4SrivX6kFFS+in6Xj5eS6teDDzZBKde31tWf++Cbuq5ar/7s9wC1uVxz7L7f81xsFtFCyP8gJ/ki7bcimLq117L1MhfZxsDCiXHdYWc5njt2XozmppCqVkWz2nhaa9ilGkaaJL8kIbBlmBIlK7aTJylN07cYa9AGdmuPvS4VZC2eDoHbjeJLxx1/eDZSpkhrRGy1HCOqZNqS2A2JXSrXsoJtErTMafkWFfjtxItHPYtGc76dGPYcndWELOBEo2DuDA/u3paQpFBzNkmUtEPpnpIUOWaUAVUMZZqY1ufMbt1FO8f26ZJ3n62qoTrEQvVkkx5AGaGM9GJ8wpTFNqmgCMBlEHJiay27GPBFV9JlYsqFP3x7w5N1YgyJxlqG6irz7OSU1fkz7ME+Ts1BCzsgaksppi7+umjLFUut1kLXJ3764Pe/56opoAVMkF9ffbTOsq43g0LVUhz1oVa5FNmcpQgR7cP/xtWm0jz/m8VoRTcTrcpykJNIFcXtecNmJ0ZwVzfHpDPrKWIbzWaKxCTOgw9uzSVye0rkWiKt1562lWYzlcL5zlOArnGkAs5ZCWJ1FusMpCRKOS2nUsgyo7icPDeM4eOLhvd3De/vPK3WeJXYxMRyO/EX7sx4fx2ZZei19DFnQ42/IPFrf/E17h45nj7bolMhfv2MZ5uRXUgynESxcJp1UHRtw53bkrxVspaFpevbm0dK0VKWSR4H0+U5zWyB6WeUAudPnvLw2ao299LMUwR80HVYqgqQMl0jn3iq7AauanqlcNbio2eqDUOrFQur+eNl4P0p0TnJ+0yAKpnVaseb336deP8Wi9mcrpcbxrea/dkhbd8hfVaqJ7yrG+jD5dTVIi51KV/9+kON9xXO+6Hq6uonfwr+LdWYN0fIEhmCEhJKKYlSRJ4gnywjKrXv/zwXm6WUwm5MtJ0lKdGKKJXYXzgmn9lOoj8hSb1tK5W7bSzLS09GsdzE6p2rcM6SSsIW2HmRrW7GcA3pTpXaULRhPu/oOkvTWYalR1XURFeCXu80p0NmTIaDRvPy3OKL4nTwjElO/eWYGNcTn+4Vm1A4By5DIjrptfZaw+uvP+MNlbBNg06JVsGtmaPThb7TPNpI0m6roZ/33Lx1LHwupSjFyomqCxCkVHfy1sXtBqLH3bwFWqNi5vL8kiGK8C3UuUYsGdQVu0BjrAAk2zERQRKca5BTtVqv8xDNZSxsgsxp3p8yT2ruomTXyI9gyTnznW9/l7C+YDHrmc9aur5l1hmafMiDV44+QPdKNco1jfRiRVBI2a8FoSEoGbSWKzr/h2+EuoGyTJqLUijM9S0st4qgLnJv1moB6q0mf09VU0GKsB5KHD9ynT4XmwWgpEwaPXNjQEO/aBhCZD1GNqMQ5qwRrs+sdayHTGhl02itON2MHC5a1kPkfCd+tUbBlGCozoS5CNtWMF5H2zU0rcNY6S38GCqsKJPknU901iDjHLF/vdFI8m+uMKxTUuLsBs+NecPdPcPgC5deceYLu1TojSIME+sEU0mQM9oZLgbPy7OWG41m6gxjyOLbfHTAbH8PSsHaWhkkRVFaKPSmAWUoIZI2S+x8Du7qdCy8893HxPQBsfOKe1b5BDgtyb1HnebJNqKVYczigXxF2Sn1Rmqt5WQK/OF54MwHzqLAvyC39Z1WM2YlLv4avvGdJ7zx1hOM0zilaFvNfme5t+f40o/cZNEpmraBqym6dRhrMOS6mJWwE0i01lGy/LxpWkIUOyPrFL5ociqUEFDagBGl6bzviDGym2TRxynipwmF5pp+pgqaRFHCfNb6Kj4+sds898bg8ibuNQafChsfWW4955sJow1j1cvrejOMIbH1iaQU1okYyjmBNlORc2nn0/VNE7KYJGiE/t31Ld18Rttb+t7R9gYVM6kOvEqBmBJb4Paiw2pNKoVvriL/z8nIkymxHANaa1qTcWRmynCxS7gpsQmFqGSGYqwCJdSZF1vDyme2Y+ThENj6AKVlYeGo1TycIjOrOT6+Qds0KApaZ1AZioGswTqq6yBpcwkUTD9DGStUjpR4750nIi9QGnVlF0R9PUoyOmOp0R2lUFJAo9HGoOqUOydx3tTasCPxtbWvh40hVdn1vQZ+8cUZv/PulpfmDZcFLgfPbhiYSmEaA8d7luO55T1j+OobTxhzQlmFUpnNhZzqbas4nDsSGV0Ho+3Ccef2IefLgcFHZro6kc5abt7c59HTM3TMzGYz+sWCkiKvvvSAf/Nnf5Y33n6L3/7d/4v1dstyuWW19cRSrq12jRLDEokTR2IRKYwh8vbTzfdfojwvm6WI99TFLnG6k5lG0bIpFp2pkl0JYJ1SxmcJLepVBQfqhrgcJD7CGE3MkaE6deR6pmpjcI2j6VqaTuS5tpHSY9hNqCwDUI0Wv6kQZQZkLcsQ+d33dpz6WMk4VC6bZe4M807Tt/AjH59jFRzcaCipsLxMrLZZGnIKD1eZtwp851woI71WzExhssIQ6GcNh0c3aj6M+qDyoM5sSgWLQyCOW9x8D9W01Eg0So6MPjCmfK1gBLkZhySw/I3OsBsjN4zluDMsgww0QwhEpbDOojtHiokUC4111ydvruTDV2aGz+47Hm0zPhZeW1i+toaxDlsnL3Mo5wQcyEViQaKCKSbaDrxRJJ+YG8uTy4nFngSoqpTRMbEaL1EhMZXCmU8kEm4MPFwO5JxgTOSnA9kuabXi+MYx1rY8fHLKN954LH4KuTCmVD2c5WundWG9mzAIc1kiGoVm9ANIx/9q3LD/vx6thWZ9MkSmLPXzFaq3HYM4oCDzmJQrRytnJp84uxzY7Dw+JJa7iSEWlluPT4lUZAqfAecaullPO+vo5g1db+h7S9+3OGvZbTzWSGJV3xhmraMApiRu9wK3Kg0vLBrgg0HjGOWWaUrB5cLcFO7ecSxmilmrmHcKnTObXWIYMyVn3rj0nI6Jm63huFXcXRgshQaYz4TynnPGNg5lNCiZLVw3u7mQdmuU1pjZgmtzr5xJIXBytpJeR1Fv2lLJmvKaW2vp+pZorLjoRJmSQw1hmjy77Q4ozPoW5yxd09K6BmsM+07zoDcMSvPNS8/Lc+nnqNy2RgsPLilISjEl4V/5KsMwKGZa88s/9wVuHu6JQYlRhJAJCrJTTAXu3ztiEyWNrNHiz7DeeaYSmRvForPQW5qFJamMco6iNcrBrDPMe0fbiie21Vc/appr6L8wXVkfXfUz/zJA8KHnubhZMnA2ZrKW4Z4q+RoPN0YRI7TW4qMIs8aQaU3GKJneX46BVBQ+wWY74pPAj1rJkNFYh3bm+tZxThwlr05uqwxhDOSYSFYAhq4xbEeDzol/95UZf3yi+e7lxJQKnzqY8e2LgVB9wpYRHg2Je3uNeCl3mhSlzNlsMk8upe+ypvBsm3i4CyQKL/SOI6dotVBcZhrZ0H1DRtwrxc4pS8N6dXtMibTbYPf2wVb8t6I+0U+sNiOpjrVNhYeuaOhX+pm+0VhnSNnQKYvykSlIMldBep3tbmCrRm7sL9CdsIyn1HC5WvH+kHjQNmxC5ssHTW2wpR/6j/7aT3Lrhft8/fU3IazFPMRapurgsp0CT05XEAZ+9LVD3n9ceLoa0UW4gd5H/BD5o2+9R0hJiJ0+YXShNZYwRd7fiTXv/qJju5mYLVqOjg+5ePaIh48ek40ww1NWFKuxWjGERMqJEur8ywrBMsaCMaCL+kF75fnYLFPMbH2u1qqK44NO6kut2E2iqMulEEuurNSET4YwBKYkakSUEkfJWiLpK8y9sk3brsE4TdsYnNM0jUUbhQ+RYYykkOhay+ilN+obTeMsl5Pn2SYSfOYTc8t5gl1UvHbY8c3zHaAIpbBJ0pvkDMkLDBsinK0jp0Nm8MKCfjYJsuQUPJhZWiNvVEwZaxTd/gFd00NOEgakPqyLyagYCZsVaNB9L2BFFukvWuM3G7bb8UPajHI9ishFkeu/1xhFr2Es0vw3rQwWM54QkwxRkVv/bLmm7xwHja1UEfj08YxvXURe7AyHjSGWq9da+NGf/lE+//lP8bnXjvnGP/kqIYwYq7G2RRk430ZIkbfffo8f+9Rtbu8f89VvnDD6wP09xztPAwXNOHmUEbrNboqS3DyzEgmCkC9vLBqsU/zav/dXaDG88fYbfPHLn2dSHV/7468zb4MkLTSazZiENpWl5FKUGhsv5bwz+gcyj5+LMgxgmCIi0SjEGEk5sxnEDCJWivkVnNlZR2ctR4sOpTVjSoxR+ENXdGyl1TUtu+0b2s7Rtpa2s7SdwViDtY7ZvCNMUUz7jOFg1hKTkDDnTnM6ZtYJLpMCY7nXKAxSuy8agwZCUZz4JMNTL+6LKRXWq8j5LjFmWMfChS88HTNjLuxbwyt7VvqtVBi8gAtuvi/7oohN7BV3CYASKH5HCQN2NkdZV39fSjOlFNNqhS6pqiI1VmusuvJyFo19b4S71WpwquBU+SBOQ4lEobX22iTPKNDV52vygTudbI5cCp/es5IfjwAjPmZOnz7DjxtOn51yuR7YDjKwnXxitZlI0bPoLWfbxLunI0WJUlOheP/JmpbCK7dm9K4K2bKU6Vpr1kPk2cXI+WrCGcOidygN//sffI3f/4Ov0uzv89onPsmv/fVf5D//z/42pm/QTlC5q8Rr+/9S9x6/lm35fd9nhZ1OuKnyS/26m/3EVjOIpGgFE1AADNiSYE8sj/w/GPDA4T/w0CPDEDwxIMCABwYM2AYMmIYCDYsUo9jxvdf9UuW68aQdVvh58Fvn3HrNfi0K4qC8gULVPTfUPXuvtX7pG8ph4UpmMWvUkzPl9OZHlpwFyZk+6gwl58jFblKqpyhMwlC8M4q+1s04cTOpGc2ebKUS8fvc/Da67KEsbVfhKzXf8ZXFWe3hb1c7nVCnjDhP01QMY1AXZGt51QceLSt+dDHyq6cVv3Li+PB6ZKos61JFrwtV9fomcnrHg4Pnl5Fn68TLbeQmaNR7NmjNdbepOGos12PiYlOio3N08xnGZpwkfAFnigjkCGFEwoTxFbabl0GeFIddPUxWN2vWRR5pbxZbO90wIWtEyzGybC2dhyEbQkysd5MSvUqB66ylraqDBfoUIjebiRAiHxzXfLqOfGPheTBzpEIDSIUCcHW1pWkrHr37Lt/74+8zDH1R0U/0EbbB0HQ1d5ctf/SjV5ycNJgoXK5HXIbWCE831wdKgWAw1uO9puW1t4hRf8x6NZAw/N4ffZ9fee8Of/QnH3Fzc8Pf/K2/xcnpGTFnZp0lpkTTqr2fUv8VFhMlE4NGlsab18Evf+Z6IzZLyqpcn1HIR5LILqhcqCkgwP1DDFm4CQHvzCFF2cuA7sF7QGmbWpxzukGsoapLYZcV0QoWJ5btZkJSIhgDKOPyrPOMIeF9xZPtxH/woOW7F8L3bgK/cez59dOah63j+6vIyymzScL1lFn1mTAK2cJPLgM/ulZ08Zhhk4Q+C94YHnWWo87zZBUxOSmdoKqpZ12B6kd8VbphOSNxgnGH5IxtX4sqKSIpgK8wCNNuZD1q2mrLIC+Vqdt+7jJMkXuuwjnhkz7w/KZniukw6gMKKS7hraOtKxCFt+ecGKnAwAdHHmvUq9Hm4kpgLOfnNzhrWJ4d8e4HH/D59/8UpolVNuySogNe3QTOVwPWwLPzHZXdO7OZYnuotIa2UoPdLJl+TJhRmJzjuNZ07N6dGcEKT883XF5vud5+Row9p8d/yvLsjG7eMayvVYO5wH6mQtrbj9zULUCYohz0Cn7W9UakYftJ85gy2xA09SpphSuzlX0ueTu/3efyaBfm8Fn9fBbdaFXtaRrPfN4w6zrquqaua7puzmK+YHWzZdsrf38MUZXhc+Trc1jYTOMNo1imMfHewvNyiPzBtcqtfrBwnKoXBbskXAfhpo+ESbi6jjzbZV5Owi5pS3VXpHk6Z3ln7nBGGKIwRpVCslVNXTeFNJYVqZUnCH3ZKAlTN7jZskRQYD98dFro99stYyqljKjIh9p3K0N0LDoD92phHCOPr3f0IRIkE6V8nWikTykTYmScAjmr0Le1lqe7xNdmnhrB15abKbOL+kxCzFxe71TPrKn4pb/6y9x77x3Wmx2SIjkJpMid45o+Ck+ve8KUVHs6q6rkhHb/mtrRth5Q23FEBcq3QyAKdK2nqjzrbVR7RQz3zjpyHPmj3/89vvfH/5Jf+YVj5osZvq6QrMIo1ioIVrFtBmP1YNlH4q+63ojIYkypNVIuUBXDUDpf+zmDRQdHr4Pu9lhVs/8hlIm1tRhbZipNBcaohu+2p2oqmm5G23SIRM5fXmv3zKjz1i5GxuQJoir7fT+wqB3DkLnfWD5ylpfDRE/DwhsCQRX7k/B8zKwLDn43qUr9rqjLJxH6pFJI92vL+0cV2RSVfpe1vd01ap+9x0bFEQlZTVGzYKoWuqUOJgFyJsdJYS5WcWL9eqcFLAroULIXLKqKTYxEAZ8yLsPHV30hhe1xWfIlMAllwEvSXN7aIk9lDd8+trw7c3zvfCD7irgHwhrD85c3jNtrFSzfvOSddxdsXjX85MkO5xq6ueeo9vzGt+7zv1/2xCjMO3eg98assq4Zw+VKTY+csSwqx72TGX4zgskEhMfnG56c91TOceeo48HJTHF9cSI8+zHhZsff+I1f4+2vfYPPHz/le9//mKfPz7m83hIjXG8VC2YBxPDz+mFvxGbJImxHLdimoKmWsEdr34aU/VvZv519WqE1yu3wyzlH1VQHgOThj/fFfmJimzOSE+vVDkEpqwZYzhquJuGTdWJZe5azhmHX48TSoPilKcN3ryYaB59tA0GUcfdkiFwNnnHIbIbMLkGfhUl/QzKGmRU+WHoeLT3nfWYXMvPKghiqtlFYiqhYeJwG2kahJ6ZuoV1iqoY9rEVi0IVc1Rpdc6LfDXr/yuJ3RqnUu3L4gKa9r3aR50Pelz2HO/p6ErLfMLl8j+zhKNYzry1iEp9tI9+637Kayncaw6uLDVdXPXfuzLh48QVPHz/lzrtf49n1TwEP3doAACAASURBVLjZDOw2LW4IzNqK3/xL9/juh+f0feS4qwk5sxsiMQqdcwxWhdoXreduWzMOibsLNaC93Ca+uOy5f9Jy3NQcd55+t+GLF5e0NvOr3/D4XPP548fEKfKXf/lX+K2//td5eX7Op59+wR9/92M+++IZry5vGIZ4YE1+1fVGbBYRHVYluW0Dm8Jfv+0GfVmmxpjbrSPlK4xVbVv9nH6NCtHJgTqaJUOEuqq5eHVNDoI1OuycYmQYtMOyjcJYzJCMr9glbWHmctp+uB5xxrApLW5EWAXh021kMySV9ClIXm1lq3XCe53n2yeexczxqs9aiEeVQG3aGdY4fFWB8UxjRI4s+A5Tz8ErcVqPfkGmqcA1VBdA4sQ4DAdDpta5whzVb/ElrZUMz/rMmMu94aei9JfvNHuWYRChrRuMMTxbZ/7Z5Q7xTjkzr7Wqr4bE9z+55gM3ozp7n/B0hfUnrGLHx+c7Tk/g7XvHCI77dy1vrROfvljx1p0Fm03E+Mx6O5Gw5KQIc1t5jWBknu8i995dsl33mNqTrefBvSU/ebHml77hWSwbHh4vqJaZkVNsmnOxFX73j77Pr/xqw927b/HB/B6/8Jd/nc12w4cffcKTF5d89MnnvHj64Veu0zdjs0DhPr9epOu/byNL4c6JikznjM4azL5jouhXawtfP2aaVuceGPCVL+1if4gw65uepqpo65opaW5uDCAqwdN1FWNU2MYf3ggxJzYFRrIf+k05U6Gt6ijwdEhcbotKv7lNaawxdM7wsLN87cxzduz4/HI6REdrLTjHOA5U1TFtt8D4GdQLTNWBrTlslJwVHRsnjSrOHSLNNPQMBUmbcj4Ifaulnd7KJHBT3LUyHOqUQwJiXh9k61EkIiqj5CxHJvKyFy5C4u8+mrMZIjnsU2XD9ZT47/63z5g97MgYUvo2xres3T2GhyPrE8/NckbddIxHkfZR4v0wYbxllmBhDPfRlFWSINYUCxJLzHAXS5h73v5liGEgJctjLHw98KMmkx45+qrmsZlIxwsyKlCCsXzxUojPAs5ZRCySl8jiryALOHl7xP/x737lOn0jNsu+9Xs7TNy/rpccvqZk1eagYlp4GvuBmFWYiK+oG491UNWWtq30pG1brHUYA+M0EYZA3XiFgIhX9cKUWDaOm+3IxTppsW0NzwbVLh5TVpxRltsBqAEnujlWEZ5dB945qw+LEVG0uTNwVhveetDQdJZstNAMUXCVw9UV/TAwTQGcw+6jiSlT+hxvB5Bx1Elj3SDGYnIix8BQ7DlUM0w3SOMslbW3nS5gSPrtsi/oC9xDXtsoBgoCoERvgTiNvH+v42Lb81fuzfj8ZuCHVxNf6zxH4umzB3F88fgx25t74GpMVWGrEXyDq2Y0p0vWc9UUMLX2KioAo2IaJkeMBEBt2a1/jT2ZEobEVHVaY6QKt+fFGEcggxH6bOnFKIcFRwLyNGGbTlNJiUgM2P6K7Gdk1yJdjXXuK9fpG7FZoMwSXqutNNMwB5apcDvNVmrpvk4xOO+KHbXBuwqDpa4cdePoZtXh1DSiRufWOOJ6xdBPCodxaixaWVWhH6eIiNAHHXYe7BHYJyiv5fYlhQxGsAKjwHmfeJiFuqSKGSm2fXBUG7wVjIWqMkSjNRrOU9U13XxJO5vTti3NTKHsSLitUfKkdFoMpu5KC1lxYRIG0jQV9RMV89YWqSk2GBBEh4e7QhfZAzQFTcfsPnrtG40CUtiDISe+cdQyM5nZzPPDy4HH28AHi4qHtWUGnO/KvQ4bOrsh+wZjW5zziBnxxrKoaxYzjzEO41RJH9F0GEmQJn3PWMRqx0T1BwzYoAeDmUrk1nY1ziPZHA4o64w2RpyQQyzT+hEjJwiGFAfS7kprshQg90jKSA5fuUbfmM2yR7J9KfUqL1Pqlz2bbj970ZdUqdCgcxRbTFZTBmscTdNSNzVdmvjAbnkmLffazI+3G7w1jFPQ03NPOSUjZYaTXs/nywKSfaeK2xM5ixQBP134V6MwjZml56DtK6Dq/tawuoxYUW0yKRusck7F8HIkTgM5bHF5DsOWXCoKg4UyP8LXmKY73BNSQmJAivpJbS2xbKrDrSy1TBKtp/ZVXypb35ZNY/c13089nuOm5heXFSdV5k8uIldD4q+fNZw5w5ASZ61jYVW4ME471s9/zGS6Uk8Kxmes91T2jOUdBYnmgqI2ccD5iuyUAWqsI4vFykRnBesc6wkkZ6U9WMO4G8owGrBOJaziROsEZz21sdw/anh5vaUPmSFXRHmJsZ4cJo1eoj40xEBKmTj1X7lC35DNsge9ayFvZV+vyL6jqV2iMlfZi27DXqnEqObTPtoUH/v5vMEatevOORGGwB2ZuN4KH74YcKih5xQDSLF/y5lR21dUVsGYLnGAd5hy5B4ke+BWukeEiGGbFO4yd2pKNBQWbWMNiOFypUPI67UaASXASmZ7c8lVlehY44YFZ/U599+9h2s6cOr4IcZhfIOpO7C3j09SRMJI5W8XfFN4OJSPvUGZjdyW8PvoUW6zgk3ZbyTdNBm9//OmwlnD9y9GnGT+3rtzTmtL22jNlMTwgz6wyknhMbxEgpo9OSu8c+c+3/zGu/zKtx/yzoOGTz7/hP/zX3zEi1U42HVYw4EG0LU1f+3b9/h7v/FL+LrlB08uePH8iu/+6AteriZWw6Rfa9XqsJbIg5njP/5bX+c7v/HrdLM7nHYtL54/5Xvf+zH/6+9+xIubHVnUVc0UGafGZ5ad58F7b/HJrPrKVfpGbJb9gsOAETW82XdpNJIoVko7XLbYJ4BixVwZhqXyseoAO6+KKuOwURdaB5+7zCNJPLuKxJggZWoj3K31dL4Omcko6K4tluCbSb1cDjsDSpPh9sOM4NDFskuZVwHOx9vtn8uOD6Jw9c2kTYJNFDYx411BFoSBzRquW8fpomW1ER7WM2VGUukSdl7nLa4U/KDU2jAhEtTMaF+QU9IqOAx2x7Tv0JWJirl9H7z2HDR900FfFMNuCpyvM38wGv7W/YYP7rZsR+HFLpGjRqwJeDUmNSxCuN+N+HpQnWWEmWn5d76+4Ld++Zu899Yp/8eTpzQvz5mtR1aDin14WwyjKkt3POPf//q3+Du/+W2MW/Kbv9DzP/z3/xMv/tWnDBisSFHXzLx337K5ipjZDHu15oNHS6rlI/KU+e6/+GP+6W//IT4mvu4s2Ez2RbJVBJ+Ev/poxj/8T/4qv/0//y9fuU7fiM0Crz8wzcUOhaZBT62kzEhr5QCQFFH/xLqqtNVsbgF3YAhThAyVF0x25Moyucirq544BbJEtjFzsS2dl1KLVNYyq1Tzd+YspvZsQqIPt6JuX/p1y9/GQJTMJ31ifq3W3GPWzZQFLqLwWa//DtvIZTRssmXhIGIJWCIOTE0Qy26y5ITSbA3gKozvwFXl5pT/PEaNLFFdz75k5W5uFRdra8u7PCSSX0rTgCLe99MYKeGoqeic5V4NcUx89mpkMrBLQiPC3eOGPzif2CawRGKyOODOwmJLM4QQiMPA9XrD0VXDb//ux4jRDTJvHHEbCFPGe09lLD5ETpeLA09/t+35nd/7CBcS3jlSzvRjZlYbzmrHtrJch4RUDc7VIAGJmc8fX7CNGcmKFDEuUXm1T4wx4Zzh3r07zJfH7OWXftb1Zm2WfeHOLXdcOzR7jxGdZCv/XXFfbdPgK2VTGqegybrxyiPPmRBVW/iogi5mblaB9aannxJTcebKcivrqejczD1neG+emdee715ldiIqNC63Yj2GffFbin/Rtmyf4cmkNthBSnEvsI7w0Qg3YqhrzzomNknRzeKsLgpfUzUzjK/pJ1hf93RLR9W22KoDqwath8GIZK1VUiDHodgoWIxJWssZpQbnrLtZ9lN59g2T10KkkcN72IvvpdKFPGlrLLpP/+VqZBVG9jAji+H9TeTZBNvSHLHG0EdLth7jEg8Wjq5RJPMUMs/OV1xue87XE2NMpKSEMaw6t01TYEMmZaeytD4xjYl1yuysLvKgrujEZPj0pTBmy5Qm1Sqwlr2gxRgii9mMGGNJ4/WQzSisp/KG05MjjK1/7hp9szYLFACglrP7GcVestMAYvVv5xx1U1G3ihK2zjAOajMwjZFRDHVttGVaWoi+tjw6rrhaVFTLim1I9FNUOIhXtcg7jeOtpefthee089jK8Gu7zD/+wZpPNprAeEMRieMgO7p/FyJKcc6ltrGvneNJDNdjZhsEP0HrHb1oWuRdRdstWSyXdIsF3XyJbxZcrWuCqejwNIATLVXMHglYCvscRyQmctKGdsoFSInu1NopRB/UckEj4W0ZL/soJTp8dUVbK6GD3CmoOs51VfFwXtMNkfMpM2bBOMsXfeLJLh4cC6yBJI6b3nJnadhGYY6l8xUeyxiE7TCyHiJi9T56EYIkrPfEDEMWbm5GhvVE1TrGYSKlrIQuowa2zqqC/nbSTqPETNd2h3Z7TpGrqxVX1xsErYvIutnqxtFUjsrB8miJynl/9fXGbJZ9smysOYgz5z0AcD9jMYXt5z2LeYev1fnJecs4pFvGm1f9YhKczjLHM8svnkTmAywk8zsJWmt4uGh52BoedIZ5DceNZeYNu15dcK93keO5472l4/1lxWdb9SEEypC0ADz39UGJijEL10PguK2pvMNmQYqMU1s5zuYVn1zsOFvOSMbxcsp0tubo5C4nZ8ccnZyyOFoyO5pj2yOSrRiDIUrEB6h8VZAFRjfKGPSACJbrTSQIB7Hv0pWlc1qDpbxvu9+2iF/HRmQKZUIyuVhhCMLVGLDGsJ4Cx8cNfYnwbx01fLEJPN8G7Ug6R+V08Oucp25gchPbXeB+hsp6vLFEtGtWVZ7dpO3agGYH2ah3TGMd/Wi4Oh+o5oaryx1EIU6KC2tqKZEqESZh3nrm84qjeYMxak+ck7oKVJVlCokY075aYxwjOUTaRc18ufjXLtE3aLMULv4+VXg9XaCcgsao+69VeIUr/PrNelSjXadTXl9bWm95UAmPjoSztufX3st89qkjOM/9Y8d3n4883iWOvKEuwuBAmY3s+fRwvzG8d+TLvIRi36DRbz+LEFPcevdRRISxOHnq9FxPaClRxFnLsqtZD4FJYAyZb3YzZu2cWTunrTt81WBco7MI40hikGzIUUgpYINyx6d+Qxp7Qj9x9XzNF5vI9jX0bOs8BiFkqCtDJutizfuK5cs5+kF0W6tfHcCKzlhUvMPy4WbiwbzmrrN8eDWwy9BVLXXlcV7vlZQZmHWBRD7QuQvbCElCDqq4EmNWhZUkB6h/ZcA3FlPcyiQYbjajNmYK0HLMQjevmdWeysDVGKgcNNU+9BpSTry83hCmwGymA9MwTgy9usmZxrHrI1XlSgf2q68/92YplhO/DzwRkX/wF+lWDBx0rfZGoXCL8fLeYd0tTMU4e+h+2cKlrxv9u20sbWOojeDI7PrMwmSudplPL2HbWf7+b3a8/O2eq50w856Hi5oBy6eryPluVBSyaKHfeVhea4t3F9OhyZ1ElNG470Lsq/ySPiZRl6okaJ0l2j5IIjy57rl33PHp+VaFIox6JlrraOqaqqqo6hpfVVjrbmcroriuZCCmQJgmht2AxMRuPfHF0wtebiO7VFIhVGneW4MY0e8VjRx9ythCo339UHrtgR+gMHsA0j5yni461iHy+GrAes+ya/BFiilkddJyTljMHJhISoLJ6iBtrCpgppTYDiPDFJU0VrA4e/njkLRLWNc1e/u8cRqpG0ve6PxKYT+Z43nFKkxsh8jDeUW36MozyYRp4unFDc8uVjyUObOmpWkrwjQybSea+YzlrKJta27lZX/29W8SWf4z4AfAUfn4v+IvyK0Yiqrha7yV/bUncXnncE4diY1RIe9ahwo4r9ispvW8vcx88yyz2hoWJOYx4BoYdpZ3j4UvtsLLy8y7Z57Pb0aupsjT7cSDWcPdpqbzLS83I1eDKsTsouFcymxCNOxb2c9WbtG9+teXh6n7JsVkMyYpjbVylk0IPLnacdxVrIYdlVck9DiO5T4UyoIri6v4W+1Ni3LxqskJ7fCEyMXFBecXl0rQogAbJeONQbDkLLQlHCbRlMdnSjrJn1kjxtxGmX2maQ28fTLj+XZiiJaumwE6dB1TOakxivq2Hm8TIQUEg5PXDkCBMAWmSd2iQ9ncxc4eKS1hYwxV1WgqnjM36x3bELl/1nF+taPFsu0nphBoazhxlpOuZr6YsRchH4eJEGOZpUVO545tVBt2YxTNcXLU0bavteK/4vrzGrC+A/x91HPlPy8v/0fA3y7//h9R+7z/ktfcioFPjDF7t+L/9+f/L3Kryfw6y9F7bROjp5EthWmKmYCynPZ5OMArk1n4jNtYzHLi4clId6/hi59k7rnMr54KqU08OvO0X0zcTJnLKXE17qjtwFHtuNNWVL7h5XbU5oChSIsa3L75pbkhWVRAI6OdJ1ce9v4k3hfaqnqpANB5XfHspmc3huLjLkwhMgwDu74n5wU5lyhWDFzF6IbZp3l6x2CKE0Pfk1Li5O4dZotZ+ZymqJOoGHjtfGlj3wIn9+15S3n9pxvGubTx9a1SWctmEoxvaL2ln9SyQzeypsjOadHd1QYvE7uMYvSMobY6OLaS8UZ9H1NSQlcIiYSQjM5NvLeczmq6ymFQKMxewMI7dYW+v2hYnCzYbbZcrHsmY3k5RFzVHh7SFDJHi5ZX64bnr9bYYUOfLE+vRu4dzVgPOrmv6uLu+3M2zJ83svy3wH8BLF977d/Krfh1A1ZTIA4iewSuxzqFrYB2sw6QfHN7oudy2glqcbbbZcZeeBiFv9RFvvPActMbdpeJ2VxYXQCpp0nw/rFh2RhWkynKi4ZsrFKDp5H7y4bjtuZqNxJSugVuwuH30JmeHE7dLCokbo2hNnBiMr51POsTqXzLGCInjVpqq5e70EeVQjLWMk0TMUzaCg4TyalsD5IRYxHJpBjU9Xi7ZdxtyTnQdS3TrqYfwqHe07YuB+Rxktc2muzfC4eW8ut7xbAHq2q0sEDImc0YaBuHSGZWW7wt5Lqs3+UrhzOGhsixh9WUGYMUR2Ytur3LVJVREXbRrKIy2kWsK0tdVVgE4w1nCwW+ZuCmH3h6uaNzjr98p+OD+0uW77/Hk+dPufhoIlc1fYwlqmSQRBgHXpyvkCxcbQMpGsQ7Ou+5Wo1sU+KXqhNcVZfM4N8iDTPG/APgpYj8gTHmb//rvp6fvTX/zG8gIv8I+EcArp6LLbKptiiz7FvHynx8nW8P3muKIqiSymEiWKSScsjYWeJ8ZcjO8MULqNtMbTPrLaxWmbfOLMu2ZjbBbhz151pTTkk47mqebdaMMe4rqMOU9NBmhQNfRV83ugoNzKzwzUY4nlkkZV4GS0ZYesv7jWUtcJGsOgTkzDgGnKtwrtIoJhlbHniOk0q3Gm0JxxBIIdBv1oRpIoeRYXXF5YvnhN34Z2oPjQz2z0zrv/S0RL78OSnd8de6fjFltmliN4bDIbwXD7d78l3ynMxafNGVNghSlHMq63DWsJhXrHYbrFHt6hgzjbNlZpSJFOc051jOG8QqGmO92VIZ3WSVgXNT8b0fPeb86ppNtqRt4KTzuiZSQR/3I5t+wlnhtDWsp0zsI8u6Ysgws57ZfKayvTGRfw61+M8TWf5d4D80xvw9oAWOjDH/mOJWXKLKv7Fb8U9fsu/779uae2h4QaPuC3lTUpsseoK4wo40otKr754K3zhWXvuTCwgmcdRmrrbKhT9tLZ9u4PFTwyCWblaTMcQUD9HruFZIzWZSxKt7bZEJaGp2OL3lsAjVNEh79aGEvjmZt1rLaBy7lKmd5djDWWe5uQkgFm9RQbvtinyiBLDKGU6XHlsr2DAlFCSZEhIjod9BVjQtMuHMSBo22Kw02f0esGaf/qle8j7q5FJn7XFtX46c+46foSqAUhHBW8OictyZ1dTecd4HxqTJm4rWGWonjH2POMu88bSVpo61tzS1p2s99x+d8OPHT3GVQaLqBOSUqQo3XmH5hqOZyuuGcgiZUstgLN9/tsatI+89WHC1GXjn0SlXL66ZV54QUKSx81zf7LhY9cQ4Ma/094/ZsNoM5KrCiWHRLiE6IpoWftX1rxWsEJH/WkTeEZH30cL9/xaR/5S/QLfi/dDusFHk9vV9wZ+iCijEqJpdOeciqlB8tKwieK/Xho8vLNNguZwsDw3krRAHXSKbnDmxEKNhMyacMcxmLV3TqF+8BS+Zm+0Acqscc1CNQRQzZbVgd7akSaXTJWhKNWXYZkgxcmSFWhIOpciGLDxaOGYIoXgmhjixWV1wc3XBbrfC5JHjE8/J0nGyMJwsYTkTlm1i2QlHc8Od45q7S8udmfDo1PPr37nHX/vOA11Q+wd8oBbIgRqsz/V2vvLTqcC+sbdnmGrDQSNUyMIuRK76iT4qWqD2mjYPMbEaAutx4mYI3Gwjy66mqW1R5ze0tQ6L1xtFU6eC2A4oE7NrHE2lVAmVGtAWd8IQp4hBeUBbr8LeL55eEcbI508uQQJN7YhRSFOGGOi3O6R4UuZsqURJcaZEvRAiXdvQD5HVeuLnaVb828xZ/hv+gtyK4XaOvB+UgU7pndcumC2bAbOfPOulcxmoEb45h7fnwqwOJJtxI9xMGStCEz1LH3l0Yqka6J9GajIX6w2V1xy5MUKFsAsJY+DdZcuYhV3KxCQHP/t9+1go/7czKlieVIPZF9zRZcj8onU0OVMbMGIYUyKJ5aR2vD1zapaUIVohp0i/u+bqqmO612IIWKfcDI+h9oI0VikfM08KE5Idxhxj7RLn3+ZPP7zCvtZg2HPKdcYjh2JFvSlfm2H91OWsIqa1MLdUztA4VyRPDctOI/L5VjuHY0z6owtkydvExTYzYogm0rYV3mvKZjBstr3WmYOyFqtiad6HwKx2GDGFrs2hgNqsd4xlvmINWJOJRMakKOdg4Q4VIVpiAEfierU56CuLGIaos51RtCv3cj1gqprVNjBlfq6997/RZhGRf4J2vfiLdCsu3wMiZLKmXcZg3d5XhNu2rCjcXkqrORdY/ojjk5We3PfmljMnPFpkTgwcd5YfvzK8uhGW88wnzyMywGlleLmJRb1Ec+UJo7wSEWYucbepGCrPTdATVbIonixrv6nzntNZy/UwMaWJkIW2FP2vpsSAZ+YNTVF9xEASU1iTlldDxIjQOsPJySmu1iJ/tb4mjFt8fVSm0YDJGCOIzRhvcK7BuHk5JSNIT22gc4ZRMqZo9x5SyHzb79p3wW7JDrfPwZSGwPszz6+fNkqY85bj2uGKNNTvvBp5tQsEAWcdR12NM/ZwkFjKYieQYtlAztJ2HmOgHwLOOm0WaF9e14DTtm4jsGg9arqqaeTFZktCnastwmZUiL2zog5uznDU1Yg4wiTUVX0bLZyjcY5NijjnSFGYpkhbGaxt2Gwi4v1t5+NnXG/UBB/Zg/u0RoHygI3BOV2gOWVqJywqGLPFOMckOkDz1jBkw5iFjTPkQZidCBsjXCbhRRDS88gUQCQzjjrRdqWb1RrDsjLMVGSYUZRncrex3IyJIRaQIKbYX1d4a7nqR4ag6WASKZ0hWEXh6Tbx/sIz95Ym6WYZkqr7zyrlmDROmxvHx2csjua0VWKM0G96usWi3B4DRrWCsVnRx6XhITlCykzrC9LqinnxX8kY+qidvL1062W53Va+nIPvB8B7BRhrDO8edWr4RGLbT3x8GRhzJjqViqqqmhz1+dRVdagfsyjQ9ajOuthdTVurtFQ3q4DMzWbAoPCffsg4B1hD5U3RFoPWV4qmRtRrNAUeLCw3vYrvDaNaScSUiDEziJROmGOcElWduF5tyVlV+murIvBjMuSs8CipINuKMTmcK7i5r7jemM1irMNaqH3J/ZM+TUEhF1bguMmcHStZR6TiujdcTkJEu09fb4VJMqse0pgwPvM4JZrKsDCGk054udW4vvWObdDOmjeGmTOceMPbneVO67gOsM2G08ZxM01sph5nLJVXSI3BEFJmM00qQFcacogWqYJKJq2i5sgn3nGVHaMo72MXYV6bA/wjpogzlpPTO3Q24Fxmuw2cSdS5EzWyjzCvleMiCYkjYfWU1Rff5WxuOHLQJ8MkQls86u/VltYbHq/1d7ytXfKhzrJFwwARppToY+azXeZizLzd1Tw6ASeZyz4QhhFrLF3tmbAMKTNMmr7WVcVxbfiaH3naT1xHUQER75nPW0Qyq82O3RDVt0aEfMCqGaX3WsOideQ0IimTs6cfJ2xpoYOUxW+419aEoHrZdd1iDAzjRFV7blYbrFNq926cmFvDmArXH50NVa46HNY5//9hs5BpvOWX3rP88ImqE0pJd5w1BIFNSHwwj9QI372wbIOlcuCd59gljiTStomrKNQeXq0zQ/Ysm8zbQFcZeidUGW7QyX9dKalqhwre3WwFt4OjyvDO0vKTVc+nq8CibZXVWByApxgLbkoOm8MWjJiy/TTCXIWMiGPphCMrvEyGKcMuCPdaS+MM52MmSmJ9c837zS8wb2fUfmKYdK6iqKGordV99V3qD0kTYf2ccfWY43ff4vRporI/wRo9jVtnOa4s3znyPBu1dHy9SSyHXaMFeNi36BFebUd+877DYfjhzcQPRLjbeh50Dd/sIMXEeR/ZxMhYVUxtTfQVlRFOmFhUUAUwQagrdS+YH7fklNn2EyFkvNV76sQWVUkIIXN27Lh/4jGmRrIh5QlDIgDXu4ApFPIhZSQmhiikmGgbfZ4xJKYpcb1TPbocE5lMaw03MZNExdKbytO0DaZoXcf41eX1GyHf6qxh2RoWteHtpeHOUfFPMYbGw7wWjlvAeD59lXhxk8gp0tVRjVRFOPbCvIaZM9xpMldDJInlfKxYRYc9tQwIjypLJhNy4KhLhBhKoa4dF3GOk9Zy3Aj/8ukNL4fMWyczlm2jvXhgpEwjdgAAIABJREFUSumgH5b2HSN0LiCip523ime6CJld1MbB3Ao2R4XRJD3NT+tSz+TM488/ZxpGFkfHzI9OENNpmxrQzREwotxxIwHSyLR+SZh2zB9+k+rkfarFEmH/Oxiddhvh3XnFbg+l4TAOKpvvVntNC3AVEH/aB/6vxzfkaeBv3K250ziebAO//2rgd14F/nRjSFXDN05avr0wfKsJ3HWRmUROvbDwiVOnjYScdQQgyZETjOOEd5ZkRI+C0qkbx0K7FPUOzSmTcyDGkXFSrTYrMAQhCERr1VEsRLwRThYd1qp4xdgHNtsBUqK1aLaSEg6tjbMxKu9b3/JYfp6E6xsRWYwR7s2hBt6eWb5vMo1Epmh4dGZZdoZn60RnLd86qnm5DiRJVEbINEzJch4zq2D5+lHmm2eWH60iq6D8hikn5i2ctI4qZmSABkPrVebTOUdXWfpxYh0y11thPU7MqoqvHzVgDEMI7Aq8Yz+nSDnfntJGEdPRyGGxTcAmZl6MiSPv6IzSjx068c4CrTMsKzU/ujo/5/OPf8TX3nvAbLbANwGho2QdCoZEZ0qCEMYtkjPzu++Bdxgy7eKEbAx9aanHJGRrGGLmVV9m8gb2KvJ7jFsure/97NVbqwa2Wfijq5E7m8j7Jy13Tht+sgncjIldFC5GwyfeM68sy8qolZ1Re/OLAV6MGtFr78g4nn1+yXvf6OiHiTFmrCvIgqjYMKT4hk6ZkC0pBUQM05jpdxMzZ5gXzFxIWa1GskbQpoLaq3WFmIoQInHb0ziDy4XDIzq8HkKmqwwPT5bUfp+Gyc9Nw96IyGKAYcoskrB7ETgKwtxmzjrhus989EJFJFoy9ysdCp41wt3aUZlMZRLrMXMThestvP/QAZmUR2rZkVLkxy8jH70MnDsIXmcOFxs9ZTyZaQqK0cpagHvnmbUtL3aJz25GVkWZfr+4Usq3EJGywNy+vS3CSZktTFl4OanyS2szs0JDSLmQ2IyoCgyCkcRPPv6Iy1cvFIHcdGRpEPEF8KgsqVKCIxias3cw9RHGzcA0tG1H5Uw5yXUgWRnokxzsMfSe36Ik9v0yTTE11UtZDiqTIoZtFi77wMV6y3eWhl+92zCrHNZVZGPZieNVMFz1UW0Mk3C9VVv1hzPL/ZnHGsflKnJxObDeTVintnWny5a2cjhjcZVTJmzlwHiMbxBrSSlyXGcanUDTx1SIaYBVSz7J4KvqwLDtp8j5ZuCyD9QIx62auxrU40cwzGYdzjttIuR0m5b+jOuN2CyNh7/0yHJ8Jnx2HfE5ctIlTl1mPWoL8qytedQatkE4qoWvHcH788ypDzQScRJZyEBtJ5JkHp54Fg5Oak2H5rXOKi43kePKklHxhrpSTa0pqzADxtBWFcezlto7utpzZ95wNmtYtqrRlZJaf78+1t8jDfa6ZndqNQ1KIlxOWbnq1nBSqWVbhoNTQONcwX8J/XbLD//0XxFDT914YjTk5EE8mAoxDowDU1Ev72ObY8QW5ReBtjWczSus0dNWC3fDTdSNux+pGLPHH9wujj1qGFEEQhCYymHwoPX8jXsdv3l/zrP1iIw9f+dRzf2jhsW8pfGmaJZpp201RpwID+YKhI3WYpzSlF++WnO16rUljSiL1Rl87QhJCCEzjIlxhJS91oihZ24iw5CZN56MKIzfFiE+UaDmvPX6HASmYSSmEsEQ6krYJlUPiikzhkTXNqXFLUp7CPEr1+mbkYYJ9JPw9sxSt571ZeL0WHixSpy1mTt1zXEV+c494ell4LSG57vMoyNPazKLDkKYWHihquEHnybSTphbGMocLuas6cioGr+7STirPaY1fHEzYYzDO8dxVzOr1QI7JqGfAtsQ1YO90lbxGAKvCYkBuuzGGAGVQlpHhWbkJKxj4iY45h6OrCFiSMaq2SzCzZiYMkgWnDM8/uRTVi+f8K33T8lZ7fagwlqLdZpfizFQ2I8lR4MUcCZQe61XKgODaIfwKmbl1mOIkg9D4NfAEgppqexhkLmPpHvZuVXxTTnravqYGDY935jN+dFWB6pKBtCDYMyZXRY220jlLfMTjaICbKbEth+xkjFZ3cKSQE7CFBKVUy2EzRDZjAZXQ5iE51cj5yExWg/WkqIaPs2dYVFbaucK8Ut9WLabHdMUC8TIkI2KnSQyU4hYb+m6Bmf1/U4xHLShf9b1RmyWLLA0hhcvM8sjx7MpczYYKiucevVtWeXIq7Vw1ljSlPneNrOsIxbDFEW96GvD03Vm3Ud+7aThB1NiOyV2IXLWeYYkXIzCbhuovLpBGWuxCFOMjDGSYqCvHFPKjFEL+co5FbPYzzX4cqjeN6hSgeFMxvJ0KBALUduJJ2PmXmNpbObU2f1Qmm+c1Qxp4sWkg1ZrIEwjn/zgT/mbf/PbJFxJ+xy5qNmY138PKdSGnMlTz4uPHnOznsjAkDJjzlR1xS5qIX3gN72+S0pClilFuNHN9O6i4t2ZZ0zC1ZT5Jy92CvxEadlXyXBsAqvVhKFQfFMkRkc08HnQVPWsVni/dQqAHVPWg0WEPmiEuLOsmKLWSh6FBCXnmaLQ5kw/jqymjG8shSSKs5ZxCtxvtSkxZKFtm/Ku1G9nPSo8qhLHaoJBMj0UIC4czzscFjLEGInxDd8sBvhWJ3QLyz99FjkfDPNaqJyhlsx8mdiGxMmxZxuFm53QNo5NFmoHeQpsguGyT4gzNE7YxcDDmefDdeB6zFz3E41XcN/Ca24NBu8NlYc+7AGGmW3WCb41xdHWGPVRKVCN22vfUtJ3oWJ+gpjMlK06TYkK9D0ZEu80lkedw1tBrCkAQYMHZs5yHRMhZZqq4scffs7Vi0+58+4HxLifxnDYmVIUXgwCORKHFVeffMzHv/djOl949K4sPqONhtu7rZdFDtyb2/dRpuEWHhwt+MVW8DnQ44nO4r3FZh3ivhyFP7wcdGZhDI1X+aMgsI2pgFsN6+KXeTaveOtOzfPLAGT6kGi8ZdE4wLAZAzOrP0MQvDfkpAdi6qeD4ZQpYnygmLVtMjijkr1dOwOU77Te9JyvdlhjeaYAbsaSYiYDs8pxNKsxRsGq4xCI6atbx2/EZukaA53hs1eBRQXOG3aDEKzqBR9LYtnCk6tEXRlOjw2by8jlRph7z5AyUzRaTHaOmcl87S3L86vEcWXpg2XKCuevnTCitULMhirtWXkKq/DG0Dg49Y5gDBdR5ZJUGcTiS0v71sdD5YQOhCqKMpc5gPlJApsEn/eZo8owq/TGW+NYRVh0nnE1gigRLGJZx8gf/PPf5d/7h4/w1TGS9iSrwzSxzF0m0nDD+uknfPz7/4rT+5b5TKPPmFJJO4SbsJ/R7KNLqVlEC+a9skXOQuMdOSU+vOrZdDW/flIzjiOfXQ5cR0UfnNaWaDyVr5hZyxgSlbW0ziJkgjF02Wp6CbSN5Z0HDY8eebZjIMakZkteyV1jcasOLpOc4aSpOWkdOUdyDmymnuwt1nj6ILQl84xW5yUWQ1tXNE0RZM86y5miAm63yUDUA4+cqa2lrRyLeYcpz3PoVT3mq643osCfRuEnnyX+8DxzOWXu16l4GgrnY8SQefvYcm9u+IMvRj5+EfjameeDU8eYIjXQi2qkBoRXvfDRy8gYdNA4r6yeYK3ndKYsPmdh4Q13G8fbncWWgjeKogVaq0C7WKRY1QFZT+p5XVMVRuCeNmAMB6iElIW3xyrvreouovC0F/opk2MihMzTdeDFJuiGpdi4ecvxyYzLmx3Pf/wnWDNhvSnOeAIkkIikkbC9Yv3ic57/8PvUPvILv3GPBw/mSlgTpRaLGLY6kDpEJ+3klU1/2PfmIFARsyAp8Xg78c8uIr7r+DvvLPm77yz5hZOWm5j5fDtqmjWrub9sOW4r7sxrKg+7EMkFm+aMoXGqvNK1FSGOhJjJoqJ3jfeEkA6LXrC01nB30dC2DTlGrncT6xBx1jJrGhadwxvNLE6ccOwSrTe0lcqGGGsJMSh12Br6kLnTqX5aiEoJMMC8aw9NmX4Y/kyK/fr1RkSWKPBhb3g2edoo/OKZIY0FEySGH15mProceXtheX/uuRozL24iv/W1irPK8L2XiV2lKik3g2Hh4Wzh+OJZpHOGiHDiYF6VojUKtdFTdIiZmJVsFIplXOsN0z5ci6Y8tXXkzCHK7HP/PWp3j6va95eMFO+Y0mAYy4BSgFUyvJPhi93Ep33iXm0Zy0mZc8J5TzNbcnz/kUocrZ/SHL2HoWgKx4Ece+JwzbS95uLxYy6ueh6+85B+nAiTaJeohJJktOB+bU/ovUUOoub7K4uU7mCmD4G785qLIfD/vIL5OzX3FpZZtHy7qXm8GnmxG3iSMneXHSezmtbBalTuVVMp+rofI86ZQtQz7Hr1pfHO4nNmmiJjzFRGtdXqCmbecjzv8K5GrKMfR6Yg9EMgmcxx5zg9brnaTFzGzMzDsTO4ypU6zrJeD6hTmWGbM2OfaFAU9ZSVf19XDdno85xC4OfslTdjsxgD2QgZz00w/OHLxDfmjm8d6yLbZgVN/vAi8U4NSwvfPnHcbCIPZp4/ydqtai00Xng0tzy9SkyiYMivLVV+FZQUtijcimFSQYcgqnBvjDCzMLewSYldUpwXIrTeYbKo+EFKhyhy4LkYg+ynxAgnHu7Uls92mRtFqjAk4YUkrqLh81GfSuMsd63jug/qXBYzOUPGM+WKXVzy8slL7tLRzO4w9QPD+pJxd8M4bBl3PU9eDvT2GFk3rK5WXOy0QFZxCJ0d7a0H92shib6WS5SR19KyWKJSHzNDnJhVLRdj4p+/jPztB47WwdPomM9a3vOOH1333PQjbeV5sGxJEmkaz7oPDCFy1Na4yh+sv7e7gZQTZ3NPK/D4pqcqCPMxZQjaHDGIKuPESBpGVamxCgFaDRGJlqPKM1YNfYgcGW0b79/Par0FEWaVOhIvvOMmcQCwz2ctdV2V9x25ubn5uev0jdgsY4IhCnOb2CZDbYU7R47jmU68hyg8H1SdZCfQYfjJKtF5+Hw1MgWdgp81lmlKxCA8HRIL63irNbgseOPpnME6ON8F7jZqR32ahY1xvCTTWMvD1jN3cBVFHbRE8UrKWYlMIag4Ba8p0ZRMxpryMHPGIbxTwxDN/0fdmzxbkl/3fZ/zGzLzDm+subob3ZhIgMHJJjTZoQjLVsjaSXbY/l+89corLx3hnVcK7uSFIyiTkhl0iJZsgOIAkgCJBnqqrvnN994cfsPx4uR91aaANoPkopwRQA39quq+e/Pk75zv+Q7c5GLhQirUKozYkx4USZVXQ6ZxQrOfjhFygVfnNxwddeDvMHz6nKO7jtevrui3O2rODBOMo2fyd+juRjZDz9OrMwZ90107bJbLVVGZ2xyx1lDmPJP9rLUHKlIppkhVuOwn7h91LNuW57vCbzzJ3IuF10PhYkhMxcCQ4D1jVV71E5CYtKCVW0+C4P0ta2CzHamlog6mAgHltA3sirXAoMTAHCRlqNWmH0lVybOtxm4s1CzUoOxKIpVCDA3BNTZTlsL5zQbVyl1nOTitU9biSKMtkJddc7u9jwjXV5svXUq+FcViZMRKcMpXVnBvIfSl8oMXtuvo1LLWsyqXRemi48/PMw8PApuk3IueJ1PlSSoceRj7ynLhebIttE5YeWHlzMr1k7EScLTiuHvo2F1nc3l3DiGxdOBhRrLmlmoe1jdDpu433/uWS/c2SPPeZW7DXo2FF81++HzTvt1+KdwSMItAqjBqZZkz4h2L1Rp84Pq6Z9lc0S46pvKaXhfkcAhSEBIiE11rSVn9tGGc0q2qcd8apvnfi27GmdQi/fLeq+sLN4iItVD7chtLpZ9GlqsWCYFtKVxsCqUKPrR0rcHBfo4EcTLvUBwMkwnCuqAsPByubfiexsSq8WzGiUYch22gVGXhvFk/1cLjg46u6yg4kMAwTeRiWZ2qBkjsaraiV8eyiTy+f4ibM0RLqWz6HsHIrEEU9Z5drgTvaILQtQ0xmAlh46Gk8Uvv07eiWLyDdQONg8tRKRuPUlCnHHaeX70b+L+fJ7IXgkKmEr3wdFs4isFoD0V5nZRFK9zvHGfbWYKKY5dhIZmVgKvKcXRsdsUMt4PjoPUcToVtFj7rE8dNoC+GgoE9nQNKrvnWUhb9KaOg1Fv6y3Wu/PFN4qTx/2+XTRH2akKZaTKC2E3gzERvmhIxRk7v3GG9MI2+Kz0lO7rugBQapjFTs6AUyhyfN44TzjkO160lKGOzS5/tpFgvOvpxus2arLXOWTfAfsei5qd1W9hAENMPbYrgXCSEwJQS0zSRJhu6m2B6e1ETyO2ZABWlazzHS8fhQUTUMQ4TQ8rWajmlOKE4mCb7PafKIjYE31BwaDX/r1XwlJRpg6cJnpup3vovTFVR3Ew5sve6H0fzbVMzXXw1Vm4UnHccL1qOVt2cxmBS8Tz7qP2s6+0oFoFdVna1sMnCi6wcROG9ZcXVwpkqzsOxE3bJLFDXradJhaFYr19qZaHm4zUU4chb4KnrlCKeO3hepspKzDEREZ5c9fztOx2f7jLOwckycLnJDGoOlPsBPjo4CZX2IPKj6zQPxPNtPp86NihXc9ScN/O7opSp3KJPgs1Gts+Znfv3xQNzESklJa6vrum+9g7vvHvMveNAmbbs+pHddEVoT5EmUHJkGHtyEcpU8T7Shci9A8vQzDMqnCsE7027UzzjjCrlUm7lxc55Vk2kT9OcVGZtWXCOu8vIYTTmwzT7EsQ2suwilEzNGdHKQiqTFlrvKMUgaBwEB6frSAx2Y15td9zMVqtavW3cq2nw2xAYx0Q/e5xpNQCi5krXBFonTFOhlMpxYxmSY6qMKc2FY59bLZnrzY7ojVWwLYXiPEWh9cKQCot2gbOVJNTCkCZC+NkA8VtRLGp3D6eNRbvlklkGYd0pD5aOj84z19laB+eEPiu7MfOwFe6uPOd95ah1OKe81wqrRhjVBvehVpZB+eQqI8ExZMdhdNxpHC+Gykd9ZTNZqFERj/PeAn/2cwjKcfR8rRPOsvIkmApR9os473i8DCwaz4+vE2fbHVPJt7PANCNs+5Vn5013v8uFXX2DRLmZ/BeCY7Fw9Lsrxt0l9+++w8nJEuSYnDacv7zg4uYcCYcsFoEpNUwpozjaENmliQ+OA+vguZwKuRpbOXhPzbZf2FBvE4zfjP0m+FqEOBMhbaZyIjy/Hlg0noMIZ5OiagBvzZk0jqCVbiaFne1GotjclqvZIS0bz2phBMdaMq+uNqSqHMVAA4gXzgbzMDjohCmJZUHqrBcqmenmhsM5ImT/73kxen4qcrtAFgzRm9LEbtcj1TyrYzCWwnkxOUQSoWnibOGEweTPL79MVfx27FkEOInQqnLi4HEH314IHwTPaet4/65n1cCBh9MgLL2jOuFlUoYiFG8341cOjX/1os/sJuWjTWLRCd1SuMzFdOvOlmp5SjhVxmInw1Fj23yH3cD7nEUvwvtLz7sHgee7wpASKU+UmglOOe08J0uPIETnWTSNLb7gVmK7n33KvPeITm/NAXUequ2cUrqm4fDghC62bC/OCNLjouDCknZ1wt1HD1m1IzpdEVzi6KAz9/g20nol1p4PHq0Ifp6H5u/HUoDNB6DxjqJmyxTYJxIzpzEXvDgO2oZ1E2gE+pT58esbyjTSuWLNbbW0X9PNeDrv2Y2ZNDvVl2rCri6YpXrb+FmQVrjajHhnRhwShKupsEtGtR8nY/6uujh7KwslFzabHbsxgxOOoqcNRq0ZUzENikLXNtixouSc6YfJ2N6irL0wqjIUbqlMJvqyP1JKwbvypUvJt+NkAQ67Cs7Rbjy7qpys4PVY8Femmlt6Gzx3Q0WDPdlukuK3hUdLRx/g1MGDteNQPL/z+cRdJ3ReKZYdx/M+07UtdxYty3HgrIDkyvVYues9y5JoXWWrsE+Fjw6+cRT5+DpxnirdzETu2gbnHFNRPrwspGoDs1NogofMLd0dZulwfRN+us963LdxzMW2WrQcnN5j2TiKd1xdvOLo7gp8h9YWFxsOTjLb/oxpuGaxWHH8+JDnT7ZcTztElc0uk4oV4n5bX7UylgQorfeM2YZjhxXL3o49z1SfBeYPdhod5/3I2Zh5frnj/rG1MqVkGmfAhEmoK65RDlxgN2TATrMm7v3DzNC91ko/TiwaZy1Uhd1M9lKF3ZQJ3nHUtVSCcdyGxGYYucpK1MquGGiYa7VU4lwpWll3+zh109dvdiO5wmW2dOahis1IM2tj2RpyhnhqSTx/ff2l9+lbUSyCcG/p2TVwOUCsys5VLqvSBog3BV+UABy2nusoNNlzgNIr7Cbl4cmCdcqkXvnWqefJMvDDq4nmwvH5MFKzcF0KhzWx0kpU5U4jbHPldS74JNxxcBIdF2PGovqEQ+94tPJ8vs2cLloWOIoY72vblzdJW0CumazmOuOcRURY12DN9LxCoHFzTsoXeFpWLsrhouPu6T0aLxyeRMYS0LpFfAQ5YJocNRzTHULebJmGG04ODnjv8ZofvFS2k+ejH7+YGb4GRFQg5WyS4flkab1nM00GAuylBbdoXaVPldfbSlh1PDpcsBomnm8mLrc9y+US1OB08bavGUpmNySLVA8WXDulikzQqu2THObNnMfBkqJzpWk8y0VgSqbhrzNU30YrLNu0V/y8A7vMhRFHKkou1Wx3VdFaaWKYf12ZcmY3mkZpJ45p1hYhbzh8bdMgYi6oY5/MK+5LrreiWIaibEf48CyTkuP+QigVtrmynYTYOnNGrNDHyr21o9/B6dLz6eDYTpntqLy3Fs5ulO++TJwEUyFeDcpJCPQoqSg3Q2HMhZOl41unLf/HM1PfXU2V7QybNl5IM966DsJQgRBxVdiORsXfJxf7eZVfZpcRnQ0fYJ5DbH63rHeY6SR+T826JTHueYyNdxysFniB4+MDjk+PkNhiRPkNpSzpJ4c0R6yOF5RpS7MInJx0XL864OXTllyU1ttNFMwQYI5H3y8eK60TBme6DpXZ/ghzjVzHQCuFyyHx7GbgagzcXTY8OAycbUe0ZPzMwnZ+9hvwFV9t+E6l0gRDpbZFmYCmdRapXRpSKtRi3gnOCeRMIxAbxzgVvCjr1s37KyWlibN+4qIUdgXEO3LJ7PMFRA1RbZs4KyFh6EdyTrTelpClQvSWJqDOkMImBPYxE30/0I+JL7veimJZeHjeF5ZByFR+8V7EHwgfnmceLR1nk3I9Vo68PamfvcqUKvSt4kPDyarhxQD3V5Xv/Lzn//px4rOrwi+fNDzbVt5bR7abiZ04PiqOQYWhVH7wOpFK5aQJbIsd5Q5lIdDPM0Vflc92ykfbytlkoUBmbWqkSu8glcJU8q174xcxZZlnEic28Gfd15KyD5plP9c42OwmM1FohNUisj5YgWtBCpQR1ZGcA7UK4iK+WeCjI7aVh4/v0PrK6WFgEeawVePjWASlvikYUSz6u9jJGH1g0TYsmpYjV/iVpaN1DT/aFT7aZJ7eTLTRsWgbgoMpF7wYD6vxSp/KLO+xG1FUaGcFYgjuNpsl7TIlF3JVxpToML7W3ehZBs+TMpiPWmxhTu8qpVCqQdeGNhq9vyi3cH4b4+y4b+/vNEw4bL9SnFJS4UAiA2rs8xBoGts7eS1sNj3DON06eP60660oli7C+4eOp9eFNiiPV+CPHVEgJaULgauxcHJoUt1chKdXhWspbIee6yys1ys+mhZc/HnPxSgsYsuDoNw5cLweE+92jusJNtHzyU3iqSoPvUOLQ1pPi1HCGxUaHN+/SuyS8HrI/N6rnpFA4+1DR/XWsbFPyjTrwBG5DS76oqEdvKmhXApF/e1Tk5lsOK8l2PQ9KWVOj4/wAqENgDejPedBR2pJVPUGe0pDzaNRctZLjg4b9MGSNjjzNxNrJ7/ooI8awBCxgnEzOXTdtaQCm+p4koT33cQJle7Qc5YdL0ZDCa/HiZKUw8YAAolwVSCoM/9yDNWrxTLq29aiQ8CKbDNMc8xGASrLJrBqAxfbkW0qNN6jwaMz6+Fis+NqzLfmIFrrbTxJqmbBK+JomsbmxFKZhg2Pg/DhqDivHETPlLMxnZ1nEqGNpn0RJ2x2PdthfPt9w6YEJ43y3nsNv//ZxJ+9LgwvE62DTYXdmLkcKk83hueunPD+wnHpPHecp8nK6b2GoRf+7EI5HzL3DhvcNvH+SggeQDnsHI+D4/wmc7cLnA2VnQpdVbbJ6B+dh3W0fMk+FSqOp9uRomaps2ob7i0jDxYGIf/oMt1yxawXrrcWoLKvAmc/dzMRM81DtH3dmx5McEzTyPXmim///HtM48TZqw0P3zkAbYCAaKZMW7J6nAvEGEyr7pR2seT+ozvcv7fkwe+/5k9fD7bs5AtLUVWCCM18AjTO4XygayMh2G5jSPDJKNBGIolPr0fORjOx22ssuxDIVQkedmkOLHKO4Ox7arxQcQTnWARLjwYlTYmbfsCcJhVXhTshcJ0KGzWdklNh2QbK3FJNObObRkQts0Dm3dRe8SmYECx629nUWhm3Gw58ZemdzVSChd0WRSbHeh1YtC01G/o29MOtzOJnXW9FsaSq/OmTzKItnKqwRFi2jiKVP3xhdkeI4+W28PAo8HJXuLP2bCYFp3hXeP7yhpvRzcYTheu+54eqPNlWvnkU+OFYOVrCbky8t/REhZvZFWTaW+UI3ExQMC6VE2jEnC6vkqn7UikcucI3TxbcW0dqzlwNtlRVrYiIhb8yd2NfpJI4O3mGvVE1zJ697jZIqNZCGa549+GKy5sFz15saJqBk+OM1sy43TFsd+Rq8HTbBGrXojXiZOTx+3eZ+i1HXUPjhLHa/oIvtHsT5qi5J+c0ce/GaLsXN5+Yn6lwt4ncXzuqjDzrzcRc5u1/QMlFOdtOiLdCKcWKIKeKVnOCbL0zhBBhGCw2r2sDigXDpgp9qaRsoEQbPOuupTorsCkVc6mZx0EHXw/zAAAgAElEQVTVOpsTzr4HGALZNMGWmChXu5HLpBx74xNez1ZLHqgpcTADEfZ5OTZDz89uwOx6K4qlCpwD06byKDreC7BLwpF3/L13Hf/mswnVyrcfRlYLz5/0lU+3hUUXyY1jmArPrwYWbeTeypOLBeNMuVIdNAjnRQnFju9XCVbzfNSnjMtw1AppZhZTjFS5CobJH8aGzawTBxhTwaXEt44jRSOvh8rT3tR2NiPYjVyqLfdKrZRq8KgTxy7ZzWt2Q+aW6MX8jxsRGnYcH8NiueD588rrcyvAw4OWbi0cDML19UAaJ6Y8srvcsgiO6AcOT2F3MfDVh4HFD+y9KbXeSgqsFTQU0WHm69ZaGggRgNbZzLZJSgIOvMc3DasCuyndtnRVTY6dsy36QuMpZab+12o0Iae00eOjQ6TSD4mrm8k8itvIVCo386mcsmmYWik00aI7VJVxGK1ARG7zPhsHnfcMM7s6BsscrbXiXOK83/FyKBwAncDkHKmaldVUldhY4rOf3UXPLq5NJfklS8m3oli8CFdj5W4UFh4+3BS2SdikyuMVfON+5OllZR09H76caL0ndh2tF55dT/SpkAo8CoIvlYuxMhRl4ZSHp5HLXFh1jsukrLXwMJoj/Y+2hYpwGoXDRvhkyNTqOAzC4D0hJp5muJos7HV/Kcou21lwsUnsxomSjfflndnBtjEQXGQ7ZcZSycVZnN5sdKeqM0fLoPOqSpTAwXLByeGaGDJNe0SaKptt4uJGcGFBjCu69YLiBnLKUBLajKh3hOURwfU060P+038w8uu/+5zz0XYe9Qs3wd5ZJjoDKfZKwf3XOITozdllzJALOB9oWoeKY5js4ZW1MkrdoxhMc0uj2AMQlG3JNI1Z85ZpYDdM9CnTFlgFz5DrLA5TihcmzO0meoFqy8LNbsswJUMS5/dfcDRYNzCUYmCLB7RQUmYaRpwzheiBN3XkPvfHijcSXcA5RbRyedWTS53jQ3769Zfa4IvIxyLyfRH5AxH53vx7pyLyWyLyo/nHky98/X8rIh+KyJ+JyH/+//X3Bwe/8MDz7qGjRCUHiEuYgvKqwMubSgjC07OEFGECzofMD897+qysm0jjhSFn1gsTeWmtVHV8cpm5mpRlC65WpiKcTcrLZPEQiLCpZuK9nZRU4LoGHMrfP204jo6bbPuUOjdLojBk+Owi891XE321Vi3OfXMMjiYKXTRdenSeNgSi35tN2CdSVW9Pnr3J9+rgkMePH8xRG4XDwwWLViipcHa+43pbmLJHpcG3K+JyTVydIs0xygFajpimNSJLoveWSckbZrHjDU/tNk1tfi1V51gKbK7x9nAnVxgnZSpCCA2LtiOEQJ8NKu+iRVGIGLctOmuLRIxtsYzBnuLNku3Ohmgvwtlusm16yriq3Ft6jlsr4BiDQdI5c7PdMpX5hMTar6xGaA3VIkU672nFJM21KuN24o6YM2c/Uyka3rhEHy3bN61oKdzc9ERMxv7XKpb5+geq+quq+p351/u04m8C/2r+NX8hrfgfA//jHAv+M68uwJ0FeK88PBCOozkaPlwFGueQAt84jUyq3AtCKxXKyJAywzRx2Q+2tGrM6vWkg196GAnRVIlno32AUxZeTZWKsM3cLhNTdbyejOS4bANbIFfh2a7czh2Gwugt2zgpfLYrXBVHEyJd29I2Dd4bilOyDfCmBC6g9iRvnSFIcX+Tzq1aVaVxwnrtee8rdxAJiI40MXN4tKaJkd2YOL/o2e0SuQgVj/eBpmlxeOvXC0x9ZnNxQ5DZ+WWmtO9vA9tLWHKyY16c7pnUdR9DXm/nFxWj7eR5qSrO08VI20VuxkwUMwncx7J7gWHMSK18sO5YenDeg4vstgNNcFylRJ0XhCkVLobC+VBYNZ42QDO3VNM0cX29sRlFDN3rZnM+7wQPvBuEh63MpFSTRJxvdtwUiyW8Lkbjyblau6hGK7Kg3zlmvB/xfxH3/2sUy1+8/gmWUsz84z/9wu//uqqOqvoRsE8r/tkvwkHbCYtWaAIctVCz8vlV5vOrzJAq19vKea9cDZVYoQuOZXQ0zuaKqsrlplAKfHxZebmFo87x7qFtjm92yqe94fWLmRP17qGwjCajzdmEZjfjhKJcVuXDUc2BRfbbib3s1jbyD1aBJjoz6KuQijJlo1OYo2PByXzTodyRwi8tHX/rZMHfvbfmF09XNLMBRuMd6yZw5yBwehqgbNF0jdQNbRxZrBw+BIZkLNtcbHfgvWe5cLRtsAXhdM5w9hEr/4qHR+E2RVmxwjQQwhaqisw6m/03Zz/stTwyI2YexTslOLUAoZQ4XLY8vn9is1BVTjsTUWm101JnBrYCbTTPAxB2w8zadoKKcdaWTSQ6z2YsnA2Ju8tIGx1jnsgp0Q+TnTjzEsVhy90o8Cg6Vs6cXfD778Vzs5s4CfCwNbh+bzS/mJHAZRuZYzapKXGz6xkVW0D/jOsvO7Mo8Jti1iL/0xye+jeWVnzQBb7zgeeTJ4VXN4ag1AxjKVwnpXWOH18kvtIKBbgo8HSTWTTetrLzmzgURz/Bah76v3LkeGclfFjg1bbQz9Burok7R/BL70b+4EnhooedYk+1xvyr8iy5nSNVbvlbqrArlddD5sBVHnTC86FSijF0g7PMFUFJ2Z6GSwenUvh6YzQOFwIhOG6KUuuSH930OFWOusDB+pBhMxBdQTTjfMT5QKgBlzzUDlyw6LdacRohVUq6hvwaz4bTR5HQfYX3fvsV7uOb+dVXCw2SfYtky1Tbs7xp027/X9/8XDCNSa2VKU2glQ/e/wCPfc+T2oPiqIlcDmnWs1hxOC/ExuYJLYnNtmfK1gLHYOFIjXOMZTLvgylzv7U28GaaIDRMaeK4NWPEospU4LDxlq2D8DxXHrcNrjojUdYEaeS48wYYODOFX8SAK7YTi6HDzcyFUiubXf8FefVfr1j+Y1V9OhfEb4nID7/ka39a0/fvvYIvphWfrjp9/aywaAW9qgzJXBr3f1Kr0gXh/RPPR5vK1FcaN4eSsicpGiybp0rnK4drx0/OMleDZ+GUZTCH/a+tPMei3Fl5/vAniU9vKorHieOk8yTn2PY9QSrbpDAfzvsloohSquPHm8K//KSnr47VHOpZmINI3Rsv41Azj8l8pRFqcFTnaAR8rXQVvrYIbHPDJhd8bChuwW9/9yWP7hxzerrk5Dhw//6C5aJwejhSz7Zc3VghRy9cDAXZZI6OhG7hEVmjFSoNB4cHuC+0FjrvKBpn+48KLJowpxLvl6RvPjBrPUHnKIapZEotHC073v3KA548eUHFktKupsxhG8wcvb6J3xAvdG3EBduW77aT7ZQUci54ZxqkpYe7rdCoY+EEdQLV2rSczMSvzC9v5YS7XrjOysfZvBVOYsBppqoFO4WU6Kuyy0rrHVWUqRaiKhkhNN3t3VpSIeViLOe/7gZfVZ/OP74UkX+OtVV/Y2nFqvDxVWGopoTcpMqNGo+LGY4tqjy9LDwZCoilOC2CuXRsckXE8j82U8U7yMmMtLd95e7KFn+dKHcjUIWX1/Bqqxx3DtRxPZnxdaqF3Vg5WToOFtBPNszvX+f+SlX5/uXEtir4wLrrLEW5wnFjtqplSDzCzPWqC+zEjPWmrASUCDzwDn/U8kfXE1uJ+MUJdf0A1idoG9EQ2Q4O6oT4jmYx0WgmJROs4SOuUVzjjSClhUKlT1BrRFA8kGU/2NuT1J7stiwUFJmN8/bGgIq1k/shO5U97d5x/2TN8fEhn376jBCEcVSzGuoaWu8Yc6FxghdPrtD6gPcR8GzHiZINQZtHQIoze6jtWHnQOooDnUfxWjN+HElpZjbjSKJ8vEso9nBaieCaSMK6hpzhVT9SE6TMbTxIzZWDebeybM1l1KOM48RumAh7z+m/arGIyApwqnoz//wfAf8db9KK/3v+/bTifyYi/wPwmL9EWnFWeHJdWS6NbvLjs8okwlfvBl5cFh4Gx1Wx3vvhynM+KAtv0XWTWgLU6VIp6rgZYZXNUsJppUX49MbEQo+WnouxGDYfAxJgzJVUbdjPaq6IMTZM6hhTJooNrHuK3e17KWYknqoy5UQInsZ7DhvHyil+HPlmVO63gU0VztSCghThKhdKrpx6+Ern+GbraV3HSyqfff6M1cGatQ/cWx9ydHjAZpfZbq0Z3AyC+AVtFKQWFp2wWAtNK/gYqBV2mw3Pnu94dbEB9jIAe/V5hqvbEGlCMHM87+fM+nlNqXOh6L5trbMHsPX6jx4c40JDrrZoXS4iUpXrKbPqAleTSQQQc5kx6Fio6tjubMeiYkwGoy8ZGuhjw1lKxNCgU8K1jporN/3Ei77O8mwzWXcieIROlVGVPjYEhDJl+s2O836kTBZeJM7CjfaEioM5ctw5hZoZtjeM00QU4+79lYsFeAD88/l4CsA/U9V/ISLf5W8orTg6eLmtvBscRwcOzcrZWDg5bqARPtoU7jeercKTTWasQm8DgVnrVGVMFSfZBmvvOJbK0guNwE2BtYcHnePZvFshV06j47NduR2wVRyCQ1RpXUZ9oeYvWJyitzdU1S860lv0+OkicDcq63HiOyvHw86TEH48Qp6rzfTewo92I9deOAnw7YPAlCu/lwaeP/0YYaT0j3h8+JiDZUupDc9fZqoEUvF0vn2DWDlDtpxvzZ9sTDx7fs3/8r/+Li9enbGfOW4DblUZteAVjpuAeDOLGLIlryl8ISVAySWTi2VpNrNx+p3TQ6im2RmHYnr6avPDu8dLmjkPZhG9aVmaYK6dNXHTJ6aZ7qO1ctQ1pGJulMZicYyhoc4GGFodF8m+3oujaLndB1WULWaA2AYhBEMGbxhmM/eCbwPJMUu+4VwF3wrSRCu8zRWvX78m50z8Qhv6VyoWVf0J8Cs/5ff/xtKKReCkdXiE15vKz90PnH2WeHVmfsQuCATHn1wmU9jNGScy85FUlX7eVzReSMDpQqgIn20Km6Isq2eclCebgsegxu887Dh/Ws3+tRo+6ud5YztBcJXTCGOAaZqDVwVaZ4pKkNlMW0ArUgptyvzDk8iDhUdFuCkwDNZUCNamoZb7/tGYOR0cr4bE1xeej8fMZnXM+vQ9lkd3cXEJVNrWk4HNLtE0DVM2dKpx+/jziLoG1Qyu4clnL3jy/IxFZ60GupcNWOEsveMbq8ixr1yWid4HknN2ghRD8faFlXKejS2EZRNYLxtCY6/HYsrN+b9Us1fajomTZcPza/MCa4MQo4AUSk5c9z3OmzQgBk+pcNGbq8pYMsF5fPSIm4mUWCsYvbftvd1hFtct5jWmIrRNwM1JkZoMKi9qi9WU6+zub6fZJtd5hoLVwYoh7+k0bx6AP+16Kzb416Py2ZXyCHi9qRw3yjsLx7PRjtyl87wabBPe4ubsEWEZjXq+qXWGeK09awNcJ3NVr2r0jie18Pq8MCRl7R21ejOP0MpYwHuDo8E0+LkYzTw2QtFsbijzzbYKVqjXMwUmeCvY3Xbg508ij1eWGz9l2GZlV5UothNovAESD7uG719u+XQofLRJvN/amXVw+pD7D97h3oMjTh8cQFhDjiwPDunLltDONA2UpnEsVg1xEZFFxKlQhiu2uy1NI0Rp5nyWmWwI3FsEvrFquRM9TpUmF/oyoSHwwnl22Z6uln9iMludoxpCcBysI+qMdlIwz6TGm9N/mh3xu5n6Hr055zet6UZKVi43PSlnVI2K1Kc5BmJ2zcm1EIN/s4PKcLnd2Q5nLwLSmVOHFegqRNbR5NwqRsnJuRCc42qwhOUg7nYeOYyeSMU5C6262WV2pXzpQhLekmIBEFFe35go6aATnl5lunkfolq5no/pIMpp4yAaLOiAx0eBqwk2PWhRXBBDQdSOX/MoVu6sAmc3hbteuNN5PrnJ9GlvRQT9ZGZ4VRXnQJzjaqqsWz/vTezvG4oSnLllqgpajYj5bht4b+kJ815lrPB8rBQ1K6UoNvyfJ2VXPQcxsnOejzaJr0bHRjrudiuaaCZz60UEDaRimpBFZ5tz5xzBW7GEJiLOI2K2pdvLa67Przk5bumnghcrkoWDX1i3fG3d3TKHa1XWQWhToisJKHyscytWTW2ohhXeLgGbLqA5kdJo/sTBNDF7DlqqZhvbBk/j7GRY+ICPgXGobHejERrljbirCZ5c5kg/VVatOc8UZ4TMYcy3O5s9AdVj6GPWSqiV2NhOqaTCbjfcRn5HJ6x8IFWLNDz0wqNFQ3T7pbRwddOzS2WWq//s660oFie2vfdeWASjifhgZs4Bx+CE42VAk/L+gScXZfCOi6FyPWYCjuPo6AcDC3TWuj9eBRYjXKoaxWWTUYUeE3U9XHnCuWPM9dYcGpnpJzhSLmxE8FLnGcGi6XZFkVtiZWWF46td5GHjOIr2dudq8oLzrBSEiHIvOt5dCFOpLIAHbeCzfuK8CzxJil81lnNfElEy66WnlEI/2Ga9acMcSFpxrpqZuBZ0biHz0HN9fmZ0j+L53oevyVVpBP7uUcM/fbzm46HySa+3nmheLLX5WCunFFzO/Fmeoe+Zt+bEWMr7oB83uzzoLEe4pdIIDCmTsifPUKxzjjh7p6VcuenHW91/Qem6SE6VXOZ5zgmLJsxaGEjJkLi9k9ne3HA/iO+Bce+caVlyoh+2dpI7Yd14xmytNsDKO2J0+BjwamYam+0GMEPBt75YAFbRkdUWkk9vMkNR7naezzaFscJhdfyd9xb80bOer92NLLpKt1G2r5TtVBmy7TdWHoqD87EwzaRFzK+CqcBpdNxphAOvpo7EgrO1ziQ6LbeF4ySQtSAonbdYPdX5plGDJL0q7ywih0FYOOiwbiEB50nZVmM3C7DUyh0R7lC5FOXdzvNqhGep8PmkHK8q5y8/58HdFS1rgp8oCcbk7G8Qs/MOonRNYNEZ9KvVbpLU95yfXfNv//QzfvP3PmUzZKLC+63wX7+75GErnE1GTcnzstXNUFlwwlKVX24Fp4XvDzMNfrYKEuGWm+UEWvGmhQFWbaTkyrYvVNnn2sytWVHjmAlMQ2YcDYTBgQ+eYco4NdS7OKENgWUTrFWLkJJlpsh+NpzpCPULBeqcw80nXAyOfrRg1iEnNtlQsCimVt0ANxLoQoun4qjsdgONd/Pp87Ovt6JY1kH4ytqxm5Q+Ky92lVT2qkK70ZcePnw9sUvKTV/ookVQ5ClwtTVq92omLr7YFRbWqdFGx4s5hWsVHCetsPYGV5ZiGow93VzUAIPGC0VNxVerokFYzsuzERuTK8qQCwsnrILxkdbe9j9a7WZ8MVqsXJ7pI1djoW+UOx7OnXJV4VsHHR9tBl7nig4j/+r//D1+5w9+wHd+/h0+/ck3uHP3HjTHdEcnxKYDVbxUfBZc9vSXiVISlxcX/N6/+wG/8Tu/zx9+es5msgmrEfhH9zq+vvK82GQue6PKuJmb5jBZgHmIGev7640J2344VTJq8mGB4LHeX21uc6KUKTNWo8YE9ya3pgtutoe1sCipiWE7Wc6MWKGAReO1wbheuVS0FJaNsz0MhZwmouMWUSvZfMLGOrMM5gesRd0pUxnY7Xqy1jcPQSesgvke5FqZJJi2SCto5Wo33vK+vpgo8Bevt6JY+qL8+Zl5Dt9rhf/imx3/+08mdmp9vuyfTLnw3/z8iu+/GvnRq4yKsw8e8F64t/BzYRhd4qunkU82htl2XninMXntdYalCn/2IrHJJgqqM4U7iAO1Vgdn+EojngalDcLnQ0HE3aojh2qD/t3geNS5WcILN0W5nDNIyozM3Ag8Hwr3W8/Xl0K/tdMsrVp+uJ1oR6Uvge1mx//2vQ/5t3/8EV0b8LElxsCiacE5CwnNCacZp5WqhZQStVQLTa0Atsz9tcOG/+g0Qq0MWbmYClOZg5nEcic7D1G55YshlceN47IqnyWDp7WaycQ+oVmlktV8B4Y5tHQZHAUYpsyqiTabeKVxFdJAvx0sLHVm+9asaKn0xfjcOhtrLJuA9w21Vnap0KfCVAqrNiDB2OVflEgHb75tOWVi4+jTZHEh9gV4NWa7LVkrMUScUyoJaLjuJybVLy0UeEuKpc7oxq7Cqywc3GTGWhnKnPPhHCEKKVU+vpg4nyygZ1cqpcB6HiQDMCRrHxZO+MZpgGgf5tcPA9dTZZvgSOC8VA4a4fns2ggW+6ylcP/I82LrLCwn2s2fnGfMyZJ3a6UNAe8cU85c5sLCBU4C3CQrgKtpb7BtxVxRRhG+d5P4Vqn8hycNQwE3KDcO/ihbmE7TdIQQqQp9rlQxQ7vcj4humPLcuxfTjgRnc4dgT1iR2xAvvtFW/ss7DU2pZBVeDpWnfaIA68azjoGFM2KpxxDAsVQKQnLQtYFIpmi2ZebtXGH/XplPiVr3vpb2d+yqudQowqLxBK9ombjZ7Exg5q1l0pm/Ema1qpEkhSY4pmwzSz8MTDnT52yzXvQ00ZOHekvPCd7RNg1t2+C9cLkbyLObpYjZMA3FWN2xOladeRz7YBPKOM0Awv8fikXEUoWnquymRHR+LhTlTvTmsTsJU4U/PEscNIJT4dgJEzMz1gm7XJjUWMw4GBMsMVbAN9eBH59nWqdkZ1TzJdAGR07VEKx5gyvVBmpxhs5vp8LkTaCu80ZMtXJvFWnaFZeXWzOzFjNq0GpuJ2Mxh3xVM4dYOSHFwL++HFlG4b3Gk6vyMkP0DX0eKHnEi9LFhuCtyPZ69b1gTLTOLY+1QvvYvlxMgFYKBCp/byU8bmwuebrN/MarHX+6y/ziqiWI0DlrbztnN/tYbBtexOyfxMG682z6NLc1ijizuG2DmYe4WigqqDgodqKnWtiOgHMsoscHM9vYbrdMtRhooDrn2Jtt7F4rBEoTHT42xBAZ+4EYvHmflcJmSLf2RQYlC9H724eXiGPT96jaPk7VHgC7XFlGz8J7uibixQRuWhJBzfTiy0vlLSkWVejTLCl1QuvgawvH09FamaPWI852KNGZAUXrYUo6e0fZX+Kd7UGOF47tUPmDJz11JvbFxvPJzcDjznNRbJY4chGZdQ7VAeponOOT62xPzCR40ZkTZZTwMj9Ji0IXPN965w7b045VP7AKsG7NeOKTbaagBgoAp8HxTiM8bB1TbfiXrxPfXhYed34uKo/3Ea+JO40ivjBWsT8/y6GDnzPfKcS97U+FEARVexrnWZprWIWZ021G5d+cTfzRNnFZKqOqnShujgNkZvNW2KkwzDdy0MKxWgbnNAuonFYeLRJfuxORWufWrNDMn4+gXFT7vr0obeMIAVxYMGqgT4mCoWSizI79NkMGMXg6BAss0tm9spRiiKlzlJJNJyRv6DkxOPxsVoEqQz/ON7/d/rnuNUhKJ0KMAVHbD5WUmKZkp1vKb/+Ar2reYWCLu4ubwlH0jEDb2FOlRVlHYZNN0/LVw8gnfWYdTEuyj6XrnG3b+1y510WOu4bTkJCtHdtXpXIxKd47Ph8qFYeXevtCOg+97dooavqXxnmiCJ0oDmvDwAKYgsD94xaphS7Oy0yZrZIEc0IslakUTqM30zcCfyLw3W1iuK48LWbaEHwkTYmcM6IwFmGs4L0pDWsp1GrAg1Qzwq5iPAJmoKhiQ2orBs9+fjnyclS+f5O4mW+agqkGV96oRn2xOWdbzQe634MSs0x4SuXWUeWoLfwn/8EJV5N9zYDJkFO1U7QoVATVghZoopizixOuh0xKNniLn2HoGc3zs1I0ekfjPFoqGirjNBHEuoaU37CmTG3p5jbL48UWmuAYRptporP/rrOhuGChSovWlJ1RPGVIXO4muuAtmuNL7tO/jvjrb+yKXvjqyvNg5cleuCpCjmaBOk6FlCvPt5XLsdLnytlY2Q2V/+xOw9+/F5mqFcfFYI4iY7bIoVqUD9aOf/iw5eU2sfCOQSuNh4NoxDWDHu24nqpyPVWotmtxcGtt5EVReePgoii7VEiaqeJ5VgNjFbYJvvdq4vOdvfHtLDd+MWSe9Ykllfei8isHgZ9bNyTv2aqbJb4eXOD1aHNUrYUyp42VeUEoGIRbirmrNM4Unn7+PmbHDDpRfvkksmg8fa68yGaCt6frtM5QQdCZmAiDKhl7AldVNkV5NRXbURR7P37ha4d88M6KVJiRLaP8o5ZYkOfnea1mW7TwwVK/qnJ13d+GOtU52LZkYwkIQLXYwiZElIqmRD9OpuSsbwJt3XxH1zlaqoneuGdaKbmyGUb7OvuDtwyGUiviBCcVrRmhMI6Ji91AnD+nL7veimIRVTYjfL6rvBqtew1euBxs8FoHmfUrSi0G0ZZG2UwJLcqg5mn1eqxcJjtmoijvHwr/1d9a8GSb+dcXI8/GzMWodEE5isKYM+LsyVhml5B93mCdN/Y2yppf1maqLD14bBj0DnDK1aA8r56X2ZZqf95Xfvt85PmYjR7jhcMm8t2LiatUOA6VrzaVX1sK77fCw5VjEe0+9z6SK9xMiTGbRanWQpkH4DpDprnYhtoEWG8Wg8LcronjPCkPF4EMTDNx8att5FETuNMIj1rT1lxne1BUtZz4DIxYYtbF+OZp7hAO160RLGslYBGEzDp4rdWskph9vERYzBJgVbjeTDO0/MbHzEbAfaqx7XscNp+M00Q/VYbZYX/fVsn+f/vFpHO3dHCtyjDZ+55hjvGz/5arsaBjMKcZH2DKyVxlinUJX9aGvRXFMlZ4XSqLRvjaYeDOwvN8UzhpPN8+adkkoXXKmEz34GZqxOcC/+L5yHWa1XOtudqPRZlUEQ9nm8Lvvh65ybNHmFpa1HWqjLmiWpB9XgnzzsG52UPL2MgijjGbdesmW/8soqxaT/Se8wFG57gqhpwNCM9y5o9uei6T9diPOkOffuvFwKYo6wAnXnFVuelHTjpYRPDOzPOmYtRLGzurtV+zyZxiM5Mxn2e7VNk/fQ2tGgX+52cTf3A58bVV4G8vI7+8avj2KrL0cBiUOy2sxLZGN5P5De75+LkAABEQSURBVBgILKRqJETnPYhB7qkUfvzkkmFMNiN6E1alWmcb2jdGfghUB7GZI8mrY9en2/0OcOv9VbTMVJY3SBtq9rTT7D+8P1X3G/vbE0aVJkbERUQ95FnrI/PiUmyJLPOM0+eCDy0NjsZF8pgppdKXwmLmpP2s660oFifwcOF5tBA+CMJmNM3047Xwq0eBUZWkjknNeCIIPL0sPO8rxcH9Zo6xLrYY22V7Yl3slN/8dxueD4WjRmi8UU8+3xU+2WSSKkOySOlmJvOp2lDsvTc1ouzfJNO7THu/OjVzi7ZrqRLwMbITx1htHnq0alh3nk92E6KVDxr4teOGDZ5ffzbxo940/j8pgeviudyNnHawbATvIlWFVKw4Gudw4mYXmJnTJLPZuFp8QpgLeL/kdk64wPPDncVe/J3Dhl9dNSzcDM/lSlAzbzjSyldbYTXfKQ7lKpuUwQlm6+Q94oTnFxPnlwo5Ww6Lmhgvg702INdivD0n9hT3VvKbIVFqZczplg1gWZ4yc7+M6uKDIhIIfsEwpNvv6zaYUPUN+wChjdEeK7XS9wNTLvNjZu4WdE+TEXxo+Ohi5OlmwjtlyJmsdgLpl5bKWzLgm6rOyHHqhFVwlGqQ5tUu8407xgPb9HDkhIuktEAZKwtnvl9DEsZUOW4cV5PlpNz3ns3GPL8WXhiqbQOEOTsFZlNsR19mSnedeSBqr2XU+ek3G7RlNVFZEMeiMYhbfMOihaojZ9eW8d4h4ANn48Q2Z468527n+ONlw+/0jk+vPIvo2ARDs26mkXK9Y9W1DF7INVBLZsiZVdPggy0895Dp3M3c3nRFxSLB635sMVujswJHjZg5w2jmg9dZORuFJ1eJnJWfW0DjlE/HiqvwdCycpWL/XoUxGZyvEriZPB8/2ZD9IUMN9LnO3DFbSJZSZ4dNZ8vUEG3TnwvbCdQZElUx9M07MwxXhRoCP/dLv0BolvQVMo718SkudGjKILefnn29cxwcHvPw8bs45wzRk0j2C7JLM2pmIIJg/3Y4+Tm2umBACM7RLg5ZrI+4udlRvTm+/KzrrSgWEWhaz+tt5SdjJSKsUCQZjaEtlUcBXkdhNMCD91vHlJSNc/xoV3g3Ol5MFktXVFk1jmMnfG8oFIRtMvJkcJaAlap5SuUZxfHekBzmJ5h3zIO83WizEAXFURBElPWy42pyOOf/n/bO50euK6vjn3Pve/Wqqru6223Hzg/n5yhIhCFkIjSDYIQQsAgDQmI3C3b8ATNigYJGQmIJC8QaASt+bQibQSDBDD8WIJiBJJAo5IfjTDy2p9ttd3f9fu/dew+Lc6vdDM7QDml3jVRfqeTXz11+x1X33B/nfM/5MuhVdLuR67uHTJtIKTBqI13n+E6d2GlMM/Na8qytlZSlhYU9iaosUBVm7Rypa7plh5A8rdrZaRash0CK1nRisWWxhcZWlaiA6tHZy6MElL1oodNRVO40gWGybi3vJlgTz2M965qyowW7g21evx0ZFYqWmXNljBACUJ0/R5TErf0Z3cE65x5/gc7oHCpmR+mgwHRpnHNsDOZsb59HxCoe184/xfrlASHmxuiacL4AZ6tQp18yOb/F13dafuhin8KXvPCjn+Obe2vcnomlnbVAU2uyFx6ef/5Zzl9sOWzmtOEQ9QMGT36GydChWmRWv+TnCaOUuHJrxpWbI/YniYuPP8HPvPRL/OVrJmTkht/5yHG6FM4SFW5NE4d1tGSkh8tdzyUHm4VllyUpL1zs8PZBZD5J7KfEoHJM5pE2Ko/0HXutMmwTVSn0C8fVceBqHWkB8Vb70ikcsYmUYnyhcYh23zs0RQoxTlPHmVa9y7OeYLwo22fb1mywtsa+dig7BefXHZ/6wUe5ElrCmzdZQ/BScLOO7PiKv5h16BTCflHQzTLls2BqVBHBeY/EDuN6Tjc1dIoOILnTPKgIIYTMB1gkR/NmI4dRvbfBSqbtFKIMC8dr80ioW0RtIup4W8nviGOiJXtS4p98hok+wmg4zBWoVjmqOYHoXGCapry7P+fDacmd3dvcGRW4wSXj71WeslOCekLdkpoZjcDruy1PfDCk7CnXZg2drW3ipCWFCHikKij7JUWhuEK5MVbGc+Ub129waXPGzYNI6Ff01itUSlIQYhuI80ScJ67vjpnMA391/QPW3ZTu2jqzYpNis4/SITZKnCesVjeAD7Ti+No7c66OPuDzLzh+/HPP8k87bzOckjPa98ZSOEtS2OrCpUHJ7iiwUeUukUPLrN5qEyrC1RsBdY55MnHVYUqse8FHo1c/Ugn7UQgRknjemQYmMWf3W1OZWiwSVY4nljnj7iAPMEe/tKRXUIhZ0sAtMuh59u6VBfM2EJ2jW3mePN/l0qVNLr/0HOceG/DX/3CV6STg+1180eVWglgrbbQDZVJQyeI6aRFqBec71DFQEnDi8Qvt+rQQhbU8S1BIqcgdSbDEasT6pjqL6omYYNF/OuHFnvBpH3i7Tsy9Y/1Cj87DW7w3K/nnay2db82Yx+todJkfJrYlEYscFVWXlDx3Zp5X3hyxv1+jbo3OYAM00j9XUXY9qY1MDgJzVSZxjVfeHPO3V3fpbnSpZYuio5QhErTBFY7eVo/BhT5VDwoc9VS5PYXJpOK1qztsPLRJtVmy1itwZUlKjtkYpncaGgkczA5opMNI17lddygiVJvK4JxDVZiPAo1gn414UAfaMnHrXJn2GL0/5FNzZbDdQXuJne9xil8KZ/ECoYENER4pPNuV8O5hYj8nBtLcOoAMuhWTkOh7Gzg3ppFKhJ4Ih3krdaFjLXI85mRBc+ZZJHO0DMYyNr7QLC06iVi5r6X5chO6GPHOI5qOylwVKAurKtRK6HUcT1xYp1OUeFEef+ZRntoXRh/us6GOojWqucRIaE3NbJab5aWUFsV/IOBwqJTUbY1IxDl/LIKUqS/ZhpASKVmvsvWOo0Wp5xbCdaVF8cQLRc8T14Tx4ZSHLpT88A9ss7G9xrUhvHtDOZwJcRZxfmaRPp+1YFAQ22ZqmFjksLQkZlTFSUTimKqXWOspZVVSE5kxtTob9bQpUVcDKLt2popTYtMQY8B3uoiLpFgT5462TYwOa8YHLSmWxDYw3Nujt1nQ6/SJyVjK7bSmnc0J05owUWYHDqS0b1cEH+0Vm5Ywb4jTSDrePE8V9ZG2XONg1Oe93TEhBrM5xwPvhaVwFlVlr47cnAYulZ41Eet07pUGZdQoUghNbOl6IYlyp4006mgV9pNS9qBKluwiKRvloqsiVIW3stVo0bSN0hpd1MHm6ZhyMZSY5mLbJgrv6JYeJ54UorU5FSxqpmr6hUWXfr/Po1sFD1/o4gtFU8ksKLNqQO9iia8TayFSSqDSwGQyY29UM66FeVCaaMVsi5xDzE3g1HtCaI4awYU2HnXi12zrgisGFiEsxSJxKi6fXxJNEJroGBaep5/d4uGHKs5t9KgpqSqHyJzQjkkqBDuN5QhBHhqy2HQq4oSUAiEI7VRRHRHm0DZClC7ihWaamO23hFlCcRRV3gKHirZVZoeBehLQoKS5kFohTj1OknVmmTTUE0VTblg+VVLtaScFONsFNJOWetSg4W5ixD4SQWuB6InTDqmNtJOGOI+gBYt8DpJILjGLHmIXHfeseC4qGuqPHKdL4SwR+892S8etEAmtY5wPCu/PApcrz80mIZJo1aJac5Wj6EvpHLNa6RbWjGGvjXxrdNROhRgjm6UNcmKi9MosGrXDubttmAWLjkU1unNt4TIjM+bBOQ+R0nm6ZUEThXUnXN7ssNHzOC+0akpjrZZ0K8F7Resp5ysoQs04RToKwwKGc2U4N65WG/MBPhq3yxizQhsakvNHoU+wdSXl95TOI3mVtHp7JYk1eOgVwqDr2VzzbA8KtjY7nNvo0ev1SK1tr2JoSM3Bkeb8IouxKEeXfC7yAipKbO352uYaoMIhhzDeMRqNpNyry6jcBCeM9xKT/K/FaAlAUSWKEnZh5u7mXlJufWuDWgiaaFCm2S+cs9xXqoNJFRbOtqCYswTVox5phbM3WTWnTZpV4Wlioo2RIDC5KdR55U6ANs1HjtOlcBbBZLwrJ2x2PYetZnk3uN0kfuRiyfig5cOJkjTSKbJwKLm5tSY8jr15/sIgl53atOMyP2KWLBePYFIHzh1R6EOy2hOwM44Xo31IzozXOZm5KGt1zhkDQFu213qZcOmYtsLOMNCqdZEvXKLfFR7fEHz03Ggc124r3z4MzJKjjdYZMuYziVVE2gxv+RRPSlnFd+HYeds2D4lRHdkcFHRLyy9xzKUsgGfbR3FQlJ7Ce+sFFkwsNTZzUjvKuSM9CkUvchOl93igFMc0Gjm0dM6ETlG0zcxerAle4X3Wm3FHuRPbPt7t3H9cQnBxtdByFCwPlPJkEVLMhMtMMnaCFp4UjPQoyeceznnSxRzDcixGzNQFayBECimZtyGfARMBSNlZglq9y0dhKZwFhIvdgv06UqpwGJS+S2x3TVvw1f2aTe/p+0R0LjeLcEc10x4x4Z0EMzV6ic+ni563aNZ+m/KT4LCx+0mtLlxFjpJXSZVCbZZpQsRhEgh3C4MktwgyDvt65bm4VeJQogrDeWJ/CpEi5xsaumWfWRvYuT3nym5id6oUnZJeBLCCrYWUncWFxfI8+SBzxCLONuhi8CDcnkcGpaPECtxKZ+RLAVPdKhwbXcdW39PtFJkcCXUdabPEhgkFWaQv5RUsZiq9pIQ6Z5LlOSq4SEVJrqPXzNKyMmLrhIMzFeGkudgrj/ZFIhJyo/L8qSZNeYJYOJcJw5IJkLYNzty4uMiR2ZlyQUkyzvPiW5IjBzRq0uI7vpvQXPSxXpzpc/T9o0fp/1Xw8iAgIreACbB31rZ8Fy6wfDbByq77wf3a9KSqPnSvv1gKZwEQkW8e035ZCiyjTbCy637wSdq0FNywFVb4fsDKWVZY4YRYJmf5vbM24B5YRptgZdf94BOzaWnOLCussOxYppVlhRWWGmfuLCLyUpYAf09EXn7Az/5DEdkVkTeO3fvEJMs/pk2Pi8jfichbIvKmiHxpSezqisi/isjr2a7fXAa78nO8iLwqIl89VZsWZaBn8cIqea8AzwAd4HXguQf4/J8EXgTeOHbvt4GX8/XLwG/l6+eyfRXwdLbbn4JNjwAv5usB8E5+9lnbJcB6vi6BfwF+7Kztys/6VeBPgK+e5nd41ivLZ4H3VPV9VW2AP8OkwR8IVPUfgTvfdfsTkyz/mDbdVNV/z9cj4C1M7fms7VJVHecfy/zSs7ZLRC4DPw/8/rHbp2LTWTvLY8C1Yz/fUwb8AeN/SJYDxyXLH6itIvIU8BlsFj9zu/J25zVMbPdvVHUZ7Ppd4Ne4W33Badl01s5yrw4Byxqee6C2isg68OfAl1V1+L1+9R73TsUuVY2q+gKmQP1ZEfn0WdolIr8A7Krqv530Lfe4d2KbztpZ7lsG/AFgJ0uV8/+VLP+4EJESc5Q/VtVXlsWuBVT1APh74KUztusngF8UkQ+wLfxPi8gfnZZNZ+0s3wCeFZGnRaQDfBGTBj9LLCTL4X9Lln9RRCoReZoTSJZ/HIjRbv8AeEtVf2eJ7HpIRLbydQ/4WeC/ztIuVf11Vb2sqk9hY+frqvrLp2bTaUQn7jOS8QUs4nMF+MoDfvafAjcxoa5vA78CnAe+Bryb/9w+9vtfyXa+DfzcKdn0eWxr8B/Aa/n1hSWw63ng1WzXG8Bv5PtnatexZ/0Ud6Nhp2LTKoO/wgonxFlvw1ZY4fsGK2dZYYUTYuUsK6xwQqycZYUVToiVs6ywwgmxcpYVVjghVs6ywgonxMpZVljhhPhvYW1ars60vkcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(photo[:,::-1]) # Mirror image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2e57f6d7370>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(photo[100:300, 100:300])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2e57f72c850>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(photo[::2, ::2])  #half"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        [ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        [ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        ...,\n",
+       "        [-0.882  ,  0.9805 , -0.8115 , -0.5063 ],\n",
+       "        [-0.882  ,  0.9805 , -0.8115 , -0.5063 ],\n",
+       "        [-0.882  ,  0.9805 , -0.8115 , -0.5063 ]],\n",
+       "\n",
+       "       [[ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        [ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        [ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        ...,\n",
+       "        [-0.0796 ,  0.9805 ,  0.05304, -0.5063 ],\n",
+       "        [-0.0796 ,  0.9805 ,  0.05304, -0.5063 ],\n",
+       "        [-0.0796 ,  0.9805 ,  0.05304, -0.5063 ]],\n",
+       "\n",
+       "       [[ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        [ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        [ 0.4678 ,  0.9727 , -0.3713 , -0.5063 ],\n",
+       "        ...,\n",
+       "        [-0.882  ,  0.9805 , -0.8115 , -0.5063 ],\n",
+       "        [-0.882  ,  0.9805 , -0.8115 , -0.5063 ],\n",
+       "        [-0.882  ,  0.9805 , -0.8115 , -0.5063 ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-0.544  ,  0.9907 , -0.988  , -0.5063 ],\n",
+       "        [-0.544  ,  0.6504 , -0.404  , -0.5063 ],\n",
+       "        [-0.5366 , -0.2878 ,  0.5513 , -0.5063 ],\n",
+       "        ...,\n",
+       "        [ 0.271  , -0.906  ,  1.     , -0.5063 ],\n",
+       "        [-0.9917 ,  0.9565 ,  0.5293 , -0.5063 ],\n",
+       "        [ 0.5293 , -0.1323 ,  0.5293 , -0.5063 ]],\n",
+       "\n",
+       "       [[ 0.412  ,  0.9907 , -0.988  , -0.5063 ],\n",
+       "        [-0.544  ,  0.6504 , -0.404  , -0.5063 ],\n",
+       "        [ 0.4202 , -0.2878 ,  1.     , -0.5063 ],\n",
+       "        ...,\n",
+       "        [-0.9995 ,  0.5293 ,  0.2964 , -0.5063 ],\n",
+       "        [-0.2625 ,  0.5513 ,  0.745  , -0.5063 ],\n",
+       "        [-0.9995 , -0.6436 ,  0.0177 , -0.5063 ]],\n",
+       "\n",
+       "       [[ 0.657  ,  0.9907 , -0.6636 , -0.5063 ],\n",
+       "        [ 0.412  ,  0.6504 , -0.988  , -0.5063 ],\n",
+       "        [-0.5366 , -0.2878 ,  0.5513 , -0.5063 ],\n",
+       "        ...,\n",
+       "        [ 0.396  ,  0.271  ,  0.5293 , -0.5063 ],\n",
+       "        [ 0.6704 , -0.6636 ,  0.2964 , -0.5063 ],\n",
+       "        [-0.305  ,  0.2964 ,  0.851  , -0.5063 ]]], dtype=float16)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "photo\n",
+    "photo_sin = np.sin(photo)  #sine \n",
+    "photo_sin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "163029941\n",
+      "0\n",
+      "163.1336940294027\n",
+      "94.83104299577008\n",
+      "8992.926715665593\n",
+      "4\n",
+      "255\n",
+      "915732\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.sum(photo))    #sum of all the element in photo\n",
+    "print(np.prod(photo))   #product\n",
+    "print(np.mean(photo))   #mean\n",
+    "print(np.std(photo))    #standard deviation\n",
+    "print(np.var(photo))    #variance\n",
+    "print(np.min(photo))    # min value\n",
+    "print(np.max(photo))    #max value\n",
+    "print(np.argmin(photo)) #index value of min\n",
+    "print(np.argmax(photo)) #index value of max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True, False, False, False])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = np.array([1,2,3,4,5])\n",
+    "z < 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False,  True,  True])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z > 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4, 5])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z[z > 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "photo_masked = np.where(photo > 100,255,0) #if greater than 100 asign 255 else 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2e5067e1b20>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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98IK0+HNp68vSpnck33bJ96N0YLXk3WX6H94Dks8reb2SN1vyrZYWvCoVrpZ0QD6fV77ly6VFi0y9Q1ulstWSd4Pkq5DP65HPu0m+kuV6dewpcoAs0NPdkA75b5kPabS59gOgvf77IPz9lr8h7at1m/PQ513R8wORbxPybUP6Cmm/f3+XIN9fp5zAvopP8nrl83rl8/m08NtvtdPfd7rttn9owoSJ2rJli9555y2df37fFg3ioaa2LJK+pWH3ombJWJwIfAPMAb4uNFLoAnYBB4HTMNJY7K8fgfGy3RgHp3eDAatgfQUkx8EVw8D6ECp+hP5BED0KWA9ciHHLLQQcUDjKQ9GcnbR1QcdoUyc6BsZvzua1ddmEApezFsu/2cBHXyXE9V/4swOifXDIgrviYXoBvOcBdxvYmmbO+Kp4WADebXsp7LGT6Hwv9BkCw9zM+dDJ0MtGEjbkNth7JVz7JRzKh6BQOCODfy110LF4LWODizh9tJc2s8DthQv7UdPK9TBYBRC7uNoyB3A9cGc9TywUzg4CdQUrDXgcKDD74T+AE6xJkTRfADFh4ge4TCktBXmM65s7FtMLC4eyCuTIY81Hz9Bl2TzW+CxiOp/Fe2v2M/+zz5h43XUsWvQhS5cuxeGwyMrKYuHCVc10jkdHq4gblgLc1NZL/3Rx0/fm/xnAfRjPDWE8aVcCuZjbfD5wWinwJ2g7H1wvguMQOD4z+wx2Q3AicBbGBTcRmAkEA6dC5CSI/BZoA87eQBw4ekBwEfz8Gdh+0DxXJzAQCD4dWOWDc3zQr/LMs83vVcDje8C7x+z/JGAKlD0H25+HPgLeWQih0KEMdid0ouugInjHAxW5MBJ4qwLmLmJIW3h6FrR1Qp8IOFUQ29ZJ2196q56WBQwA3gIu8x9/EHALMJ4GP21W5SonMBHoAFwLLAbmO2BbDoQ8CG3S8e1x4dn5ET63CM44F8ea5RAZCkVbYaETrpwAFRGQMx8VbmX3q9Duqq5YA7eAJdgp2LMQekVBxSg41JsP3rqXgiXFXPXCmTii23PolR3896NDjO/8PTe/mce9l3oZkQRX/30Jb28sQ8DSqVMPn/8HH3xQ55qOJ61CWNYBG5nNngQHW4ESIAcjNMsw7+N9mCGDKRh1/HYndLgAyAPOhPiTgFDMaM5LwGTgVMCNaarcwCsYU1IXjCTMxDRbfTFOiV8C28D6J3S6FmNqyoYQF0YJjcFYmjYD24HTXFAaAvcXGQ+RL4B/AmFAEoQ/Cn36AL/k8PB7V1lwxhmm0rX94JYvwXfIOHFFQJ9UGLkWXtsG/bLgHoeL8HtOwhq0puZNE2aAZbL/GpL859eQLXKt/6b29ddJxzTdnYHFoCzBO+9h3ZMDy0Th4gTee2oXSwp8jG7zKpvK4KqrIXEN5MyFgtkv8+YBuKA9ZMyFO/bCb7bCgDhYWwrd3RCe7T/ekO/ZNSOF4fv38cOnPkrOeJfw06DiDZgUBUkD4HLBllfhy97woV9QWhutQljcDtjxXSzOHm3Z134hJ/WCp1zADnjlIGR1hkvSwHLA9AzItSBxC0bRqx1joC3ma9kT85I4gKUYwUnDDKcW+Ld7G8jGzKbai7HH/gHTY/7Kv/xrjPC9CmT5t5+NUV2ed0PfC4Dp8EgFPOCBYEHlex2EucOXYnTHH8ByxUKbCCAMom4BcsDzLHxUBnmQHwsjnTC6C1hZMCA+HsdlcXXdWnKBK4D9GNf8+mItlADzMEI1BaMVVbqczcQ01TcCN8BqRMnr2wl/H5wF0CO+hMuzfMT64LKDZiAz+yn4c1v4MR++/w4e3QuOLuDLhWgLdneAvith1y5QMMSUQ+evTAP03+I9fOExt3bcJrh2O0ypgOGJcMocmJoPhYKKRk1BJ5iGOjPHswzojLS/i1R4qulwHvJ3PD9D2oL0BdIOpBVIO6np1uFDKq+1zOsvOUiFSHJJSpUq2kvlTmlhnPSkJWU5JQ2UcqOkovHSoQTJ21HSRaa+EqSyHmY/Psu4lOzw77MQyRMrFSdKe8OlFYnSt8FScZAky5xPGVKJ//zmI707Tto2W9o/X6oolbRE0g/SwSulyUjXoPKTUcU4pF3Iez4qczjk+0WwlOe//vlIB5GeQGrn79S/QP2uLnORQpBc/npp/m3LkYqQSv336Ea014kWgs4HXQvKBh2KRu8GoU7+jm8S6IkQNDUU/WcIOhuUPw75uqDCcFT6B6QRaOvv0D9C0a9A+aDX26EQq2YnOtT/NyQYhVrN20E/1tLkDv5xIRrw7oK9e40qFYXRq9Mw/4/GXMYmjJdt+1rbL8HoapUd4B/8ddyYL7sEVims88H3ggsjIcsLuwogsQu4QiDkR9johXaHoM0ByB8I5d9BQjzER5mdbcuDrSVm/+da4LgSvkuBhDL4vhwinDBwLLAQtAbe3Ym2zIFUsEKA8dfB1l0wdyr8bBEq/xn4HgHvJXDbGzBHuCLBkwdb50HWfviNz8fN/y7nqrlgHQJOx3yeu2NaxmswI1vp/nXV6Quch5lnDKaF+QYKXoGIZ8CqnBi4IYKwuwrRc3B2njG2fAW080J7H7zcCR4uhfC98EOZ0eJuXAFtOkJeL4gsBPdgoKQNpHhJS4vm+puy2bPRxYKloVy7O4vaw4CVg6xlP6EomK1DWDYD5wgyi+ExjIttMUafLsYIiuWE04IxugWmz5GPEZA21LQUZWCEbRHGZfeXXuiRbVSzLsDKHaYjngbwthFOQqDvOEzHIx8KhkHiMIyJYSbwEaSNAucaY83Zt4fypUtxnRaNI3IADB0Ioaehgo8p/PRLdNpZaHMmr/0LUrJgQkdwdAPF9WfP8hjaXeRg4Z2RnHJtPJsRU66E4lIIcUD+XljxDrQPc7OeIrb4YM9m+GUwaDNEbfYbJbzAzRhh+Z66whKNEaYPMSrmAeAi2OWBpL3GBh9+71jCZl5E9HNTOPWcLFLKgjh9HvTo60RjSnG0Bcf+UIacF0r2oxYVg803qO1Gi/a/6gy7Q+EeJ+w+BN1eBmsjlms+7l2FdFUES99cRPDUusLyU6R1CEs74JNTYOZSGF9uFOSHga7+9VcA7o7gjITi78369zB9g99h9PauGDnaC3SxoEJGIFIwlp9wjBy4MX2QzzHC+JEFsW0wb1Iu0BvYhuIeQLkhUBSNFXkQq6AcXt4HXRPhyn3wTAG5L39HyUrY+cOX5JZarO6cSIinlLfn5ZF66hyeDBXxe8BrAbv9h+jWgdhrHsMbEkv7XzyIK+0kOmk/E0ZGs+blPJI7BpGXEYVj7SH+PH4iV7/1Ftu8Xt4CNpZD+PfwEBD5Pmz/DA4IepeDKxvzAantwOSp9X+5+WawCEoG9Mfq9iLekGKckx+B20tJs9qyfqUTZ0Yk+Zs3EN7RwpnfjZC23ejwYrWB1ArMR6yz36oQV4oUhcfTB3wXYLW3KC0t4l9bbqaIFU17L1oZrUNY3KDoBcYMGuk36EwFCIaIaDOgUB4NOT+YM27jgstjIbcQkiogOdZYej4OB3cueLtB52F4ls5n7+tr6eDwQK/2kHeuUV/+sgbaxkByB3A7gZHgm0Lh8lLc6U9BVAJUfMRzV95FTmEBo28aQsq33+LKKqPNwVyCR/wKq80ekrPexDs8jdjwZHJLs4mMySDjkmvZ99ybbPnkXZYV+ngWeOW6eBzdu0O4A4sdhGX+BU/6x6ycMZP216RRGFzEWaNHMG73ZlbeOYo7i75i1+fl/EAs0XFdCNm/kQeBszGyUGnwcpQYq/iZwNVPYFSuQdS0iG2hwblcizZt5o6R40hPT+eUU04hKiODMb0G0q1fAjjB0XEEhIIioNiy8EpEAsuLiujrduPEaLhZwLdzFrFjxSo+/jiSkpL/kJgYzdatW9mwcWOzvSYnGks68Ua6gb1DNX1MGY4lEHfLRKLOWgJLwuBrJwztBCM9MMcHi7+Bsz2wMxk6nwt9f4Cl++CMB8G1ArJegbg4WBAKW0ei7avxxKYQNGkELD4IY0ZQEfQ+zq+34FjWBX5/MSUVuyj/7nn2fLwId/4hlBONlTaY6O5BFJS6eXxFIdPenUkvj7EWn+yE+391CsN/PZ65Y56ia4VFdIabafuz2dprBA/+/ByiOncnZ+8yXrz3Mf78xUEmXzeEqYOvIK5gO+EX38quxx6m7YOPERK6Hlzt8eT6KP3uGVyOofygPczK3MrGslx2zdvCgg8W4PFVMAzTjwiGKonxmi5cMf559t2An2EsesJYyX4NNT7sEcAfgYfggzwzflWJIziYaHd37p7yX1KuSGTJ3/5Kl7Q0IiMjie7alX8++yyxxRar91Vw7YWXsWpxNgPPi6bfxVei7bt54d6bmTnzc3y+n1BHpB4k1TtS1UqEpYOW3XkW5UN/IKesHynuCGg3G/JHAV0gbj5YfWHVa/huX8fBpRAXDNZ7bhjVC75tCwNOBTpD6CDYMxMWZ0GPROh5PljtMEP3B9i3+GNiwuYQ0m4is1/8jhn/fp3v8grZmguDIuFOp4Peo5xsiBpDUdBavpq/l8d+LK1h98/oEMJIp4eUQi8X9IBF++G69UYLGpESyYRLO+FK8rLszbW8scpoLInAuEvP5YHHp/H888/zm9/8hqeffpq0tDS6pqbyxOSzYJ+T/aERRGRksC0nmLiETcybt52eEh/2hS57/APsF2B64XswLg7VSQNux3gtvEXdacdDgfeBO+CDN2sKSyU9Y2O5snswy5ZnsdoD18TA0rJwlhcXU4ZpxN3uZHr1Gs8ZZ7Tl+l9P5c//nsmjt4zl9l9OZvr06U1/GVoBDQnLCTcbS2LAgM5SwSRp/++l/W9IBwZL+qWkyZL6S7pOUlvpDbfK3WhuAspyIl0WI5UtlXa9LRXvNPNdVC75dkreCpmZiQekgpelXf+Wyl+TKipUvPItPXV6O/UPRRd1QMnRyOFAHWPR4g9/q+3T/62RneP1n47hehjUzo0uvDBECQk1IxeeH4/+ORSdFVnT9Gj5C7XKueeeq1WrVqlDhw7atWuXunXrpquvvlrz5s2Tw2EdrudwOGRZ1uFlbUAbLL/5F4x3caUfGPUUq4Hl0UgvIm1F6oHer3V+0WZ4RIMGojsvRP/9eSetnNxeu+5O0Y3haNcp6PGh5triQb0S2ui6yXdrwYbduuXnt+vnky5RSkrKCTf9Hmtp3abjskIIO9l4/roSMErFuRgzVTamVRgOFw4kqPMGzkgYCRvCIc0BH3wMv3sIzu4EncLg7hvBNQoyp8KqTexevoW3v1pLt/UOTv9vX3KC5/DrS98nqs9BJqfDHzbAwTIIDoYOPVLZtq6QriPKuWCclx2rinlgB5QXQ9dgN5POLuSqN8oPtzIf5cJH9cz/bKytjoqKYvTo0URERDB58mR69Qox21TbqDJyfuWy/cAlMh3zC4Gzvf4xytqtSmMnYGFG+vsBqUAPjOuEnxSMTeUOYPUaKN8I38Zn0S/DzeQwJ7k+IA0ivjPW/Apgd85+Pvt8DsUOLwtee5b2qRUUZRv74dpG7sFPldYhLCHJ4LwF42/ixQy5h2IEpQcwBDgDgnfA4DFAHHTJB4qh3T8h5Wz4ei/EJ4IjE/bcDzPmwB17aRMGN3YESryELV3Ky8uXMr84DC0IYX5kGfl+a1FCAvzynF6Mv/ZcFv72Lt6ZnkeSx3QPJPjqmwPsch/bZQoRFBxEp06djGm7IxRuOEjUyUeexPa9v8zy352jjtgijLSVYPTFLTVXh2C8gXIBSuH7UrDyi/ElhBFTXE5qAkxf15mHC/Io4UClAZ+83Ut46wUz7L5rCwxtDyF7+V8IEFOH1iEsbMa0JJWjjnH+UgbkwoEPwPMeuL6FmAgo9MKcAuh9GYR0hEHpkOqGnGwoz4VH3oBnAB8EV0DwljCWh0HQeyXcuA7KIi1WFVusy4c9/q/wnj3wq0c+x3NwGW9+uJ9NHtgQFkaZf/bd8kxYfoxXuSBvAeOuGceGbzbQsWNHXnrgJeJitnGZ4ywab48MfTFW77SmnsDHGAtBJ+DA4RErwMwb2lbt/xTgsu7wz+W55HSEfVkw2rWD68eezsocB3O/+LrG7M1KFmUGHgHzJ8eJ7q9IYkBvjGvLZ0gLkIoukEo+l9acIc3pL6d7MIEAACAASURBVD0SI/UIkbphZgqeiTxByBeNFIN0HlJmb+nQCGlpmnTZQOmpc6UbrpMvJkYVI9qp6JnT5bkMyY3ebYNOi0Cf9EXvXN1N1w+J0n9+fb0mnRmhkaAUJ3p1Etr81ys0smNowLquC3RrOhqT1EAdF8JhfqekpCgiIkKAgoMD2/+51J2P7wUdAs0G5dTXT6ks/ZFmIRWYfku+hc5s5FgO0Dlnmd9OTHT+LsEoOTpcI4cMVpK/Xn9QKiioGfsM8f5jBlo/FdTzOPRZTrigSKJfECr1P1RfCNp/crTyL21rBCES6fZQKcMlgbJAeaBvQWswk56yHOizVFR8YbK07Wpp9W3Smr9J3s/k++ZZbe3YXt5ItBE0HzTSf1N6g94/r5d8P06Xb8Nqrf3VBPVzODTG//A7RaDIyMZvbL/oqt/tHOjHXuiqFvJ1coIuAd1TrdwK6ooR1DNBz4LKqgtJCNKFSNkc9lfz/AHdYNVvhKivnAR60I1eHIwmx6JTMNOZI/3HvhD0N1BIM12nm7oT/o5nad3CAtrjf/FXgh7yC0MuyAOahXHsE2gX6E7QVEyEk9swX5Ug0I1uVPZKivRBvPREV0mPSN4tKv5DL70NynCiYUHoj5gH/jvQ7VERWnFZV61/7zcq3LpYl3fuoLNi0cR+bo0aEnvEF6ptArr1AnRlB9TBjVZMRMOST9yDPh3/h8eB1MO0Iirz3+oKpEy0dJx50QPbp2lZz4lC0zPQ710oNcisGwTqhnG+PKnWdl2acO7tQemgcZgWy+1fHsnRtTT/08LSEzTKLxTLMV+pdNAEUAHmi3kXqAIjPJ92QFfEohEuFFXtKx4OumxEO111WRe9OCZanq3/kMrnSx+O16YQNCgcPRJrvlrhfoFxgxLC0dCB7fTyVRM1pW8nxbtQaoRD53V3KRgUEoKiouq/scFO9NV9aNFo9EY/dFZU01WSFNCQY3jIcaCZIF8HpNepmjLsQ1qFvNehhW3Q4KNq+SYJYoX/XqXH99Sg02/SSX0GKDQ4TMEYc7qzGV7oEIwKNg6UQFVLVV1w/t8LS3+/MCwBvYz5Kj0DysSoFBNAd2NUMIF88cj3G+S9Ez3by6hVSaAx/r9xoDTQ/EnItzRYm3+bqFejzcv4c1DsUd68oCAUFtbw+i7BaHjosT+kONBkUGKA9R0YN/jr/NeUhGmh1btaa+JD+hx5E9EboCj/trGgiCaep8OdopSOEzRs9M817JJ7lHbyAPVPStK1oJMb2CbFf75H2ncsKAYT38zlX3bKMZzr/5ywDIhDnklofgJ60TLzKpZjBGUKphmeiWllVFkSkScCbTwtSe9e5tTkMLPNdtCPmP6M52Q0+wb0t16heuaOdkqKDuyBncjyGGgz5uNxJKEOAv2AaW3XYNShx0C+npj5KsLM6xmJFmEExR0crIEdOuh20PhmOF9XWFulDhinXje9qNPG/lr9ktsr3f/MOvlf+naYD9mRAvxBwwO6x7O0bmEZEC1VPKCS+X00u0u4tltoK+hBqnTrCEwL8iloN1XRTAoTQvRWsnkQ32CinZT61xeCzjqGm3YiBOsMzMvvxUSqaUy1SaemBSwHYxXzuZCuQHoV6QGkdqYFqnwZg0F3YIQL0KBBA5WcnHRs526FKjrxJA05+w5dPvFX+lO7dnrCsvQApsU8nn2O/3FhSZL0nORbo6Jdr2vf1ASVhFia5HYr2om6JxqrTxrma/trjEq2GTQN9E6aMR++i+m8/xe0H2M5aqpVJZVjE7RAi8PhVNvkgSK1/+Hj7gHtw5iEr2hgu2RM63M4BNIRyrW1tj8bI5iA2rWLVXp6ZLNdU3Sbrrrpzhf0+qVX62vL0oug+zDq9amg82jdLXxD72nrcKQcaGnZsjiMo8QZUPIu+sMmcpJj2VYQRNTBvaRPgz9XwOuYKRrhQCZmav11TvjEC7di4tANxvgSPotZ1hQqZ260yEC0ZREZE0tU73Po0XYQuZtWsPKHj8B7CAdmqs5KjOt9jP9vXrXN22H8KDsQ+KjyjZh5cIdPwV9aKpitwxlP8JABjBmQwaGXX6JbwSE+wlxPJ4yf5/YWOvaxotbtSBkkaYakX0oF6Spddqpy3r5I0waH6+lfhGlcKNqfgh7F2PWpVYIxndvzMGFOszDqyHBQH/9X7ETrwZUl3O3WqCuv06Rbliih0w0iOK5OnWsxlsFTMC3kdbXWj6HWWEr14sTMua/lTPlpE+5BCMYkfCzpwt0JvdW+/Q0y8ZFMp70+a2EE6LRW8HyMSLRmNax/lLT0FMn7plR4pcpW9FXBGodyPkeeS9A6zPjKlRhrVmNjBAmgd2PQmXFGLy/EqBvDQZeDup/Ah9Dt5N6aMPlNtR9+t3B2brBeO9AmjEp2LlXqUmW5bWgneVPD6xeW+zBR9O9D6srhYBVzadoHw40R2mH+82natVsB1UuhfiPAAGzT8eHSPwN5rkDKS5UWn6mcaQ49PRzdHI9ud6CfYfRdFxx2s2isBGNak7GY1uh6UC+Mvf5F0E1NfHGaWkJDw3TqFb9QpwlPKyjyFoGr0foW6B+YkfH61o8Y0U2l0/4ghVh1heUCjLnYixln8QcR/4amdbKd/vuegBHaEzGyHnKMz2sgpjWuvTw1EfWsx+TfqoUlPght+A3aMAJ95zYtQHtaxraeQeDjGI2+RE5LTqdTjiMM8MWl99TE3z6jlG63CSvwSO+pjZxnt1SXinfOkH5+keSmpsr1AFVhkbKQOpjlRZj7WrmPYKrGXBorsRjV99ZjvF9BVIVUaqzU1/GPArU9hmOHU3/LNHksujkhVlf414+PNVpLqxYWQI+MQicH/TRMjN27R2ru3Oe0dOk8vfe3X9dItFOjuLsq8vIPFRzSsMrVlNLNQsVfzpIqcqXVvaW/It2KdA81A4I/UbPVWQ/qTJUQ3IBpbc8JP/KXuynWq/Bqv1MwVsoj1X84teb4UhhGdU7HtG7NeR8tC50WG6sOGGE+NdYcu8nCgplYsgQznWItcL9/eRxmAsom/9/Yatv8HuN3vwEYE4iwDGziAzmexel06uabb9bChfPl85n8kD6fT7NfeEH9EhOr1XWJtgMF1wn6N/t5pIAyZ82SyU2zUFLH+m/tS5gOvxspAvmC0eOgtv68L2GgYQ6U+wy6pH3znqMDo0aGYgQxFGPqPtJ2sWH1fzA7H8f341iExQIi/L+DgO8ws7GaLQHrsVzY0eqyEVS5URxtGTlypEpKSlQbn8+nBbNmye12m7qxY4X7lGrbdlagndxAy6xZsyqPLmmRTATNWre2AOk1TFTPbUhvo60nt9dtV44XmDGc6y301CTUP7r5zs2JmaLcHeNsWdmanR/AtiNGmCneR3vMZJrPTb/JwlLrpQ7HxAoZTDNm/mrqRY3s3FnTb7xRvbp3D3ibKJomLMGWpZkvvNBg/pLy8nJdeumlIrK/6NG72rZBgo5HdazKr3FjdWbNeKq6uEpa3HALU1m2IH39R23+doZOc1iKttB5oc2v+rbF+POBUW+S/b8rr6kXRpjq29Zq4vSGcFCHJmwXE4Pi4gITloBySlqW5bQsaxVmnu+Xkr4D2spkMsb/N9FfvR0mPnslmf5lzc4Vv/0tFz37LHdXS0twJPKpG3cuEFxOJ3H587B2/BtUew8+glwVXDXyTCjIgXXVZ6BXYEL3N0wQNWcX1k44XC8fVk/MYmESQr9Fg/MoK4ClfWDwL+jUdTCnpXYk0gFto5t3YNICLsfEZwez7wT/b2GmQ//baYL/14f5dh49xZiXzoHJ+NErwO3y8uDAgcDqBiQsMmFS+mIiCJ9iWVZGI9UDSsBqWdYNlmUtsyxrWWCnWg8hJthDcHAzpq9rgGCPh9Qpr6Fr/gwlxTVXZm2Guwfw+ZTfYZI5NzzuH4LpBFZSmf2uelaouVQlbmqIvDqhuSxMg/8WJqpmtcfgAV6MgKj7Ibw9VkICYSNGkOmFF7PqeTjHQAfgt8mw3n+RXmpOV+4dCn3HmNRxzU17TKqbacDLJ0FMWPPuP+BsxQCS8jDP8hwgy7KsZAD/32x/tUzMPaukPSbCVe19Hc5W3ITzBqCkpIQ5X31FacFRZC0+BrxlMKvfEAir+RS0v5ySJ7aSn9vweTgwsbufwUSjrXyVYzDRZfdVq1vEkV/gj3fthOK8WksrW5i5mDwXlhGUv1vg/RmMOQ8sC8vpJCEjo0XmygcB7INN1Sbot8XE/wsFHj0T9mab6ArNTVdMZ3o24MyGxAoTrTeomfZ/RGGxLKuNZVkx/t9hmFxa66lKwAp1E7BeZllWiGVZnQggAWtTmTFjBuTkYG3e3Kz7dWC+0VG1llmA4+AK8NYM1bAHuA2TeKmh/VXmGyrBpEqJ9q9LwMSQOFofNB/7MUGSamNhkrVMA26AtyNh01Vw9SMmC6ufswcNIjw0tJ7tj50DqikMIZioTT0d0DMeHlxjgmU2N3MxPnXlFmw4CHs8MBYTsbeSY+kPBOKHlwy8almWE/Pcp0uaaVnWImC6ZVmTMbrHxQCS1lqWNR34EfNdu1lSi/gjhpSFMOLSS/k0MhLX44/j8XgwcUkicLkgLa3G+xH4fjGq0W+pCuh4BpDkgPbDBtfJWhzfpg3ZgwaxcdEi8NXsATgwLclazIPciNGt8/3rB2OCrmw9ynMs2rwXT7aFK62hGtHAszDmUpg4ENw1gycFp6ZiuVomuM8yTAz0Sq7AZMcoDYH1OfB2qQmq2RLMBSKi4GdnwZVZ8K9va64fh3kGO11QFgx7jqTvViOQBKw/UC2LYrXluTRTAtb09HSeffbZQKtXbReTjmVZjB49mtmzZ/vTO58ExBEcDP36NZpBu0EsTNM9nyp1yAkEWUCwCxw1G/aQxESuuv12Zn33HfL5anSYM8Lg1BjYuNc0v//F5D6trPMjxoP4aNWSb/YcYu/X8+gwuWsjtRyQcGa9ayJwkkQQ9bXJbfznk1/PukCo3u4GYb7sXYBZJfD6LJN4ralqWAzGVlHUwPoiYHo+3DIK+pViMvVW4yXMs+wZBbckwvXrAz92q4gbFhkZyahR9cpdQISEhDB8+PBmPCPD0cjZpk0HCfN4cFL1IEOAR38OH31oWpD7MPp0dVNhU60bB4DZFXu5RmpShuH4+BR6njSYzcs+r7MuHaPgNUVYHJh0MJWEYj4MlbHJ9/nMB2hN7Q0DpBCjO7ioG8a5EgkeucP0FWrj85fVB+CfB6r6joEYOZqgpNjURhKffz6Tg9T84p3phgFbYY7fvBHE0Qlgo8cEls/8vHKc6uiJcNC7e13bSjzG+FypRlmYaK+NEYSZc9MZGEPNj0ERJtv4TkyLUPkCN1VYYv3H6eA/t4711AkGVpfCtvqiAPqpDMoZT/3pOOvDFpZmID8/n9zcLXWW7y6Gp2bViZTabKzIyUXehr6vjWNZFoMn9K9jEXNiBCUH6BgEozuGUxzjJiYmisiQ+luwSqX0ZIwBo3pGFh9G7boC/0vcpLOtIhdjHFiH+WBkYjrVUZgk0WAU8bupaaCpTrL/bwimpQoJ8Ni2sDQDmZmZbK7HIrdacL/PqGD9W+C4WrMFbc5s8vaD47uTHFzzu1r9hRjmgY9PHc1H0y5i5dd/5PWpI3BUk5d0TF+kK0Zg/pIAW+sZ5HRgrD/NofP7MC1UpcXIi+n//MqCpzub8/gBeJ6qxMy1qTS4H8L0r2ob4BuiVfRZfjJINXVbbwHsOcC6xYvxehs2+C3HZBtvzsE/gLXlYqunkG5N3D5sQCpRbduyZ1dVOP7hVL0UPsGPM75k49woVoS+TlCBmxtlgpNXYITgXEzCtv7AZzn1R8+fiAnv3lJ0BD4TvLaVw7kr3dQcDK2OFyNUlXVLGqhXm1YhLN68PA69//6RK7bBPKEMcLsH43JVt5ofwnSXhddbSFGRD6fTSXh4AsXFWeABJ+H4coPxRh4iNCaMkq0lKF9Gye6A8aPeixkUqJUxdFV5OTkffsjHxcVVZtGy3bBkI+uKyg4Lixvj0lF7kPE2zNh6VoD3ZDjGStYYxR4PpXM/gZOHBrjXmrjD3Yw99SzWv13VFR6CMXEDFLvg5pJievmTGP2RQ5yPMaOfgbnWUMDRCRw7TDLobOpyir9uS7ES08JVH/neRsNuPMGYD1dHjGu8hXmtjjS+0SqEZe2WLWRceOGRK4YCFljxMGz4AJKTz+DO395JckoyO9/9hGUr3qPN2Hg2vfUhn83cT3nfdtz7h/G8fO+HeLKziUpxsXCZA7WN4mc3XsNHb77LmpU7jOIagVFeizHteq07nQP1Ro2vJAIT5X4nJs/jrf7flRRivsiBEkjEfq9gWWYFJx/FfqtjBVlMyhjKk28/ixMf12AcZSqFJSIebsuDxDJI7AgVe+AbDxyIgR87w64KGL4eYnZCtM+4dTxO3Q/Cc5jb2pJZKGr3C/c2UrfQ/7dScQ6hKhNQoxyN13FLFZro3RrsDNbqj1ZLkm656SY5QE6nmfdQOffB6XQe/r/So9WyULt2bkVFORrcd2MRKOsriZhMWkGY8LN3E9gU6GMtd911V71e0IGyetkPGucKUyzoM9CKTmhyqPHM7oiJfSCQhmAyh12OtAF5K1BFMfKuQLkLUFEGerMvGtxAIPXWEjAkkHJMXsetFZ887M40yooT0xh4veZvL0xaeK/Xe9i2XmlldQCXDOxFWVn9XkO9e/fmlRceJyS48YY3LAhCq1UZiHFr+RfGv6cLx6Hp3rwQKhpr8xqne3R3bovuSSrmfE/uBX3dxny8EzNRyQOwGKPpeoBUcLjAFQaOfhB3CMI3wYj1sL+B0UJhnlEiZmCxXpwu6HMppP+S5vPoaj5ahRrWVDw+H/O2fM/ZhVu5dPhw3psxgz1799KhfQSrd+YT4Yb2Dsj1gD8nETExxi37HzOq3NWio6GgAIKCYFzvOC65pScH1mRyZu9wRl48Dqz6bVkju8DW1Ru46r6XAGO6/A1wCTAa4yzXlOkAR8XG3VDhafK75ezkJHlAAv1mmZfZZZldxWHUlBmYqbCHO+jvY6wV1d2G/YMdr5c27LYzGNPPuT4arnXBgtx6KvncUHI/ZH/QtItpaU60CtZUNSwpKUl33323Vi+bpz0bZisnJ1vfLP1G3Xu4dfeQWDlB3bqg3u2R2121XXx8LfXAQhdckKrOnYPUowfa9d1pysu+Wqf3dWrB5SOl1V9LKpeZpbBH0keSSiUVS5K++ewzhfjVsP2YZEMjMEEeptD0WZmBlrs6dZIKCpqshvm8Pm2+/UndhYkAs+9c9HK8SfuBX316tFIVqyxXYdJXVD7CtUghdaNehmLU0vMwqTBCQQ+D0huc4OUUXCjC2gtSBT1tNayp9IkDtwtSUlKYM2cODz30EBkDhhCemML7b/6Jmd+8RRJFZAw8yFALNm2BHzKhqJpqkOv/oiUkRDB+fBISfPDBTgoLKxgY246w9ufy8ms+ftzoJWR0CvT8F/AApov/BUY5eZdKZ4tCjD3gEKZjHIpJYroH08lM8h83NTWVoKAWUC2UDb51R67XAJbDYkc3B8HAq1QZJcY7TcdXGDfyrRgjoQ9Mc7PUvxJgL+T7qgwDlURjWtvTMfPLExzgSYCtogG8EOWFES9Az5FgJdCahgJbz5kEQGYpxPogpKyMRIcDhyVycz/h8UdeYNfj/+WJe56hOAL+swR+aPCBGPLyCvn66yoDb94BKAzbz8RL7+MPU1/j2kug79jN4JuAGRMWRqufggmw6p8LKDOkVYYRDgujoVwLLMSMQwDs3LmTiopa9uhGOBXT/zkimUVo2RHtOI3SPbIHXkconfBbzEMgclyVuXeR/1z6A3cBOQWg32F0zArgSSitqDtpKR0jaEEYr+MhabA47AiqqXMedHJAyGMQdg7GIaWVcKJVsKNVwxygsUOHKnfNGqngR710+SiFhDjkAIWGopkz0YtTUWKAeRqr7zfBYYIlJCWivh3Q9y+lSuW3STpTUm9JPSVlSPpUZt679ORtlx/ex6uxmEgqmLQX8ZhIjkdzHpXlfYx16kjz408BFc+c2WQ1TJIyd2ZqYHS0HgS9HIdejkCep9ENDdzDgaBtHZD2YFJbdEUzqArLGgG6HbQB9BQmikw46N8TUII/qF0QNUMlHS5RUeLBpSLhFcE5IvwskdBGhNcNc9tS5Sevhrksi8iICDJ6J3HB2BHEuV+Eggn0v3gnd90VQ+euoZSXwxtvQFj3wUQmV11aIBfpA3J8ZjrKvmxwp0bQ5dIwCPoCvCuhzAe56zGO9mOp9FddkFU1IOMtgFdKYX8X45Z+CXU8xAMmC2M5OpI/8X6Offwi1hFCCHG8C+w9ABWF4FgLZzTgYbgM+CgTYyHbCMoyI/eV7WYy8EeMT9hUzAj5WcFQuhhy/Ia7IZhU4nUsTPkFsGc+fHkOjHoaUq+BfhdD8t+o8uo6MbRqYbEsi9TUVEaMGMH0xx9nxXff8dJv7qHbgofxLPknm/PLaDfsQi6+6HwG9G9LVBR8OBvWrl9HcgfzEgcHQ1po4M5yAJGRLu6++0nCw28ENlC+MI/i13dC3kCMX60ZApPHg6p1iJweyPDCd9vB6gQ3Blc59x0tH3AcLGl+wiPjSekygHLM4OFaILcNnJ5Rv1cvQI5As4BsKCuomiYLVQO4c6hy8+/gg4PVZl1nYub21BV0wcyXYJUb7moHP0uCeUWw5XoaH2pseVqd6djlctGvXz9OPvlkJk6cyJAhQ4iOiiJo3z6sadPIXvYkbcbDwb4wbsxOBvT8Kw+d7+I3p1YQ5YUvF8GLL+aTkABjxkBmpintSxv3/g0JgbIyCLXg2Sk+xo+fj1XxGZu/hKiPHYRMSIXEh8DzDrhMbJLc7GyWL1xYYz+xwC8s6DAWDs4A3+6m3YdvMQHajoSXOp45R0+URZcuFotWmIAJK4GKOdCuM9yE6aXVfqk/Ae6V6Y9swASKq6QA06rWGMn3QN9q0r+Nhn232LsPtuyG78phzhZI6QLbz8fMKW2pJBlHpnW0LFYE3UeM5YnnnuM/b73Fp199xXMvvcSY884juk0bkKh480HKVy4ndsJ5VJw+jKiuw3j4L304aVMQe6K7MnVOBKeMT6akOIiDecHsCO7LkGHD2LqzI8UhiexMTCQoKgpHUBDOoCCC/MUVFIQ7MYgBo8z/I8e0o+/g4eQUjKFUV/PNO+FcvDSN11f2pXxnIuWZQ/GRjA8o9vnIrTaNOAsz9yPOA395FvKbKChgvvALOfJcixxgQzO8QMPTT2U35oMSBYRlgTUcbqF+Q8Nhr98seJmarkAejBt9dT9sH0fRLpQfgHdegORkyN0CO+pzzzz+tI6WpUd3cj+dyfOhFlhW3fnIISFw9zS4C9pYUGoZ57zyK4rJHLydT5PaYZ2ezTMHw8n45hBtAVenTvhCw0i6JJvBIWU4gILyctaXlRGLcSsHM3fjmyAICoFxhZAQG8s/UpLpgMVG70V8fMN1FK4NJ+uQk5dnpFBCBqdbFhHAmrVzyM+vmk+4EvNC3AVchznHysQ9TeGtAOq4gTZ8BJzXxKMY2vRMJ8L/OxGI7gmcA2GjYeyXZop1fRz6rKYK1hBvA28czQmVfw9XRUP+JPjgIzhQAXkJBODB1WK0DmEJg5wwBzkNrbf8eaqq9XYXAzgioJsJYRYcGUt5ClRGYYrE/7VLT2I3VUHrKjAZp9ZRNWsxCOO4WGaOggv/9FOXi/LBJ0EibHge+BzYDGtLwBhOfqBSLWiDeYz9gXuBvwD3YwISbaBqSKK5KQSyN+fR5Rj3kxLThijcZFNkxloigbZg/RFS55j+WJ3+xQFYsepIIQQPVz06ti2EB76DYcNg/xCIzoJnvgRfCrDqaPfWLLQOYanAvE3HEMiqLTWns/bDuMYn+XcfgRnz+L/2zjxMqvLK/5+3qnpfWZpu9lUUUAFZBFQCEkUjwbhkHWNmnCTqOCaOGaOOWYxGTeIYzR6N2xhNNImJG2gERBQXUAQRUTZZmoZuGhrovaur6vz+OPf2requ5VZ1V3f9nqe/z1NPV1Xfqnrvve9537N8zzkrUGGZjBqfw1ChOQKsRCMqS9FJ2Aw81QQ7jwAXo+U5nkTdODSi0qP4PrAAuAb4b+vw81DRnYQWpkgHWoC6aNSRJFFaMZmcoiF81LBbI0hj0QszHuYMh9y9kSnTgwBTDaHqdC0EzQxu/CtHf3UKodIiPFXNBKWB9JL94yMzhGVXG7x7CBgMMy2JqRWtz1WWDQeOklUymKElhnL0Rtkx63bgoEiEoICqDUPQFNcGo7d5NYW0Ai0Ib8W4w1WoFwfTDDQRegK4toUOFmbH517CFoHBaA76U2j1kKeBn1vvLRoN2XvVUE7X7tIjnENpRSTAEKyKmc2oTnk9+Pbpe+HCch7gfT31XHo3mB98nMVT6nnmaCHfvWIlX7v7DPYNvxM++CXaMTRWjZf0ICOEpbQim+vaXuHg269y6KcHCPlhx+YAu/1HyZtRzMStuzh/4eVcd/t5+IbOxIsHu3rp4WOVXPl/17NiVdfiOoeHw+JThzP/377L5NxcxlBIPfBY7T/4ww2PxtQNyocO4Wc/+zzFJUNhLmy+9TBPP/0AmzYtCzvKSXopL4EJWXBjHeSPhht3w9QK+PURuPGAWhNukotSgQG8qSa02KhpoPqG39LYuJ/dKE2l8Ukoeh9YCSNEGdX/7PS7htSr07jBM8/UMXX64/xCArz3DrT4hoNnI2oN7iCyjkwvoK+j9yLCjBkzREJ+kccuknoP8jtlU8gtIH/1Iv+Xg5wDcvfsgdLyztqI6PPOe++VfGPEA3KOiezhYUCGZmXJL267TUJ+f8dnNr7xJ8k3sVtAjBw5Ul584gmRoPZgqa2tlalTp8Y8fhDaKKgG5BgW2TAfaR+OvOrVHpGxPtvdRz7I2y9c2q0Ivjz0kBwwRoajZMd8kI9GITJVz6UWbSYU/rs/rZoHSwAAIABJREFUBakDmZbmaPpgkFcmIeOLEO1GMKo/go/xQeGl3D8ol+/nw7GKQYyfMoB7y4q5t6iQrcOLebUqj417qyM+5mtpIUeEwQb+ax7khe2VAhxsb+fXv/kNx46FlyWIFWpTVFZW8uqPH0X81l7Q1ETL3r0RBb3DcQR4OAdMuTLYfw/sbIbqKhgT1HUwXWiG2I4Rt2iZwO1yClWoU8QPbN8HvK/X8HHoUozvceBSul+tJRGCwNExmhWqroR9cY9PJzJCDVMYGOjjhHGFXHqolX+ZOo7CBYO588A2jq1sp3FgDr6RZ1ExZCDh3oB3UDu0VuDa9dAUJex9LBSKiIY3k7hIwfJQNbcSJJcsBo0YwcSZMzm6cmXM1OK/+OHZI6ocCOpQsCtbpr1seXXiQ+Li9PmcXPRraFgC1BNAk74eQxXN1+gaCkxVSMqAGwy87YFtQRXCeNUpjwMvvgID29VCiVbdubeQQcICjM9jvucYWcPgzy+8w+PPwJjhUHIIXm+HMezk9uI6xp65oMNzllcCx43qPru6Hcp24O+wigCPh3avN+6N2iNE8FMa0GSnPMJ9ZmlCl/YTSWIanPuDM5h+y9fY6P8VoA6U1In/0TElC/6+EE6oBQ7BsSqN/L9joEGUFWBfQh/q0VwGXHYtZD8Cpk4Xxb5CRglLKG8ur+wdzPcOVLMdXc22V6lbeAJwCTB5S2OEl/mkcyArDyqa6eIR6yucgXqJDhKztVDPYh1wdTc+74ORV3koezi/5yUkDKOCcMIwMJ8C7lR2woJsmHYTNO+Fr9cCr8CuVg3w/g0t7H5KPZx1KTxZDHfcD0eb4WBvEefCkFHC0ozhrpAT8S4G5gKXDYDCAnhkP9wJ/DX8Q54KwEMNoQ4PTSLyh13+MxntN5lWBRut3/CjVd3TjaqSxMckQk4BTD8fXk6jsARDsPVRrffcwahrhy2/gCoPIJDbpvGvJtSD+LUy2D4Gph6Di78Ci7fBnww88gJsTGe5mChwbeBbrfI2GmNesF4PNMasMMbssP4OCDv2ZmPMTmPMNmPMYre/UeCF64oN54woxmdgYSn8vGwQr+YU8L8eTSTqWj1wPpCLH1V5Bnf5PwwPQk6j83rgcCgpdx8BNWhOvVs0ozvLAbqvIbnBrgM7iV0mOwmkuUbEWuBMnBpqTwJPCmw5DkePwsBj8GXRul4AJ1bAHXfAsr/Dr1+AUDs0Cby5GnYFldgxKBsKE/YU7Bkk4w37NpGb9E3AKhE5AVhlvcYYMxktUjgFjV391urtkhCmBM7+dygryOIUA0PnGd74xkw+feZCChrB4/GQbY5CMLK+exlOya/OzKFhwN66Y+x6PawH49CToGys6xNPFY2JD+kRjKWNbkdxAii1IY1oJn6p1N3AfWj6MsAFOVC8HEqa4OvnKmVpSzFcWwD3fR0uvxzuuBUmplqSM0m4UsOMMSOAC9CeK9dbb1+IMjxA07dfResxXwg8KSJtwG5jzE7Ue/pW4h/K4oldg3hq2xY8wMblwknL/8kw1B4pKiliRFsOLXtryB/v6B7HcWjqkydXUFw0hCNbd7OjoYEjQBvtbJa6sCYzhSRqcdra3EpDfQO5dncsb98mHsVDNVPQeiypIxiCI33HUQQi1eccYMkEKLgN/nMl5LwElVfCWQ9A3jhYuwlCL8MHS2D79ljf2LNwu7Pch5Jpw8+nW92Kwxuw1tbafqYidjGvI0ejCLVfVqPGcnkgwLL3NlPTaRH1hw3ss589lV//8mZ+M/sU5k7LT7lpztHKo1R/aPtkDYx2rU32Oj6kie5mtYiA3yXTfyju2zSkipJ8mHAt8CzkPgvmJRgxEwqfBW8hfPNCuHIwtKyAJrfFirsJNz0llwCHRMRNRVFw2a1YwhqwlpWVdXw0nD2b2/GurjQF2dnkGhOzx0l2djYPP7yRK6++m7W5pVS0pt7FuJ56DoeH+9LRrbSncPBtaO7ettAWDPBmVSR/+CTgLiJPvQxVIW4mvZdkVht4fgJ/vwfaVqt6vWsd7L8PjtdA3j6Y7IfG+jRy7jrBzc5yBrDUGLMHtcnONsY8Tje7FcfCEK9zE+z9JoRGyTcePcqBQCAiMFiWC7NKINsYzj13Ji31R8mtqeHu5S/yj4+ja8genG3QFQxMOctpmpppGHKgDRq7QVsXOL6mjaYaJzfToEbqxE6Hnom6w+tJ7yT9IAgXvg3frFNy60fAAw2QOwD+fj/s/4myxK+4GMb0kk83obCIyM0iMkJExqCG+ysichlp6lZ8wpJTEnR9E8JN56JhpZRNHUVIhNdee5Pm9gBvVFXRIrFvpZfkvFugEt935PD4OP+2a6BsTupfcAj+eOU+DjY4i8sUNDX4JRyh8KIpCFm4b9OQKvah/oYx+bDVo3bpqZ9Aew1c5oen/OCthav+AXt6KebSHW7YT4BzjDE70Ln3EwAR+RCwuxW/RLLdiktPAxN7WOJvw7/mxbB3BtLIGAJAfX2XZsEOdrdh0+yNB0x8eliPwIPuRqXWo5CeFzgPkDUgoK0FUkTrOljbWonH47ifL0Xtks5LTinKH0u1ak1S4wIuWgKLF2o/+UWAJxveGw0fT4EiAw+OgsfKtFlSujXlpIRFRF4VkSXW8yMiskhETrD+1oUdd4eIjBeRE0XkxdjfmDzaQvDiweTdpG+veda58T4DZ0WLyHRCTMlzh1y0gslGVH+9B815uQd1afcE8oABR7rHh81aBPc9NpMRuSMAtQ+X0nXyFWC5b0lfMpuNCair9lcvwNYcrXaTczpUXAynD4F7JoFkw8OH4bv1mkOUbtslc1jHSSAVFeBjwhue+9CSRvHx4QsvOC/G5GnUNAk0o3r/54AbiOy1mJTNROwdKQvI29pOdwoneQugQgRvs0634URnLExHder7SY0c6kNjCDloAt8olP9dGOXYalTZPtIMLyzXmMU9h6BxFjAOVj4Ny9vg4yaobovO2sihZykqGUV3ASgtLaW4uLgTpb6nYRNj4uOdurDssBMnQEkRNB0jD03o+iuxV7NcdHU8G50gglI8Lkftn2TriX0JeIGujYJKgFGjB6DViDub4+6xhXepQftTjkG9XvUoqxt0olyJrq6p+t0EVafGoLvTYrQB1MPAmk7H2lZpIdoM6ULg3/bA0eu1acA60VSIIKoujqdrItoooMkDOQZ2x1FGwlvmxUNG7CyBsEJ1EyZMYNSoRM2kIzF7UPLa6lQcWoU7tGOvXwHUAO68z/jQ3P6L0NX3m+jNvA2t9vIHNLL7Ndw3/bQxNgcuixJH9QDeUbPojqAAHD4SoNkSfTv/xo/D/p+O1jCoxioWkgKCwN3olZyDqqRfo6ughON81AN3OZBv4D4/fCEEv0RrSp+E7trRmDo7gOkVMCtOhL/AwJdidWrthIwQluOtSTbjad4GomqHASYvWRL/+CiYSLLC4igMg1E1wlalBgJfRfM/rkYn2XdRFew3qPo1HeUD+YFb0YovyeBlo8XG0+G+bmwM8rvfPg3ojucUp3VwHhok3kb38nPaUfvjNTSFIdYEtPvBeNEeBtkjwPsdeDdHSdZlaFJx+SA4YYjGfSZE+Z63DsDf4tSimjAeZsTK6uuEjFPD7FJE8fD68g34bw2QnetTNl03PEFukYWmChxA4z+voEb6AXQCvIr6zhtx9GeDitgFKO/plzjlTJNFbauKapdrk5cHg1I/fxFh+fJ/8PIGrVJ2Ltp4qDNOR89rGfF7a7rFTrpmX4bDtjBLULVtbx48/Ti83aYLTgN6fZdMgr9thBeJXr7iGJra8Qx6n4ZZ73VYsLkwexoUrE1c/iIjdpaONjmAz+tlyfTpcQ+v9CdfxNPQDJL6bS5AVQdQNWwturqB3rhKVBBCYcd/BXVprkWTnFIVFNBKkVGzEwsLoYMBkTya9zdz3w9+Tnt7Cwa1S+y60A3ouQ5AUw6C6CLRG7C5BFVoqvb5O+A7B3VXt8c3ugjOOx8+aNdrG26WnIKWwSpE7R1bgz0AEa4etkP7n7qq1NGQEcLSWl/fYSgbr5fCKVO6HOOjewzyeVs2E9jnphycg7Z9+wha/fVMDhGZXKXEtjuKUTXsALrj/A6dZN2xKtqJnn47rkDIGpKiizsEy/6wjPXbnJhxAEfgd6Kr7RmosOwg/m7Q0zDoTvd91ClSjM4BW0WcdRKUlRLVV3P6CDgvR3fE3SilpLPpkg184ocv1cEZ+bBkQHzOW0YIS2N7u92nJSYq0LpvqeJ3DcLulrDfKCfhcvLali3UNVgaejZ6xywUoJMnGr6A7gKrUSFfj8YluhNoFqKXUB1NIT4pT+k7G/ft59cP/5SgtSYLWrfYVkeOo169H6Gnv4zeSzuwx3MfOtlXowuGXVQDwLsLttwJeQNUiApRkqcBTtoPz7TpDvM+SiuZSaQwTEKTC2/Kg1NPhewxnXadTsgIYXGD/YQlUvn9SLNzWicYQ1GCzzfRKeNj/smQF9/EtwtlAhr2z3UiAhuJXVXleVRHNqgxej3wDVR9+ArasySaMZoIB+ka/2jfvx95Z2XyXyZCzUP34616r2NhLkXjGfZZLkZdx1NQL9jv6T3Soo1PcNy6w3F2vZnAV+vgvCrYUK1eyGyc4uPPofG4C1Dv3i3oohW+Ozeg53eoBf5vA5S0xy+ekRHCIoEAweMO1WJBTg55UQhie6y/lXv28OHmzR3vj505k6LBLiLyEZhEUv6wQC7UuZviNajuvwzl/TwwE74xSDMEv4e6PFOhveyga35WTm4As+weaEqymnDTPkL/fIT9OAIwEN1w7StfhAq13W8yVifi3sJWHGG5Khtqi2Gi5fF4H6dmYj461gCqwg2cAJ/+orqZv4qTTbsHVaVXAs8FYV4N+OJoGxkhLC1tbXxy0GlIMHjxYrz5kUGF8IE2i1AfTkUZMEC7FqUTOTkw0j2h7Dl0lb4a+N67sOqIetF2AV8msp+JW0RzEgwsz8e7+Dg0/wr32ZJB+PBRth6ojrBB8ukaTRdUpbw/hfH2NMIts6Ez4NxvQTDfcbTYGIGqU1MGwsw5UOWHl3dAu0ftyKaw7xN0gVgRgrNqYVwcakVGCIvQ6TZnZ9OZenwW7jwW6cRU3JP1KtGV8FJgOZp37UGDcD1aJu7ci+GcVTB4G+6bWxyD2Rcy58df5ATLGW1QQdmKCrRdnvED6xz29OSY42AMia9xFrCjGrb+Ej6p76o6BYBvTYGvXgTPvwe/2gcL6mD+ZKjyRtKlJqCR/++hLIkDcZrIZISwuMFaupdlHvQH2fPKno7XHtwEmUKEl5xIRlg8aMT7CDB6LGwwmkiVdOuFRCh6W1tvmN+gBA83GARmGkNKfsH5VvNxg9olt6DerxtQGsoXiO3ISAcqSWwXeYEPdkPFFJhgInfbXOC2bPhOA7zxZ9jt13vxlxb42YfwnjWJPGgA9lPoLiRoI4t4DozMEZaO1GLINYaSsJ2liK67SuhQcu7SgAR4s9ppaTecxGVV2xuPUrc5jIyRxNYWQukc84A3vbqjpKXm+4YaaNuG+nkSuTki0UY2m62TCqGxjRBqc90DfJ3kmQbdRbQFsRgna3YwunztAh54qyM814FBOTBlBjTUwlct4+QQsLoGsgVORRnVV6MOjYdwTz3KCGEREbaudDw6FcOGMX2mQ9gZSNfyEs+/+HzH81w8TEzSZPaWQHaChfh4q/DmvrBN/gKSumICvAxcshP2pcuN9EkDtKfWmDRrTAHjihd1vBZ6oXpmCjg1F4ZbakA76vmqQt3c4bteHhBog/Pegt8LLO/kq/8sWnfuanQRS5aflxHCArApfInIyoICZ/JX0rUq1lE52vE8r7CYyfPOTu4HBxTAuCQj30dIynfajtIwerCqbAQMMHQkKZOWPOM95JWk2k+552HXhjboZB5vPd+WDVN9urEfRxnLO1BuXri90oruFtXA8lb484HI2NwfUefKheiu2YITl3GDjBGWeLC9FjGRZTR5PymUoRGEJGDfvQyCb85EyD0xpc82Hm9ghf/lHh5RbCS6dHlom8Hh6CRvQO97XT3MatUIfPh3dO4+LThq47uoLbI77P8lwP1FcGKWY+Qfw/36l3FEykxGNoVk0zNEwp6AzwPzJy0hglqQBPauWc+hmj09OqZYsO3DeMUYmlEB+DnaKc2HCpCgEfyvAx+iPr+PweripjErN/ZgA7BMICBODksyiYSZs7NUVUHQMe8WJcppqQlCa2IjP2I1WFsFbUn61Pz+DityCkuZmEGXzOs1eEd64KOnk/+wQPGyVuaTi7eHzqkizv8mo8Z1ot3lKEqvaQSspEiKUXf7I6gruw4ltZ6JehwXEXmfwwOr4fADTzdqZcjJCcYRDRlz54+uXUuoTTVQYwwjz460QUrQHBIbH214jYbjiR2xQ3FOcmPVWvxBR8t1o1EdefbZDt6aB1/mXDDgZHyMH3gmjF2R/IcNjLrpbJ68ZzOnjDq1R8YTj1W9GmUwxPuliegucRi1PfahatRhVGmuQjMt56E8uWpUuE5EhcqGrb5Fw81Zmv+ymKSV8My59yuIr94cR+1rG7tpoTnM0TipoCDq5K/EifxuJ5LMeIGLcS1ranIufDZpL56dDHYSYn/zJjAputqm5pP97ZFkV/QM+yEeCXEv8L+owESbdMWoAW+rRQYVgEJ0p1iL3rujqNI5Dg2g3ooa7TNQ93JZ2DiyidxxhgFntMMbB9TxsgAnGpBvHRtvAc0YYemuZ3XW0qUYj/vTMcBgF6p+hKJXTlduRR8iqz2I92t3wOup1zk2EFEFNJ3YhrKAO4vmWPTS1uKkIsyz/npRcqRdLe4B1B2/DY1dTUPVq3nA4nJnd8tFo042WbwYjUQ9BryOYx/lWY/JaDt2O1cmGjJGWPD7oTne2pQAWUku+Qb4dJKurSFE72mRArxoFmV3nGsTgdG72qFtUcJj46F7BZ/c4310d+jcXLkKpdPY6lc2ardMx2ERh2MdKgjNKOPgHyXgHwy7G1XYctEidtOBq1BBGG/97mtokLIJvZWl6G72Pnov4mk3GSMsxysrqd7klCDNIQdP2of3mTR/fyQMKm+Xo0zXH9BNvpsPzY5iecpfIThZielGK/BnugYD/TixKC+6O9wI2LzyHNSIvxgteOFDi4GsAPx5cNVP4cgs2Nyk9BXQa7saTbo7gsZlnsThuFUDJ38ZrqjQ+xJEE/Xi9VbIGGGpFWFnGJN4zqA5VOTE8a80NsLGjUn9Rqi5mVBHiSWDO53qOLZj0ouHM7txyTxoYkAQnSA/pnsJYZRnwSWfgpMnJT42FhpChOp7a29R9nJ41H04RHSBtu3SVagwgFLrPwP8D2rw/yUXRhfr3atpg/tuguf/qS313kQFrx6d+COtz0erEjd+EXw+S9WzwT64ukwdQrGQsXGWrOlZeMo8WKWsuqC5rY1tVVUkkyO4r6qKjR9+yFnDkqgHuf9jOLQXKk7Gw2TGMpFU6zEG0bI/XnQydLfJMBWnwuU/g5zUWWfVn2znwM6e741Xgap3nWuMde6EVhXnO4pRz9aQMhjbAjWNasgXBuEOvxVvCcFrx6wCfsNh8yGoaFfHzsnAG6hQHe303T7gwVsg94g6C66dCR/vib/LZqywJEILukLNt177rEe8tnTtELdgeDR8VAV7DsO4ClCFoPueoyA9ICjAhNxcyD4ZTOrN+GpoozrlLjbRMdXAU1lQ51f1KdUi4uXoTlx5GL4vatSfD6xth6NhHCJ7qVhf5RBCc9Fd5S1UcMM7TWehqvDiGq0psAb4wXrYm2CDzRg1DCC0NvVy05PHjOHEJIvzTSFxU556OrlE+zqpxoIPWDqsAg+5qJmaGsyudZhQz3Yy/VExnHiOKgXd4cXtQo35QaICl4ves/B9tAAnGBpCVbFBg9T5scx6fzaaagC61N2IFrLYijoKVpNYUCDDhOW5XQ7bJwcPk6JWwY0O38CB+AYk149qaD4UJLO3+nAXnOkFFJd4mHfnxV2S5JLFxMkLmJSXTC/mxGhrAwnqCh/LJsslccjqCuCLqKeqEhWYragg2GtWE5G7tIg6VrNxFsK/4CSVDUN5Y99Ci2H8BPdNcl0JizFmjzHmA2PMJmPMu9Z7Pdat2L7d4czi3IJCJp356fgf/OSTrgkNidAWpnLMqYDyJFrdGjKno5G3GArndVtYcieeRMmkryU+MAl8vxWqX4pd/KEU9YqNjPF/G2twhCTP+twGlB9m74XZqIvYdtWIQEODJnLZHbSa0YBoFuoNewn1zHlQ93EJKnyLiF8bIZmdZaGITBMRO9Gkx7oVRx2gz8CQ+MN78/XXE5ZQ6oxDzzzjvPCcDCa5gF6mkI49oVbMsZ10N5zr8cBSX8/SEnagKtDzMf7fhrrOE6VX78BJlPagXq0qnDO2/ZknEelRg8j6Z/bn/5XICd+KUqiGobvQSbkwPs6U644adiFa9APr7+fC3n9SRNpEZDdqQ8VNSuxI5WxuhkBiZ+p41C24g+SnyovJ1lWWIDR+2PHy0yTqc6yYU1FBWdIVZ9xj3jgYWLKJrpk+ycF4IO+CnvXzDEFTk2Nd6Ra0BnSsO10MHV7O6ehiegwnQGkTJQUVnpEkrvYZsn43fNWeg9aj/gw6ectLoT1OCN+tsAjwsjFmgzHmm/aYe6pbsf1e+xtvEDocqxqXAy9O4ejwE0k22pCP1sGNh1B7kH3LnGBpMdnuSn2OGsWs2YkSl1PHwBPmk1X+H+jU6gaMYe6AyRR0siDmoBVq7kUN4quT+KX5KDshVbfB6cDj1hjewClE/gmqdn0bNR+96H3/O4mXDA+a/BXucMhB3cbjgZs8cNPVXV3Mnb/DDc4QkdNQz901xpj5cY5Nulux/d7aYJDDYWrVbGZHjeJ/Qte6vx6Sp10LiXuNBIDXO5ZAH5qc6gI+HxdeeCGeJPhqyeBzvjLw5NMTPpryU2fjy3P2yyxUjz7PejyKGuu/JKKCbUxsBP6d1PP3V6Dlb78IjPfBKWGlBdqB262/U1BjP9G+mE/07sqrUVVxP1B5Eqx7GOri+LldXWkROWD9PYR2DJhNGroVtxG5Gp04+URMFAM2gOXBaGuDdl0rjMdD/qTk9pYskqV6GdwpYYqLzj2XiUl66NyiR1P6x9ERPlqAEg0FvclL0QIWa9FSp6vQPJJ4u+tOlFrSHbfxVpQZXACMtk52DEq6tOdzHaqeJSoA1YYSKDtfs1bUWbAPuPMg3L83/pgTCosxpsAYU2Q/R9lIW0hLt2IhQpOdEX+EdR98QIPtbvZ6IazIRUwEaiCk2rTJIj6/IRrm4rpt16ARI1h4SSJFLzX8tboaf2P3Kw8fqw3whz88SXNTE1egXqrR1t9NONSUeuDX6IR9EVVp4iV7dRcBVHt4PwBvW6dZSWSa8H7cBXftXJho+DPawW3VURXQ7lL0y4G1xpj30Um/TEReIh3dipuPw5bVrg4F2BEMUplsk9S1G+GwpZnme2FOkgG9YbjnPXg8nH1Osk3E3WH76tXcct11tHSHqd0Of/3v5dz2ox9wUiDA7ah36JdEX93WoW7YQlRNexp1eKQD+ShPrA1lI9seL3t3KML9bWhAGcfRvP5DgctQe2wr8TWNhMIiIp+IyFTrMUVE7rDe7/Fuxa2tIXZ/kt4O63tboalDdAtQxSMBWnZCSDdoL/FzHnoL/xoK8fof/8jxXZ3LNiQBH8z69tl8asE1XIVOnA3Ar2Ic/h5OE1m7AsvjdLdBX3T8kMhNfzyRqt843Gc6NqFj7+wEKEAFpQonhBaPzJRREfxmIg33YpLz9bjxUn1EuJvRXSPWt156j7ZWjfMOYw6nd/Hqx0O3eMUxUTYdhmdLRKp10jAw7bRCrrnmUrb51BZ7iciqjGU4rtUv07Wf5BA04NfTJEMPjv0gaNwm3D28lUi+VyrIQft8voR6wWqIydvtGFPGwnbrucU5pFadPhEq27UiCICH4fiSyC0u4hlXR+cQvUaLD/UsnUakWBf/EG78vMGXjNzGwNy5w6ktyCKEBgvDcRPaJfnv6MT6IpGGskHNuO70zomG7cT3vAVw6TWKgzp0N3ErBBkoLNvpuB1ZxCcQBYOw06kDXzB2AZ6sJOgrvYDZDE/Y834hunI+SlffwUg04ekVVGWwBeZ/fgQt88cTGJ2INJIYg63HGiKTD3JQmru9/9aghn3nVnkDUVOuJ9GKMoajoQgVUDcoIDZZdhhKoYnTzDgCmScsr70FttF+0miYNCbmoYFgkDfWrXPeOG0Q5CZHC076JtvtpVzCw9y4it4CdMVeZI2ls6gvRFe/ElTdsW/sxo2w5Js7uevWn9FSnRrhPxgMUltby1+eeILrm5u5CycoVwx8B20aBLp83Qv8Fxp9D3erGJxUiZ7E54ms2mKjAU3ySgTblmoAzp3nXFu7GEYQXQBqw96Pd68yLp9l424IiSXF3gH6iIEQKbRCCDZDw8cwVGf8p0u13UB8d107alEVqEKfRGnhpuPHY/ruS9CuYLaqORGdiNfhuESXoy7c01D74XqUMetH2wveftddvLJqFdd96UtMPPdcPMYw1uSSNWpMxDYlIuzZs4e2tjYQ4eOXXuL5LVtYtWoVBysruZvIeEU2Gg+wr/4mtNA56M6yAif70KAljrx0r9NBOI5Z52hXuw+QXNwm13r4rc9u/MCxHgWnMVMueh/sNozxktEyTlg2k95WbG3HW9m3ZjtDJy7UNy5Aq0XHu8sH9sPm92DuYnWkTyd6g8coaPf7YxaEOBGdDCHrrxddTVvRRkg7UKF50fpJg5IBX8QJagWANevX88Y77+DNzsYDTCWfnCmnQJGzTooImzdvpsVqKBvw+wmGsSVusL7LoLtXuBC3ooFGe404jjpiwunkn0YFJrlE79h4DvV2HUInafg1LPLARR54zJr9WeiuHJ7l2IrOpRLgh5+CH63rSsXPA36LNsh9B91h4gUiMk5YwmE4IARpAAAMMUlEQVSA8h7OtmoCNoXCer17E39/bauw/bgwHXQpSiIoX1pWFtPVvB51vZ6JEwOwGx5NA85GjdDnUHtlNGpHPIDuqOHdwwIiBKz0g7dpg/dcSrP9eevviWipIdvZ8CrapyW8tMd41MUcwJlApWj/mmSFpRxVhTojRKRn9JQ8KPHC2kaYPBrOGgKPrYvywU4Q4Nk10YmW7agN9g5RGmpFQebZLGHw4OVcliQ+MNmclggsIdFlaCDyhk7txq91Rht6IzufwXicCPl61H6wXbplaEPXdOAMHEFZDfwLump/CmenaUZ3mlhXfdy4fAoL3S1yscw/mw6Wi16HQBDOs4yOpiA8FeaRbyd27nw96riINtYAqlK6nT2ZJyyBIxDU8JHBYHISZFutWdOR0OUhlYKRJSSXpZLH9KSiG2vi/vc5NB+8czSmAO0UZk+5f6DuW0hvDpodMX8M3c3OQEmIQ3HUroNEtx9y0N3vpAnzKCpyV41wU4z3bXOrFVXF9vph9AQ97x2V8IqLLSxRtYRk80MzTlhky25kh8XwtwttxcHhmhqCVkHxoYSpV2mDH4c0nhh5pyxm+oTYwZAg6iLt3D7VoBR1u+VryDrG/uV03binUXfx11FBeQBVsYIkbv4zBO396TlWj9/fva404WxwOxdl4JtaLHymgC+GceFBKTvl6HnEQ3g5YDfh6YwTlq0B2NXhtghB5Za4x6/GSTLy5ueTPSRRVCNV2Bp0Acn4jrMnnseSz38x7o1oQw3NViJVgkFEcpV247h250H3ovcx0IIaup9DXdoDrDG9jVMAwh7zR9DR7bgZtW/qgb3r13PkSPhU7D4EvUb/DUwriL1r5KHVLH9IYoPcnjd56C6TqNxJxglLC2EZdp4QTIovLBH6pmkDr4vMwT17OuyccUT35XfBig3WkxLIdt/iG+NlUEFewlUrhHqZ7sbxyHjQvu22amn3Ugyi6ov7/S05TEMFxaaYVqKqYvhesR3d+e5C70ET6nr+EBW2nki/tusT21gGvJkN9/4ULoohLQE0nvIKrqnutKA0l0SVOTNOWMIRwkPtEBcGvo1B5TApMU0/tGZNR+6+W/7Z2x0O+Fb4XLx8uk4INLDm45UJ6wnnoSt1eHMxA1yE7mWFwLVo8O89tIZv6tXC4qMU55ocRd3IU9AJaK++tmpkp8QeQ4VpF5qx2hP10+1acOF4LAg7dkF9jF6zbWhp12ej/xvQc0iF55HRwrJhw8fc98CruKdT+nDDCV5G2I5UlgVliQvnOQG7fMh3PxVCTXtoWZOY8rcUjdZfhCMsglNM+y7gNtTgf5Ho7taewvuo2rUDtV3q0EZCM3DUwlGoXWAXq3oDx6Y5j54pr1ZHV7LkniA8+VtojpHgX0DiAOZXUIoO6LV2mzKYIcLiQZ2FhZSWDqC4WIWjqqqepqZC1Kwrsh756CWxjeZI/9dpM2aTN3Ayzql5rOOzO44vHefkxntGTmHhDbeDL1FbbOd3fGS5UDP0+Ib3P2RlbXzVsBSnor49akGJgt9FXcZ19N7NqkMz/E5DvXCz6JrnkYPubKdZr8txrvBLxG9a1F342+DsGBVr3TQ3r8IhYdqtLNwgQ4KSU1Buq5+vfGULo0ePIRgU1q17nlBoEUTEWg6jt6oOLZdWQDDoJRg8TG1tgKlf/g/Gn3AVWy57HoJ16OU7EzVFd1BSkssPf/hVjDHUtbfz13Uf8G77t8AzH02ejY5x4xz1bvYJNzK0dAMHji3F8VeFQ2DmHDAGObSM9tbJwAT0NnVVyFqAdQP8LD3+EcMsXlwTWibHrivzN1QNK0TVsHQjfC6uR/PpG3HoIHYG5SPAFwzMuXwso56uZGdjgE2khytm4xm0gEZnDCR25qTNlABdlLxhr21VMocElBoR6fMHzBAQKS4T+eBjkQfWiMydu0fKy38rUC9qjUd/5OTslBkzFsmsWeNl9OgvybTFm2XI9NjHe73VctppH8htt+2ShZfeKua6V4WlIWFA52NDAo0dz+fM+bvs2LFDtm/fJffee78MGLBQYFeM3zkmY2fdKg8++JA8/puHJIvTBZYKbLHOp916NAs0CeyR/7x2paye+a/yIB7ZA7IdZKoufFIAcoX13rsg5db7vfkYDFIR7f18I09/e6GEDq2Vqy8aKYCUgPwCxKRxPGVRXp8f49gckNtARlmv7wT5HshpUY7LA4k5T/taUDqExYhknyxyQY1IyZLYk73ro0rgIYEHBQ6K79RGMUMarUkYEvJFuMJ6zBXhBL8wrVLgpzrZS1uFs0TYIMJTItwsgk8E3hW4yxKakJhTtkrxyEulqHiMwCUCb1n/E2GwCGeLkCsCNQJHBc4Rj8cjeXmni5Zc+LzAbwUuErjCev0ZgXMEFkjO6EmSmzNdDDlyUwGy/zrkO2ch5TnI2SDtIIdAxvaBoMR6TMnPkU2/u0MCgSMiEpSVj94oHktIFqdZWDo/poJMi/P/PBCP9fxbqPD7rNfZIHNAcq3XseZphqhhNVBwEH95OctWCazfjIbH4m2K41DVZgVan8MPbCKw+Z+oiZcHLIb2LHhlPHjnQnk91C6HqgCqVNyvVun7wD0oz2MH1v7cgLoCqoEcJOts6icthJftirv/0IcXOGMaTDgZ9rwJn3yA2lXNhEIhWlqM9YUrcDqz/InOJejawvyWfj8MXwJ3/wSueg2euAM2rFGazUQiizb0FQzw80tGc+o3rsF4lU8wc/6VjB/yCDsOHeKfvTyeFuLbdHayei5K36lD1bZFqMOkChc5rX29q1guXGv1nSd45woUu1hNjIDP5crjEchK4vhoD6/1PfG+38QYZ7TnsR/Xj0LkmHOJgtVI4CzkPbqqH73/8AiUCoyTO6/8sYiExEbQ75cbP/t1gX/vlbGMD3tucLeT5aEqrUF3l0tAxnQ6JtY8zRBvGGhI6E0IvkXiYpyg5+U2vz2E7lLdyYcPEpvAbX+/RPmfxHgeB+FuMcBTDt7vwlSfroS9iYmoO/vRYXDfGK3IqSlg1xGoux7aHb+gJyuLr867m7y0FklyEF6qw57p8WBQ1sPj1rGHUf1lj8vfyyBh6UcHjuFkJ9mYDZ5TeqKVUnLwoYUqLrsbrl0GlwwLArPAXIJ/Si7BTjO08LPHyR/4+14epTtMAq4h9WBuv7BkIo7TdbkrA66K3hsxndiKJpz97J4sWlZdxalZq4FFYIbx+zbDR52CFCMnjeS0GV/o+kUZgAHAL7rxeWPZDH0KY0wt6tpPXBW8dzGYzBsT9I8rGSQ7ptEiEpWikRHCAmCMeVfCioRnAjJxTNA/rmTQk2PqV8P60Q+X6BeWfvTDJTJJWB7o6wFEQSaOCfrHlQx6bEwZY7P0ox+ZjkzaWfrRj4xGnwuLMeY8qwX4TmPMTb382w8bYw4ZY7aEvddjLctTHNNIY8xqY8xHxpgPjTHfzpBx5Rpj1htj3rfG9aNMGJf1O15jzEZjzAtpHVMfc8K8KGvBbtT2PjC5F39/Ppq/tCXsvZ8BN1nPbwJ+aj2fbI0vB630uQvwpmFMQ4HTrOdFaLr75AwYlwEKredZaG+jOX09Luu3rkfZqS+k8x729c4yG9gp2jDJj7YivLC3flxEXkMJqOHosZblKY7poIi8Zz1vQFPzh2fAuERE7Hi93d9A+npcxpgRaBHeB8PeTsuY+lpYXLUB72V0q2V5T8IYMwYtc7wuE8ZlqTub0LJeK0QkE8Z1H5p9Hc5yTcuY+lpYXLUBzxD06liNMYUoKfY6EYlHw+61cYlIUESmoZk/s40x8erYpX1cxpglwCER2ZDwYOsjUd5zPaa+FpaU24CnET3esjxZGGOyUEF5QkTsqq19Pi4bInIMral3Xh+P6wxgqTFmD6rCn22MeTxdY+prYXkHOMEYM9YYk402wX2uj8eUhpbl7mGMMcBDwEci8vMMGleZMabUep6Hdpn4uC/HJSI3i8gIERmDzp1XROSytI0pHd6JJD0Zn0E9PruAW3r5t/+MU+d6P9q+cRCwCk0wXgUMDDv+Fmuc24Dz0zSmM1HVYDNaeHKTdY36elx2+5XNwBbgB9b7fTqusN9agOMNS8uY+iP4/eiHS/S1GtaPfvx/g35h6Uc/XKJfWPrRD5foF5Z+9MMl+oWlH/1wiX5h6Uc/XKJfWPrRD5foF5Z+9MMl/h/eoKi1wDnewQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(photo_masked)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2e507361190>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(photo[:,:,0].T)  # transpose"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_array = np.array([1,2,3,4,5])\n",
+    "b_array = np.array([6,7,8,9,10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 7,  9, 11, 13, 15])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_array + b_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-5, -5, -5, -5, -5])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_array - b_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6, 14, 24, 36, 50])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_array * b_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.16666667, 0.28571429, 0.375     , 0.44444444, 0.5       ])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_array / b_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([31, 32, 33, 34, 35])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_array + 30"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 20, 30, 40, 50])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_array * 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "130"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_array @ b_array  #dot product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([2,1,4,3,5])\n",
+    "np.sort(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thank you"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/machine_learning/Regression/Regression.ipynb b/machine_learning/Regression/Regression.ipynb
new file mode 100644
index 0000000..b7e376f
--- /dev/null
+++ b/machine_learning/Regression/Regression.ipynb
@@ -0,0 +1,405 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Call:\n",
+      "lm(formula = y ~ x)\n",
+      "\n",
+      "Coefficients:\n",
+      "(Intercept)            x  \n",
+      "   47.50833      0.07276  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Creating input vector for lm() function  \n",
+    "x <- c(141, 134, 178, 156, 108, 116, 119, 143, 162, 130)  \n",
+    "y <- c(62, 85, 56, 21, 47, 17, 76, 92, 62, 58)  \n",
+    "# Applying the lm() function.  \n",
+    "relationship_model<- lm(y~x)  \n",
+    "#Printing the coefficient   \n",
+    "print(relationship_model)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Call:\n",
+      "lm(formula = y ~ x)\n",
+      "\n",
+      "Residuals:\n",
+      "    Min      1Q  Median      3Q     Max \n",
+      "-38.948  -7.390   1.869  15.933  34.087 \n",
+      "\n",
+      "Coefficients:\n",
+      "            Estimate Std. Error t value Pr(>|t|)\n",
+      "(Intercept) 47.50833   55.18118   0.861    0.414\n",
+      "x            0.07276    0.39342   0.185    0.858\n",
+      "\n",
+      "Residual standard error: 25.96 on 8 degrees of freedom\n",
+      "Multiple R-squared:  0.004257,\tAdjusted R-squared:  -0.1202 \n",
+      "F-statistic: 0.0342 on 1 and 8 DF,  p-value: 0.8579\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Creating input vector for lm() function  \n",
+    "x <- c(141, 134, 178, 156, 108, 116, 119, 143, 162, 130)  \n",
+    "y <- c(62, 85, 56, 21, 47, 17, 76, 92, 62, 58)  \n",
+    "  \n",
+    "# Applying the lm() function.  \n",
+    "relationship_model<- lm(y~x)  \n",
+    "  \n",
+    "#Printing the coefficient   \n",
+    "print(summary(relationship_model))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       1 \n",
+      "59.87736 \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Creating input vector for lm() function  \n",
+    "x <- c(141, 134, 178, 156, 108, 116, 119, 143, 162, 130)  \n",
+    "y <- c(62, 85, 56, 21, 47, 17, 76, 92, 62, 58)  \n",
+    "  \n",
+    "# Applying the lm() function.  \n",
+    "relationship_model<- lm(y~x)  \n",
+    "  \n",
+    "# Finding the weight of a person with height 170.  \n",
+    "z <- data.frame(x = 170)  \n",
+    "predict_result<-  predict(relationship_model,z)  \n",
+    "print(predict_result) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZbFX64CCIDLtyz+bhVAAPhDY4DAwpAS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\nRK5CYII=",
+      "text/plain": [
+       "Plot with title \"Height and Weight Regression\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Creating input vector for lm() function  \n",
+    "x <- c(141, 134, 178, 156, 108, 116, 119, 143, 162, 130)  \n",
+    "y <- c(62, 85, 56, 21, 47, 17, 76, 92, 62, 58)  \n",
+    "relationship_model<- lm(y~x)  \n",
+    "\n",
+    "# Plotting the chart.  \n",
+    "plot(y,x,col = \"blue\",main = \"Height and Weight Regression\",abline(lm(x~y)),\n",
+    "     cex = 1,pch = 16,xlab = \"Weight in Kg\",ylab = \"Height in cm\")   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   mpg    wt disp  hp\n",
+      "Mazda RX4         21.0 2.620  160 110\n",
+      "Mazda RX4 Wag     21.0 2.875  160 110\n",
+      "Datsun 710        22.8 2.320  108  93\n",
+      "Hornet 4 Drive    21.4 3.215  258 110\n",
+      "Hornet Sportabout 18.7 3.440  360 175\n",
+      "Valiant           18.1 3.460  225 105\n"
+     ]
+    }
+   ],
+   "source": [
+    "data<-mtcars[,c(\"mpg\",\"wt\",\"disp\",\"hp\")]  \n",
+    "print(head(data))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Call:\n",
+      "lm(formula = mpg ~ wt + disp + hp, data = input)\n",
+      "\n",
+      "Coefficients:\n",
+      "(Intercept)           wt         disp           hp  \n",
+      "  37.105505    -3.800891    -0.000937    -0.031157  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Creating input data.  \n",
+    "input <- mtcars[,c(\"mpg\",\"wt\",\"disp\",\"hp\")]  \n",
+    "# Creating the relationship model.  \n",
+    "Model <- lm(mpg~wt+disp+hp, data = input)  \n",
+    "# Showing the Model.  \n",
+    "print(Model)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  am cyl  hp    wt\n",
+      "Mazda RX4          1   6 110 2.620\n",
+      "Mazda RX4 Wag      1   6 110 2.875\n",
+      "Datsun 710         1   4  93 2.320\n",
+      "Hornet 4 Drive     0   6 110 3.215\n",
+      "Hornet Sportabout  0   8 175 3.440\n",
+      "Valiant            0   6 105 3.460\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select some columns form mtcars.\n",
+    "input <- mtcars[,c(\"am\",\"cyl\",\"hp\",\"wt\")]\n",
+    "\n",
+    "print(head(input))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Call:\n",
+      "glm(formula = am ~ cyl + hp + wt, family = binomial, data = input)\n",
+      "\n",
+      "Deviance Residuals: \n",
+      "     Min        1Q    Median        3Q       Max  \n",
+      "-2.17272  -0.14907  -0.01464   0.14116   1.27641  \n",
+      "\n",
+      "Coefficients:\n",
+      "            Estimate Std. Error z value Pr(>|z|)  \n",
+      "(Intercept) 19.70288    8.11637   2.428   0.0152 *\n",
+      "cyl          0.48760    1.07162   0.455   0.6491  \n",
+      "hp           0.03259    0.01886   1.728   0.0840 .\n",
+      "wt          -9.14947    4.15332  -2.203   0.0276 *\n",
+      "---\n",
+      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "(Dispersion parameter for binomial family taken to be 1)\n",
+      "\n",
+      "    Null deviance: 43.2297  on 31  degrees of freedom\n",
+      "Residual deviance:  9.8415  on 28  degrees of freedom\n",
+      "AIC: 17.841\n",
+      "\n",
+      "Number of Fisher Scoring iterations: 8\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "input <- mtcars[,c(\"am\",\"cyl\",\"hp\",\"wt\")]\n",
+    "am.data = glm(formula = am ~ cyl + hp + wt, data = input, family = binomial)\n",
+    "print(summary(am.data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Poisson Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  breaks wool tension\n",
+      "1     26    A       L\n",
+      "2     30    A       L\n",
+      "3     54    A       L\n",
+      "4     25    A       L\n",
+      "5     70    A       L\n",
+      "6     52    A       L\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Creting data for the poisson regression  \n",
+    "reg_data<-warpbreaks  \n",
+    "print(head(reg_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:  glm(formula = breaks ~ wool + tension, family = poisson, data = warpbreaks)\n",
+       "\n",
+       "Coefficients:\n",
+       "(Intercept)        woolB     tensionM     tensionH  \n",
+       "     3.6920      -0.2060      -0.3213      -0.5185  \n",
+       "\n",
+       "Degrees of Freedom: 53 Total (i.e. Null);  50 Residual\n",
+       "Null Deviance:\t    297.4 \n",
+       "Residual Deviance: 210.4 \tAIC: 493.1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Creating Poisson Regression Model using glm() function  \n",
+    "output_result <-glm(formula = breaks ~ wool+tension, data = warpbreaks,family = poisson)  \n",
+    "output_result  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Call:\n",
+      "glm(formula = breaks ~ wool + tension, family = poisson, data = warpbreaks)\n",
+      "\n",
+      "Deviance Residuals: \n",
+      "    Min       1Q   Median       3Q      Max  \n",
+      "-3.6871  -1.6503  -0.4269   1.1902   4.2616  \n",
+      "\n",
+      "Coefficients:\n",
+      "            Estimate Std. Error z value Pr(>|z|)    \n",
+      "(Intercept)  3.69196    0.04541  81.302  < 2e-16 ***\n",
+      "woolB       -0.20599    0.05157  -3.994 6.49e-05 ***\n",
+      "tensionM    -0.32132    0.06027  -5.332 9.73e-08 ***\n",
+      "tensionH    -0.51849    0.06396  -8.107 5.21e-16 ***\n",
+      "---\n",
+      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "(Dispersion parameter for poisson family taken to be 1)\n",
+      "\n",
+      "    Null deviance: 297.37  on 53  degrees of freedom\n",
+      "Residual deviance: 210.39  on 50  degrees of freedom\n",
+      "AIC: 493.06\n",
+      "\n",
+      "Number of Fisher Scoring iterations: 4\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Using summary function  \n",
+    "print(summary(output_result))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/machine_learning/Sckit learn/Scikit Learn.ipynb b/machine_learning/Sckit learn/Scikit Learn.ipynb
new file mode 100644
index 0000000..5f499a4
--- /dev/null
+++ b/machine_learning/Sckit learn/Scikit Learn.ipynb	
@@ -0,0 +1,661 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scikit Learn :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Support Vector Machine(SVM) Classifer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import svm\n",
+    "from sklearn import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = datasets.load_iris()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sklearn.utils.Bunch"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5.1, 3.5, 1.4, 0.2],\n",
+       "       [4.9, 3. , 1.4, 0.2],\n",
+       "       [4.7, 3.2, 1.3, 0.2],\n",
+       "       [4.6, 3.1, 1.5, 0.2],\n",
+       "       [5. , 3.6, 1.4, 0.2],\n",
+       "       [5.4, 3.9, 1.7, 0.4],\n",
+       "       [4.6, 3.4, 1.4, 0.3],\n",
+       "       [5. , 3.4, 1.5, 0.2],\n",
+       "       [4.4, 2.9, 1.4, 0.2],\n",
+       "       [4.9, 3.1, 1.5, 0.1],\n",
+       "       [5.4, 3.7, 1.5, 0.2],\n",
+       "       [4.8, 3.4, 1.6, 0.2],\n",
+       "       [4.8, 3. , 1.4, 0.1],\n",
+       "       [4.3, 3. , 1.1, 0.1],\n",
+       "       [5.8, 4. , 1.2, 0.2],\n",
+       "       [5.7, 4.4, 1.5, 0.4],\n",
+       "       [5.4, 3.9, 1.3, 0.4],\n",
+       "       [5.1, 3.5, 1.4, 0.3],\n",
+       "       [5.7, 3.8, 1.7, 0.3],\n",
+       "       [5.1, 3.8, 1.5, 0.3],\n",
+       "       [5.4, 3.4, 1.7, 0.2],\n",
+       "       [5.1, 3.7, 1.5, 0.4],\n",
+       "       [4.6, 3.6, 1. , 0.2],\n",
+       "       [5.1, 3.3, 1.7, 0.5],\n",
+       "       [4.8, 3.4, 1.9, 0.2],\n",
+       "       [5. , 3. , 1.6, 0.2],\n",
+       "       [5. , 3.4, 1.6, 0.4],\n",
+       "       [5.2, 3.5, 1.5, 0.2],\n",
+       "       [5.2, 3.4, 1.4, 0.2],\n",
+       "       [4.7, 3.2, 1.6, 0.2],\n",
+       "       [4.8, 3.1, 1.6, 0.2],\n",
+       "       [5.4, 3.4, 1.5, 0.4],\n",
+       "       [5.2, 4.1, 1.5, 0.1],\n",
+       "       [5.5, 4.2, 1.4, 0.2],\n",
+       "       [4.9, 3.1, 1.5, 0.2],\n",
+       "       [5. , 3.2, 1.2, 0.2],\n",
+       "       [5.5, 3.5, 1.3, 0.2],\n",
+       "       [4.9, 3.6, 1.4, 0.1],\n",
+       "       [4.4, 3. , 1.3, 0.2],\n",
+       "       [5.1, 3.4, 1.5, 0.2],\n",
+       "       [5. , 3.5, 1.3, 0.3],\n",
+       "       [4.5, 2.3, 1.3, 0.3],\n",
+       "       [4.4, 3.2, 1.3, 0.2],\n",
+       "       [5. , 3.5, 1.6, 0.6],\n",
+       "       [5.1, 3.8, 1.9, 0.4],\n",
+       "       [4.8, 3. , 1.4, 0.3],\n",
+       "       [5.1, 3.8, 1.6, 0.2],\n",
+       "       [4.6, 3.2, 1.4, 0.2],\n",
+       "       [5.3, 3.7, 1.5, 0.2],\n",
+       "       [5. , 3.3, 1.4, 0.2],\n",
+       "       [7. , 3.2, 4.7, 1.4],\n",
+       "       [6.4, 3.2, 4.5, 1.5],\n",
+       "       [6.9, 3.1, 4.9, 1.5],\n",
+       "       [5.5, 2.3, 4. , 1.3],\n",
+       "       [6.5, 2.8, 4.6, 1.5],\n",
+       "       [5.7, 2.8, 4.5, 1.3],\n",
+       "       [6.3, 3.3, 4.7, 1.6],\n",
+       "       [4.9, 2.4, 3.3, 1. ],\n",
+       "       [6.6, 2.9, 4.6, 1.3],\n",
+       "       [5.2, 2.7, 3.9, 1.4],\n",
+       "       [5. , 2. , 3.5, 1. ],\n",
+       "       [5.9, 3. , 4.2, 1.5],\n",
+       "       [6. , 2.2, 4. , 1. ],\n",
+       "       [6.1, 2.9, 4.7, 1.4],\n",
+       "       [5.6, 2.9, 3.6, 1.3],\n",
+       "       [6.7, 3.1, 4.4, 1.4],\n",
+       "       [5.6, 3. , 4.5, 1.5],\n",
+       "       [5.8, 2.7, 4.1, 1. ],\n",
+       "       [6.2, 2.2, 4.5, 1.5],\n",
+       "       [5.6, 2.5, 3.9, 1.1],\n",
+       "       [5.9, 3.2, 4.8, 1.8],\n",
+       "       [6.1, 2.8, 4. , 1.3],\n",
+       "       [6.3, 2.5, 4.9, 1.5],\n",
+       "       [6.1, 2.8, 4.7, 1.2],\n",
+       "       [6.4, 2.9, 4.3, 1.3],\n",
+       "       [6.6, 3. , 4.4, 1.4],\n",
+       "       [6.8, 2.8, 4.8, 1.4],\n",
+       "       [6.7, 3. , 5. , 1.7],\n",
+       "       [6. , 2.9, 4.5, 1.5],\n",
+       "       [5.7, 2.6, 3.5, 1. ],\n",
+       "       [5.5, 2.4, 3.8, 1.1],\n",
+       "       [5.5, 2.4, 3.7, 1. ],\n",
+       "       [5.8, 2.7, 3.9, 1.2],\n",
+       "       [6. , 2.7, 5.1, 1.6],\n",
+       "       [5.4, 3. , 4.5, 1.5],\n",
+       "       [6. , 3.4, 4.5, 1.6],\n",
+       "       [6.7, 3.1, 4.7, 1.5],\n",
+       "       [6.3, 2.3, 4.4, 1.3],\n",
+       "       [5.6, 3. , 4.1, 1.3],\n",
+       "       [5.5, 2.5, 4. , 1.3],\n",
+       "       [5.5, 2.6, 4.4, 1.2],\n",
+       "       [6.1, 3. , 4.6, 1.4],\n",
+       "       [5.8, 2.6, 4. , 1.2],\n",
+       "       [5. , 2.3, 3.3, 1. ],\n",
+       "       [5.6, 2.7, 4.2, 1.3],\n",
+       "       [5.7, 3. , 4.2, 1.2],\n",
+       "       [5.7, 2.9, 4.2, 1.3],\n",
+       "       [6.2, 2.9, 4.3, 1.3],\n",
+       "       [5.1, 2.5, 3. , 1.1],\n",
+       "       [5.7, 2.8, 4.1, 1.3],\n",
+       "       [6.3, 3.3, 6. , 2.5],\n",
+       "       [5.8, 2.7, 5.1, 1.9],\n",
+       "       [7.1, 3. , 5.9, 2.1],\n",
+       "       [6.3, 2.9, 5.6, 1.8],\n",
+       "       [6.5, 3. , 5.8, 2.2],\n",
+       "       [7.6, 3. , 6.6, 2.1],\n",
+       "       [4.9, 2.5, 4.5, 1.7],\n",
+       "       [7.3, 2.9, 6.3, 1.8],\n",
+       "       [6.7, 2.5, 5.8, 1.8],\n",
+       "       [7.2, 3.6, 6.1, 2.5],\n",
+       "       [6.5, 3.2, 5.1, 2. ],\n",
+       "       [6.4, 2.7, 5.3, 1.9],\n",
+       "       [6.8, 3. , 5.5, 2.1],\n",
+       "       [5.7, 2.5, 5. , 2. ],\n",
+       "       [5.8, 2.8, 5.1, 2.4],\n",
+       "       [6.4, 3.2, 5.3, 2.3],\n",
+       "       [6.5, 3. , 5.5, 1.8],\n",
+       "       [7.7, 3.8, 6.7, 2.2],\n",
+       "       [7.7, 2.6, 6.9, 2.3],\n",
+       "       [6. , 2.2, 5. , 1.5],\n",
+       "       [6.9, 3.2, 5.7, 2.3],\n",
+       "       [5.6, 2.8, 4.9, 2. ],\n",
+       "       [7.7, 2.8, 6.7, 2. ],\n",
+       "       [6.3, 2.7, 4.9, 1.8],\n",
+       "       [6.7, 3.3, 5.7, 2.1],\n",
+       "       [7.2, 3.2, 6. , 1.8],\n",
+       "       [6.2, 2.8, 4.8, 1.8],\n",
+       "       [6.1, 3. , 4.9, 1.8],\n",
+       "       [6.4, 2.8, 5.6, 2.1],\n",
+       "       [7.2, 3. , 5.8, 1.6],\n",
+       "       [7.4, 2.8, 6.1, 1.9],\n",
+       "       [7.9, 3.8, 6.4, 2. ],\n",
+       "       [6.4, 2.8, 5.6, 2.2],\n",
+       "       [6.3, 2.8, 5.1, 1.5],\n",
+       "       [6.1, 2.6, 5.6, 1.4],\n",
+       "       [7.7, 3. , 6.1, 2.3],\n",
+       "       [6.3, 3.4, 5.6, 2.4],\n",
+       "       [6.4, 3.1, 5.5, 1.8],\n",
+       "       [6. , 3. , 4.8, 1.8],\n",
+       "       [6.9, 3.1, 5.4, 2.1],\n",
+       "       [6.7, 3.1, 5.6, 2.4],\n",
+       "       [6.9, 3.1, 5.1, 2.3],\n",
+       "       [5.8, 2.7, 5.1, 1.9],\n",
+       "       [6.8, 3.2, 5.9, 2.3],\n",
+       "       [6.7, 3.3, 5.7, 2.5],\n",
+       "       [6.7, 3. , 5.2, 2.3],\n",
+       "       [6.3, 2.5, 5. , 1.9],\n",
+       "       [6.5, 3. , 5.2, 2. ],\n",
+       "       [6.2, 3.4, 5.4, 2.3],\n",
+       "       [5.9, 3. , 5.1, 1.8]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['sepal length (cm)',\n",
+       " 'sepal width (cm)',\n",
+       " 'petal length (cm)',\n",
+       " 'petal width (cm)']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " iris.feature_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.target   # in this 0 is setosa, 1 is versicolor, 2 is virginica"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris.data[:,2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train,X_test,y_train,y_test = train_test_split(x,y,test_size = 0.2, random_state = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_mod = X_train.reshape(-1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test_mod = X_test.reshape(-1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_mod = y_train.reshape(-1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_mod = y_test.reshape(-1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = svm.SVC(kernel='linear')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(kernel='linear')"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train_mod, np.ravel(y_train_mod))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_mod = model.predict(X_test_mod)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9666666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(accuracy_score(y_test_mod, y_pred_mod, normalize = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Knn / K nearest neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "iris = load_iris()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(150, 4)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape       # rows and columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(150,)"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=4)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier(n_neighbors=4)\n",
+    "knn.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4,)"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([4,5,6,2])\n",
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2])"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.predict([a])   # array([2]) 2 is verginica"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Nikhil\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2])"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logreg.predict([a])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thank you"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/machine_learning/Seaborn/seaborn.ipynb b/machine_learning/Seaborn/seaborn.ipynb
new file mode 100644
index 0000000..3b03289
--- /dev/null
+++ b/machine_learning/Seaborn/seaborn.ipynb
@@ -0,0 +1,671 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x2275722e340>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = sns.load_dataset(\"flights\")\n",
+    "sns.relplot(x = \"passengers\",y = \"month\",data =a ) # scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x2275722e280>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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YHSyMMcbkPUtWxhhj8p4lK2OMMXnPkpUxxpi8Z8nKGGNM3rNkZYwxJu9ZsjLGGJP3LFkZY4zJe5asjDHG5D1LVsYYY/KeJStjjDF5z5KVMcaYvGfJyhhjTN6zZGWMMSbvWbIyxhiT9yxZGWOMyXuWrIwxxuQ9S1bGGGPyXs6XtRcRBX6hqm9OPncBzcATqnp1rtvPtuhgkKi/n3gkgruoBFdhEdFgkHB3N/FYFG95Be7SUsQx9c8BkQE/g+2dRPx+fFWVeKsqcHo8J7b7gwRa2gm2d1NQU0FhXQ3uooJsHB6Brj469zTR39xFybwqyuprKJ1XPak6YpEoHQdaaNl5BE+hl7oVDVTU12Slf6PpONzG4W0HCftD1K9uZO5p83C6ndOqs7u1mwPbDtB6qI3FaxcRCIXYt+0AtfU1rFi3lNr52TuegT4/O7bs5vlndzGvoY41G05nbv0cAIKBQbZu3s7TTzxHzZxqNpy3jsbFDVlr25hTRc6TFeAHVotIgaoGgZcARydTgYi4VDWak95NQiw0yMCB3cQjEQAGaaaoYTHdW7ei0UT3BoCKM9bhq6qaUhuRQICWR54g0ts3FKs6cw1lS5ck+hCO0PL4Znp2HRjaXrF8MXMvWo/T457ikSWEBgI8d+s9dO498etZeMEqGi9aQ0ld5gmrZecR7v7qb0ETz71FPq78yHVU1E8u6WWi43Abt37spwwODCYCAv/wyTfRuG7JlOsc6BngV1/6NXue3Ut5bTl9AT9/+fmdQ9sXLGvgP778XqrmVEy3+6gqd/3pfr77hZ8MxZasaOS/v/MRauqquP/uh/nP9312aNu8+jpuvPmrLGicP+22jTmVzNQw4O3Ay5OPrwNuPb5BRM4RkUdF5Nnk9+XJ+NtE5Dci8hfgrhnq57iiwcBQogLA4SDc3T2UqI4bOHiAeCw2pTbCPX0piQqga+sOIv4AAKGevpREBdC9az/hnv4ptTdcf3NXSqICOPz4DvxtPRnXERkM8+wfHxlKVAAh/yCtu49Mu3+jObzt4IlEBaDwyK8eIDIYnnKdLQdb2fPsXgDWXLyGu393f2qbu49wZG/TlOsfrvVoOz/55q0psX07D7J/90E6O7r56ue/n7LtWFMLO7btzkrbxpxKZipZ/RJ4g4j4gLXAE8O27QQuVtUzgU8Cnxu27Xzgrap62cgKReQGEdkkIpva29tz2PUTNB4f2Qd0lKQUj0RgRNmM2xilPo1Fh9rW6OhJcKrJcbhYJP3kVeNxYuHIKKVHF4/GCQ9PHknhQGhafRtLyJ/e1mB/kFh0aj9/gMiw43W6nYRGSXzh0NST4XDRaHSM+iNEIxEG+v1p20KDuflZGpPPZiRZqeoWoJHEWdXfRmwuA34jItuArwGrhm27W1W7xqjzRlXdoKobampydz1kOJevAERO9CEWw11enlauqKEBh3tqQ3Ke0hLElTo6W7xwAe7CxDUpT3kJnorSlO3eijI8ZSVTam+4krpKPMWp174qF8+jqCbz4S5vsY+VL12fGhSoW56b6ywNqxeBpMbWv+I8fMW+KddZ21BDcUUxAPs372fDxWembPcVeqlfPG/K9ae0Nbeay19xcUqsoNDHwiX11Myp5k1vf23KNrfHzdIVi7PStjGnElHViUtNpwGRAVUtFpFPAv8KbASqgA+o6tUi8lPgGVX9pog0AveraqOIvA3YoKrvnaiNDRs26KZNm3J2DMepKlH/AMHWY8QjYbyVNbjLK4j2DySG/iIRihoa8NXUpkyImKzBzi66d+wm0ttHcWMDJY0LcBcVndje1UPHll0MHGmhuGEu1WuX4assz8IRQs/hNvbd9ww9h1upWdbA/PXLKF9Yh9Od+eXNYJ+fw0/vYftdT+MtLuCMV13A3BUNOFzTm/QwmlgkxpHnD/LIrx5gsD/Ihleex9JzV1BYVjTxzuM4uvcY9//qfg5uP8hL3voSDu0/yqN3PknDafW86u1XsWTloiwdAbQ0tXLnn+7n739+gEXLFvLGG17DijVLAWhraef2P93Dr2/+M/UNc7nhfW/hrHPWIiIT1GpmCftFJ81ksqoHXquq3xCRjZxIVn8gMVvwdyLyKeBt+ZqsjtNYDFXFMewMKB6LQTw+5TOqkeLRGBqL4fSOnvQ0FicWDuP0eqY183A0sWiM8EAAl8+L2zf1pBvyD+JwOnH7svMzGU9kMEwsGp/WGdVI0UiUUCBEQUkBDoeD/l4/Pp8Htzc3x9PX04+vwItnlN95b08fXq8HX0H2js+cEixZJc3EbEAAVLUJ+MYom/4X+D8R+Xfg3pnqz3SI05n2CnI4neDM3pmDw+WEcc5ExOnAlaM3LqfLSUH59IcVvUUz98bq9nnIdgpxuV24yk78iZRM82xtIqXj/MzLykvH3GbMC0HOz6xmwkyfWRljzAyxM6sku4OFMcaYvGfJyhhjTN6zZGWMMSbvWbIyxhiT9yxZGWOMyXuWrIwxxuQ9S1bGGGPyniUrY4wxec+SlTHGmLxnycoYY0zes2RljDEm71myMsYYk/csWRljjMl7lqyMMcbkPUtWxhhj8p4lK2OMMXnPkpUxxpi8N6PJSkReLSIqIitmst1siceiRAeDxCMRNB4nGggQHRycUl0Rf4DwwAAai41ZRuNxQr39hPsGyNWKzhpXAp19+Dt70Xg82bcggY5eooOhadUdj8Xpa+uhv6MnJ/2Px+L0tHTT09o97fqj0SiH9xzl8J4mopFolno4uvaWTpoONROa5s83GwL+AIcOHKGjrfNkd8WYcblmuL3rgIeBNwCfynQnEXGq6tjv6jMgGvTjP3qYWMCPt3oOkd5+gi0tiNNJyeIlFNTV4XBN/OOMhSMMHD5C19btaDRGyaIFlJ++DHdRUUq5yECAji276NyyCxxC7frVVK48DVeBN2vHFBoIcvix59n7901oLE7jJWcwd81i9v3lIQa7+ympr2XJyy6guK5q0nUHuvt5/q6n2XH3MzgcDta+8jyWXXIGvpKCrPS9v6uPp//yBM/c9iQOp4Pzrr2IM644i4LSwknX1X6sg/v/9BB3/PIeYrE4l11zES+59lLmNtZlpa/HhQbDPHT343zviz+lr6efi196Pm9/33XUL5yb1XYytW/3Af73M9/msYc2Maeuho//z7/zoo3n4MrgdWzMTJuxMysRKQZeBLyTRLJCRDaKyIMi8gcR2S4i3xcRR3LbgIh8RkSeAM6fqX6OJh6JMHBoP7GAH3G5iQ4ECDY3gyoajdK3exfh3t6M6gp1ddH5zBY0EgVV+vcfov/g4bRyfQeb6Ni8A43H0WiM1ieew3+0NavH1bm3iV1/e5xYOEo8Fmf/vc/Stv0gkUDibLG/qY1dv7+fsD846bqPPLuP529/ing0RjQc4ZnfPkTLzvTjnKq9T+7mqT8+RiwaIxKK8NDN93J428Ep1bV9007+9NPbCQ2GiUai3PWb+3j2kS1Z6+txe7bv4wsf/ia93X2oKg/c8Si//vGfiEQiWW9rIv6BAJ//r2/w2EObAGhtaef9N3ycvbsOzHhfjMnETA4DXgPcoaq7gS4ROSsZPwf4D2ANsAR4TTJeBGxT1XNV9eEZ7GeaeCRMPJwYsnEVFhPq6EgrE8kwWQ12dKXFBg4eIRYKn2gvFqN7Z/qbRt/Bpky7nJGWreltdOw5SvHc6qHnwY4eQr3+SdUbi8bY8/C2tPiRZ/dOvpNj1P/8vc+lxfc+uXtK9T37yNa02FP3PUM4nN0kcvjAsbTYvX97mO7OzF472dTW2s6Tjz6bEovFYhw6kN3XmDHZMpPJ6jrgl8nHv0w+B3hSVfcnh/luBS5MxmPA78aqTERuEJFNIrKpvb09V31OtOV0QuKEj3g0grMgfShrtNho3EXpw1TuslLEeeJXIQ4HBTWVaeW8VeUZ9jgzJXPT2yisKiXcHxh67nC7cHk9k6rX4XRQ1VibFi+vrx6l9OQ5nA5ql6QP0VUvrJlSffMb04fh5jXOxeNxT6m+sZSVl6S3vaCOwkJfVtvJRGFhIZXVFWnx8orSGe+LMZmYkWQlIlXAZcAPReQg8EHg9YAAI6+MH38+ON51KlW9UVU3qOqGmpqpvUllyuHxUjivAYBYYICCujmJBJbkKirCU1aWUV2+mircZSfetMTlpOL0ZSnXu0SEypVLcA5LEu7iIkoXzp/uoaSoW70YX/mJa2XuIh9z1ywiOOyT/uIrz8NXmf4mOx4RYdklZ+AtOvEmXFRVSsO606bf6WT9Z1yxPuX6V2lNGadtWDal+s68aC2VteVDz4vLirn46gum2800y1YtZu2GlUPPXW4X7/7QWykuLc56WxOZM7eGj//3vyEiQ7GXXn0py1YsmfG+GJMJydUss5RGRN4FnKWq7xoWewD4O/BRYCVwCLgduFFVfyciA6qa0V/xhg0bdNOmTTno+QkajyVmAoZCONweUIgGAojTgbu4BKcv80/HkUCAcE8fGovhKSvBUzr6p9lQTx+DnT2Iw4GvqhxPDt7U/J299B/rRONxSudV4yn2EWjtItQfpKCihMI5lTjdU7vg3tvSTXdTGw6Hg4qGGkpqyrPa9+5jnbQdasPhEGoa51A+J/1MIVOH9zRxeM8R4rE4C5bV07h8YRZ7ekJnWxf7dh0k4A/SsGg+i5ctTEkYMykSjrBn134OHWiivKKM5SuXUFk19Z+hyYmT8+LIQzOVrO4HvqCqdwyLvQ/4Z6AZaCdxzepB4F9UNZ5vycoYY04CS1ZJMzJHVVU3jhL7pohsAT6gqq8fZfvMj40YY4zJS3YHC2OMMXnvpP73n6reD9x/MvtgjDEm/9mZlTHGmLxnycoYY0zes2RljDEm71myMsYYk/csWRljjMl7lqyMMcbkPUtWxhhj8p4lK2OMMXnPkpUxxpi8Z8nKGGNM3rNkZYwxJu9ZsjLGGJP3LFkZY4zJe5asjDHG5D1LVsYYY/KeJStjjDF5L2eLL4qIAr9Q1Tcnn7uAZuAJVb06V+1mSywcJhb0o6poJEY8EsFZWIC7uASHK/s/tnD/AOGeXjQex1tRhqe0FIBQTx/Bjm5QxVdVga+yLOttB7v78Ld0onHFWeAj0NWH0+2mrL6GwqrSKdcb8g/SdaiVgc4+iqtKqVw4B2+RL4s9T4iEI7QfaKXrWCcFJYXMWTKX4oriKdenqhzb30zLgRbcXjfzT5uHChzafYSgP8j8xnksWFqP0+XM4lGMrbO9mz3b99PX00994zyWnr4It8eds/ZCoTA7n9/Dof1HqKgsY8XqZdTUVuWsPWMykcuVgv3AahEpUNUg8BLgaA7by5pYOMTAoX2Iw0k0ECLU3j60rXTpMgrr6xGRrLUX6u2l+f5HiIfCAIjbxbxLXkRchQN/vodYMASAw+Nm0SsvozCLbxzBzl623XInoZ4BFlx2Ntt+eifxaAyAwuoyzrnhFRTXlE+63mg4wrbbn2TrbU8Mxda+4jzWvuJ8XJ7svuz2PLGLv37190PPF591Gle+95UUTTFhHdh2kB988EaikSgAl73xMh578BkO7jwEgMPp4ANf/X+sPW/V9Ds/ga6OHr788e/y1MPPDsU+9uV/49KrXpSzNu+540E+/L7PDj2/+LLz+fSX/pOq6oqctWnMRHI9DHg78PLk4+uAW49vEJFzRORREXk2+X15Mv6QiKwbVu4REVmb436miAUDxIIBnN7ClEQF0LdvL7FAIKvtBZqahxIVgEaiDBw5St/+pqFEBRAPR+jevg9VzVrbvYdaCHX3U7awjqZn9gwlKoBARy9d+49Nrd7mbrb+9YmU2NbbnqCvpWta/R2pr6OXe2+6PSW2/5m9tB9qnVJ94VCYu39+91CiAog7dChRAcRjcW7+xm8Y6B2YWqcnYd+ugymJCuA7n/sR7a2dOWmv5VgbX/ivb6bEHrz3MXbv2JuT9ozJVK6T1S+BN4iID1gLDH/32glcrKpnAp8EPpeM/xB4G4CILAO8qrplZMUicoOIbBKRTe0jEsp0xaOJNyqNx0bZGCceGyU+DZGB9De9aHCQcG9fWnywqwfi2UtWob5E2+7iAoI9/ent9UztDTkSDMGIbqoq4WHJNxuioQjB/mBafNA/OKX6IoMROo52DD0XESLhaFq5zpZOQoPhtHi2+fv8abGerj5CWf45HhcMDtLT3ZsW75+BxGzMeHKarJJJppHEWdXfRmwuA34jItuArwHHx1R+A1wtIm7gHcBPx6j7RlXdoKobampqstpvp68AAHE6EGfqdQlXcTEuX3avuxTNn5cWK5xTQ+nihrR4xelLEGf2fm1lC+oA6DvUQt2qRWnbKxfNnVK9JTVlFJQXpcQKy4spmcKQ4niKq0pZdNaSlJjD5aByfvWU6isqK+Lcl5079FxVKSwqSBv2vejlF1Belf3rhyM1LJqfdm3s/I0bqJ6Tm2tIc+pquHDjuSkxl9tF45IFOWnPmEzNxGzAPwNfZtgQYNJngftUdTXwCsAHoKoB4G7gVcDrgFtmoI8pXAWFFC1cQtTfT+myZbiKEtc+vFVVlK9chcPjyWp7vppqqs5cg8PtRlwuKlafTsHcORTNq2XuRetxeN043C7mnHMGpQvnZ7Xt4vpalr7qYjQex+Nx0njhGhxuJ96SQta96XLKF9ZNqd6iqlJe/K+vpnbpfBCoXTqfy97/aooqS7Laf4/Pw6VvfynLX7QScQiV86t47cfeSM2C2inXuf4lZ7HxdZfgcrsoqShh7sI63vf5d1E1pxKny8ml11zEVdddPiMTLBYtW8DnvvdR6hvn4XA4uPSqF3HDB96Mr8Cbk/YKiwr44CffyxUv34jD4aBxyQK++9Mvctry9A8yxswkyeb1j5SKRQZUtVhE6oHXquo3RGQj8AFVvVpE/kBituDvRORTwNtUtTG573rgL8BDqvr6idrasGGDbtq0KevHEI9EUI0DgsZiODweHM7cvUFFA0FAcRakfpIPDwRAFXdxYVYndgwX7g+gsTjuYh+DfUEcLge+0qKJd5yo3kCIkH8Qb7EPT47eYAGi4Sj+ngE8Pg8FpYXTri8ei9Pb0YvT5aQ0OSOyt6uPcChCRXUZLncu5yal6+3uIxgYpLK6HI83ux+WRhMKheho76KosJDyHMxANRnLzR/8KSjnyWpEbCMnktX5wP8B7cC9wJuPJ6tk2Z3A+1X1jonaylWyMsaYk8ySVVLOktV0iMg84H5ghSZObcZlycoYM0tZskrKuztYiMhbSMwa/FgmicoYY8zsN7MD7xlQ1Z8BPzvZ/TDGGJMZEXGqanb/p2eEvDuzMsYYkzsi8lkR+ddhz/9HRN4nIh8UkadEZIuIfHrY9j+KyNMi8ryI3DAsPiAinxGRJ4Dzc91vS1bGGPPC8iPgrQAi4gDeALQCS4FzgHXAehG5OFn+Haq6HtgAvE9Ejv+TXxGwTVXPVdWHc93pvBsGNMYYkzuqelBEOkXkTGAO8CxwNnBF8jFAMYnk9SCJBPXqZLwhGe8EYsDvZqrflqyMMeaF5/ht7eqAHwMvBj6vqj8YXij570aXA+erakBE7id5AwdgMNfXqYazYUBjjHnh+QNwJYkzqjuTX+8QkWIAEZkvIrUkbovXnUxUK4DzTlaH7czKGGNeYFQ1LCL3AT3Js6O7ROR04LHkXXIGgOuBO4B3i8gWYBfw+MnqsyUrY4x5gUlOrDgP+IfjMVX9BvCNUYpfNVodI+9QlGs2DGiMMS8gIrIS2Avco6p7TnZ/MmVnVsYY8wKiqtuBxSe7H5NlZ1bGGGPyniUrY4wxec+SlTHGmLxnycoYY0zes2RljDFmXCLyYxFpE5Ftw2JniMhjIrJVRP4iIqXJeKOIBEVkc/Lr+8P2WZ8sv1dEvimTWPrckpUxxpiJ/JTEHS+G+yHwYVVdQ+KOGB8ctm2fqq5Lfr17WPx7wA0k7i+4dJQ6x2TJahrikQjxaHTUbRqLEQsNorGp3TorFokQDQ6i8eyu5BwNhYkEBie1j6oSHggQDYUzKh8Jhhjs8zNTq1CHBoIE+wIz0tao7QdC+Lv7c3684cEwfZ19RCPpr7lIKEJPew+RUCQrbcViMTrbuwlO8rWSCz09fXR39pzsbpwymu+9543N995zsPnee+LJ72+cbp2q+iDQNSK8nMSNbgHuBl47Xh0iMhcoVdXHNPHH8jPgmkz7MOP/ZyUiAzP9n8/ZFo+ECXV3MdjRijidFNbNx11ShjgSuT8y0E//gYOEu7vwVFRSsmgR7uLMDznY3kn31u2E+/spXlBP2dLFk9p/1D5HowwcaaHl8c3EwhGq1y6nfPki3IUF4+4X6hmg+dmdtD6zC29pEY0vPpuyxrlDxzqcxuN07Gli522PEewZYOEFq1lw3koKKkqm1fexhIMhmjbv49k/PEI8Fmft1efSePZyvMXjH1O2xGNxDm87yIM/v4f+jj7OeOl61r7kTEqry7Le1pFdR7j9J3fStOsIqy5YxaWv30jtgloADu9t4o8/+ivbN+1k1TkruObtL6fhtPopt3X0cDN/vPl27v3rwyw8rYF3/Ot1rD5zRbYOJWP+gQD3//0RvvvVnxCNRvnH91zPFS/bSFlF6Yz35VSRTEw3AYXJ0ELgpuZ772HuZS++JcvNbQNeCfyJxJ0wGoZtWyQizwJ9wMdV9SFgPtA0rExTMpYRO7OaglBPN8GWJjQaIR4aZODQPqIBPwCx0CDdW7YQam9Do1FC7W10b3mOWCiUUd3h3j5aHnyEwY5O4qEwfXv207Vtx5hncJkKtHVx6PYHCXX3EfUHaXlsM717D4+7j6pybNN2mh56joh/kIHmTp6/5U4GWkZ+wErobWrnyRv/Qm9TO+GBIHvueoqDj2zN+tnhcW27j/LgD/5Kf1sP/s4+Hvu/uzm67WBO2hpN6/5mfvuZm2nd10yg189jv36QZ2/flPXj7TjWyY0f/iG7ntqFvy/Ak3c8xa+/8luCA0F62nv4+ge/y5P3Ps1An58n/v40X//w9+jp6J1SW6HBED/55q384Rd/o7e7jy1PPc9//uNnOLT3SFaPKRPPPPkcH/nX/+bIoaM0H23lsx/9Co89vGnG+3GK+RwnEtVxhcl4tr0DeI+IPA2UAMeHXpqBBap6JvDvwC3J61mjXZ/K+I/lpCQrEdkoIrcNe/5tEXlb8vFBEfm0iDyTvBA38x/pxhGPRgl1tqXFo/6BxPdAkNhg6tBJbHCQaDCYUf3hvn40Fk+J+Q8fJRqc3nCM/2h6nzu37iY6OPbQXrjfT/NTO1JiGlcCbd2jlu9r7kx7oz70yDYG+/xT6PHEDjy5My22697NxEf8/HKl43Bb2vFuvmMT/d39WW2n/UgbgRHDnAe2HaCrpYuWpjbajnWkbGs90k5rU/rvOxNtLZ08cMdjKbHQYJhD+5vG2CN37rjtvrTYr3/xJ2JTHFp/gVgwyfiUqepOVb0iuTDjrcC+ZDykqp3Jx08n48tInEkNP+WvB45l2l7GyUpELhCRN4rIW45/ZbrvFHSo6lkkLsZ9YIz+3CAim0RkU3t7ew67ktYu4nKnx11OABxO56j7jRVPK+dKL+dwu0cddpsMV4E3LeYuKkCcY9frcDlxF/nS4k7P6KPHLk/6z8VT7MPpzuzYJ6uoMn14sbi6DHFkPMFoWjyj/EwLywpxu7M7uu7xedJiTrcTt9eN15feBwDvKH3LhNvjprAofRi1oDD9dZBrdXNr0mLz5tfhmObfwiw31nDJ+MMoU5BcQuT4TXE/Dnw/+bxGRJzJx4tJTKTYr6rNQL+InJecBfgWEkOIGcnoty4iPwe+DFxIYv2Ts0kscZwrv09+fxpoHK2Aqt6oqhtUdUNNTfqLOlcS16jmpcZcblxFiTdOZ1ERBfNSh2EL58/HWZDZdRRPWRneyvKUWOUZq3AXjTyzn5yi+bW4Coa94YhQu2E1znHeWN2FBSy64tyUmK+ylOK51aOWL2uopagm9XrNyldeiGeUN79sWLhhGe6CE2/kTreLFZetYxKzYadlzpK5lM+tTIltfOtLKCid3u9qpLrGOladvzIl9pLrL6dqXhVzG+fw4tdckrLt8ms3MnfBnKm1Na+Gf/qPN6fEVq1bzuJljVOqbzouv+oSikuKhp57vR5e/+ZXzdjv9xT1UWDkbKNAMj5lInIr8BiwXESaROSdwHUishvYSeIM6SfJ4hcDW0TkOeC3wLtV9fi1g38mMYtwL4kzrtsz7kMmM5hEZAewUrMw3UlEBkhMV/yoqr4sGfsh8LCq/lREDgIbVLVDRDYAX1bVjePVuWHDBt20aebGslWVaNBPNOBHHE5chUW4fCfekGPhMJG+PqIBP67CItwlJTi9mX/SjfgDhLq6iQaCeCvK8FZU4MjCp/XB7l6CrZ3EIhEKa6soqKmc8IwtHo3Sf6yDgeYO3IU+SubXUlA59gVuf0cP3QdbCQcGKa+voayhdtyEOF3dTe2072smHotTs2QuVQun9iY95fabuzi2u4nB/iBzlsyl7rR5uHJwvL0dvRzeeZjO5i7mLqqjYXkDhSWJpNjb1ce+7QdpOdTC3MY6lqxcROk0JrUEAkF2bd3Lvp0HqZ5TxelrlzJn3sx9IBxuz879bHl2O7FYjDXrTuf01ctOSj9Ookln5uQki8+RGPo7DHw0B5MrZlymyeo3wPuSp3HTazCRrE4HHiIx9dEHbAY+faokK2OMmSF2Gpk07kdAEfkLidkaJcB2EXkSGJrWpqqvnExjIuICQqp6RER+DWwB9gDPTrbjxhhjXjgmGq/4cpbbW8WJGSMfAj40soCqNg57vAnYmOU+GGOMOcWMm6xU9QEAEfmiqv7n8G0i8kXggUwbEpF3A+8D3j/5bhpjjHkhy3QO6EtGiV01mYZU9fuqulJV75rMfsYYY8xE16z+GfgXYLGIbBm2qQR4NJcdM8YYY46b6JrVLSTmwX8e+PCweP+wefPGGGNmMRH5MXA10Kaqq5OxM0j8I3AxcBB4k6r2iUgjsAPYldz9cVV9t4gUAr8BlgAx4C+q+mEyNO4woKr2qupBVb2OxK0yIiRmBxaLSNZv32GMMSYv/ZTsLBHyZVVdAZwJvEhEMr6clNF/L4rIe4FPAa3A8RuvKbA204aMMcbk3tbv3pL2T8Fr/uWN0/qnYFV9MHnGNNzIJULuBD4xTh0B4L7k47CIPEPqvQLHlekEi/cDy1V1laquSX5ZojLGmDySTFQ3kVgaRJLfb0rGs+34EiEwxhIhIvKAiFw0ckcRKQdeAdyTaWOZJqsjwNTWHDDGGDNT8nmJEGDo5hC3At9U1f2ZNpbpTcz2A/eLyF9JvYPFVzNtyBhjTM7N6BIhwBUAIrIMeHkyHiKZJ1T1aRE5vkTI8Xvi3QjsUdWvT6a9TJPV4eSXJ/lljDEm/xwmMfQ3WjyrRKRWVdtGWyIE6FLV2PAlQpLb/hsoA/5xsu1llKxU9dPJhkoST3Vgsg0ZY4zJuY+Suqw9ZG+JkI1AtYg0Af9FYlb4e5JFfk/qEiGfEZEoiSnq71bVLhGpBz5GYkmRZ5JLvXxbVX+YUR8yvOv6auDnwPHFezqAt6jq85k0kmt213VjzCw16buu52I2YD7INFk9CnxMVe9LPt8IfE5VL8hp7zJkycoYM0vZEiFJmc4GLDqeqABU9X6gaOzixhhjTPZkPBtQRD5BYigQ4HrgQG66ZIwxxqTK9MzqHUAN8DsSF9KqgbflqE/GGGNMikyT1RIS/53sANzAizlxmw1jjDEmpzIdBrwZ+ACJ22vEJyibMREZUNXicbbfD3wguWLwKSMWDhEbDCIiOHwFON3j/2tadDBIdMCPOARXYRFOn29S7YX7+on0D+Bwu/GUleD0eie1v6oS6u4l3DuA0+fFV1mG0zu1f6eLBAbxt3UTC4UpqCqjsLp8SvUMF+jqY6C1G3EIxXWVFJSN+ZLJqd7mLnpbuvAUeCivr8FXXJCTdiLhCJ2H2+nv7KOkqpSqBTW4Pe6ctHVcx7EO2g634fa4qVtUR0lFSUb7dbd107T/GPG4Mn/xPKrrKifeKUu6O3o4sPcw4VCEBYvnM6+hbsbazsTgYIh9ew7S2txG3dxalixbhHeKf1cm82TVrqp/yWlPZoloMED/wT1oJAKAw1dA8cIluLyjJ6DIwABdz20mHkrcGMRVVETFmrW4CkfeMWV0wY5OWh58FI3GACicP5fqs87AVZB5whs40syhvz2IxhOfQypXLWXOuWtx+SaX9ML9Afb97VE6dx0CwOF2sfr6KyltmDOpeobra+7kyRv/zGCPH4CSuVVsePtVFNWUT7nOqWjd3cTdX/kt0VDi97pwwzLOu/5yCsqzO88oFo2x7Z7N/P3G24dil7/rZay9/EycLmdW2zquaU8TP/jQTQT6AgAsOWMx1/3nG6iYUzHufs2HW/n6h77L0QPNAFTVVfLBr76P+iXzctLP4VqPtvGFj3yLrU/vAKC0vIQv3vQJlq5cnPO2MxEJR/jDr//G5z/x9aHYx//n33nNG16Oy5Xp227+yMYSISPq+zOw+Hhdmch0GPC/ROSHInKdiLzm+FemjYxHRDaKyG3Dnn9bRN6WjbpPhlB351CiAogPBon0jX1bxWDzsaFEBRD1+xns7MiorVg4QufmrUOJCiBwtJlQd0/G/Y34gzTd98RQogLoen4Pg52Z13Fcf3PHUKICiEeiHLj7CaKh8Dh7jU1VOfLEjqFElWijk9btB6dU31SFAoM8det9Q4kK4NCm3XQcbMl6W13HOrn3R3emxO790R10H+vMelsA0XCUv998z1CiAtj33H4ObJt4/tSzDz83lKgAOlu6eOhvM7Mm69Zndg4lKoC+nn5+9aM/EYlEZ6T9iRw8cIQvffpbKbEvfOqbHNrfdJJ6NG0/JTtLhJDMHZO+sUSmKf7twAoS16uGLxHy+8k2mC0icgNwA8CCBfmxtJbG40T96b+DWNA/SulE+XBPeiKL9PVl1F48EiHSk142NjiY0f4AsXCYqD+YFo8GMq/juMhAIC3mb+kiOhjGNYXhj3gsTveBY2nxniNtk65rOiLBMF1H2tPiwd7Rf6/TEewLEI+ljrTHo3GCfek/22wYDIZo2pX+BtrWNPEHpn3PH0yL7dy8h1gshtOZm7PA4w6P8qa/c+segv4g7vLMhjBzqbuzl+iwD5EA0UiU7q6enLd92799O+2fgq/+2ntP+hIhACJSTOLmtjcAv55MHzI9szpDVTeo6ltV9e3Jr3dMpqFsU9Ubk33aUFNTczK7MkQcDjzl6UMn7pKyMcv75tSmxb1V1Rm15/R6KJyXPk7vLsn8mo6r0EdBbVVa3DOF60K+yvTjrFy+EHfR5K7BHed0OZl75rK0eO3po936LHd8JYU0rFuSFi+tG3+YbCpKq0vxFqYOv3qLfJTUjP4amq7CkgLO2Ji+2s+C5Q2jlE515oXp+11wxTk5T1QAp5+R/rq45MoLKDlJ1zNHmju/luKS1CHiktJi6uZPfUg8E8lElbZESDKebVNZIuSzwFdI3AJqUjJNVo+LyMrJVp6h6Ih+TO2dLU94yirwlJ948/dW1+IuHvuTnq+mFl9dMuGIUFjfgLciszdBh8tFxaoVeKsSF7XF6aTqzDV4y8sz7q/L62X+xnPwJhONw+Om/sXn46vMvI7jiudWsfjK83Akr62UNMxhwSVn4pzGGP3ctYuZv2E5CIhDWHTxGVQvzXi9tqxweVyc+ZoLqVk6P/nczbnXv5iqhdl/4ymvq+RV//k6iisTr5niqhKu+c/XUT7B9aOpcjgcnHf1+Zx+7ukAON1OXvq2l9K4cuIPBKvOXsFLX38ZDqcDEeHCl53P+kvW5aSfaW2vW85b3vM63O7Ea+v8jRt42bUvJnm/uZOuYeF8vvaDz1I7J/HBc05dDV+/8b+pb5ib66bzdokQEVkHnKaqf5hKY5nebmkHienrB0jc+l1I3NB2WgswisgAcDrwEIlTSh+wGfi0qv4009mA+Xa7JY3HiIUTvzenx4s4xv9MEI/FiAWDIIKroGDC8iPFwhGigQAOpxNXcdGU/mCjgyEi/QEcXjfe0ql/OlVVBrv7iUWi+MqKJj1JY9S+haMEu3pBHBRVlQ4lw5kWCgzi7+jD5XVTUlue0zfG/s5+An1+isqKhhJXLoWCIbpaunC5XFTNq8LhzOw1GI1EaT3ajsbj1M6vwTODs91i0RjHmlqJRqLUza+loDD/Pue2tXbQ1dFNZXXFUOKapEm9yG77t2/Hx9hHr/7aeyf3xjKyI4lhwNtGmxSRXCLkF6p6zijb7icxm/xsEsOEYRKXoGqBR1V1YybtZ/qRd+SFtWlLLsAVUtUjIvJrYAuwB3g2223NNHE4cfkyn9bscDpxFE89QTg9bpye6Q0TuXzerCQWEaGgsnTigpPg8rgoqUsfqpxp3kIf3gUz84ZYUlVCSdXMXXvxFniZu2jyn/pdbhfzG3N+tjAqp8tJQ2PuZx5OR+2c6qkmqanK2yVCkicd30uWaSSR+DZm2l6mS4QcmrjUpK0C9iXr/xDwoVHa3ZiDdo0xZrbK2yVCptM+ZDgMmG0i8m7gfcD7VfWu6daXb8OAxhiTJZMea87FbMB8cFKSVbZZsjLGzFL5MWMkD0zrgpsxxhgzEyxZGWOMyXuWrIwxxuQ9S1bGGGPyniUrY4wxec+SlTHGmHGJyI9FpE1Etg2LnSEij4nIVhH5i4iUJuONIhIUkc3Jr+8P28cjIjeKyG4R2Skir820D5asjDHGTOSnZGeJkI+RWBNrGbASeCDTDpx6q4AZY4wZ05de/Zm0fwr+4B8+mRdLhJC4+e2KZJ1xILPF+7AzK2OMmTWSiSptiZBkPNsmtUSIiJQnt31WRJ4Rkd+ISMZLF1iyMsaY2SNvlwghMZJXDzyiqmcBjwFfzrQxS1bGGDN7jLVsetaXU1fVnap6haquB27lxI3JQ6ramXz8dDK+DOgkcVPd4+tZ/QY4K9P2LFkZY8zsMdZSIDlZIiT5PW2JEBFxJh8PXyJEgb+QuHs7wIuB7Zm2Z8nKGGNmj4+SvmR8tpYIeQxYLiJNIvJO4DoR2Q3sBI6RukTIFhF5DvgtqUuE/CfwKRHZArwZ+I+M+2B3XTfGmLw16buu52I2YD6wZGWMMfnLlghJsmHAHNF4nMS/EUyifDzz8iPFYzFy9cFDVYnHYtOqIxbNvH/xWGza7U1FLBKd8TaPi8fixKIzf8wjxaKxrPcjEo7k7LU5llgsRmQGfp/hcJj4NP5uTeZy/k/BIvJqEksen66qO3Pd3smmsRgRfz+DHa2Ag4KaObiKihHH6J8L4tEo4Z5u/EeOgMNBccMCPOXlY5YfKTo4SPBYK337D+IuKaL0tCX4qiqydjyB1k46t+4i1NNP1aqllCych6vQl/H+g939dGzfT8f2g5QurGPOumUU1Y7ev1g0Rte+o+x/YDPxaJzFl5xB1Wn1uLzubB3OqHqbu9j32HaOPref+jMWs/iClZTVVea0zePisThHdx3h6b88QaDXz1kvP4fGM5bgK878Z5wNoWCIvZv38eBvH8LtdXHxtRezePUiXJ6pv0V0tnbz9APP8vDfHmPR6Qu57NWXsHBZw8Q7TkMsFmP75t387me30dPdxzVvvIr1F5xBSWlRVtvp7OjiwXse57e3/JnGxQu47q2vZvW607PahkmV82FAEfk1MBe4R1U/lYs28mkYMNzXw8DBvSmxksXLcReXjFp+sKOD7i3PpcQqzzwLb0VmCadn5x66tjw/9FycTua9+GK85WWT7Hm6YEc3+35/Fzrsk3bdBWdSk+EfZSwcZtcfH6Rr56GhmLeihLVvfTneUd48Ovc28dh3/pgSO/ufrmbOysYp9T8Tg/0B/v7V39FxoGUoVr1kLpe//7X4Sgpy1u5xx3Yf5daP/oR47MSn85e9/xpWXbI2520P9/xj2/nxx38y9FxE+OevvIslZyyZUn3RSJRbvvlb7vr1vUOx4rJiPv2jDzOnoXba/R3Lji17eP+bP55ydvjhL7yPy19xcVbb+fH3b+Hrn//B0POCwgJ+8YfvsnTF4qy2gw0DDsnpMKCIFAMvAt4JvCEZ2ygitw0r820ReVvy8cuSNzd8WES+ObzcqUBVk2dUqUI9XaOUTgz9+Y+kzygdbEuvYzTRQJCenbtT64zFCPf0ZrT/RAY7ulMSFUD7M9uJ+EdONhpdsKs/JVEBhLr7CbT3jFr+6DN70mIHH3wu5Y0823pbulISFUDHvmb6Wkf/nWXb4W0H0o7vyd8/Qsg/OCPtQ+Js5KHfP5wSU1Wee2DLlOvsaO7knt+l3vZtoHeAI/uOTrnOTGx+YlvaMOavfvRH/AOZvWYz0drSzo++c3NKLBgIsmvH3jH2MNmQ62tW1wB3qOpuoEtExvwHMBHxAT8ArlLVC4Ga8SoWkRtEZJOIbGpvb89mn6cl8S8HqRxjDemJIA5neniU2Nj7p9ed6RDihNWPVbdk9mFPHKOXGyvudKcPOTk8biTD9qZirN9Ntn6GE3G50o/Z6XaN+TPKFY8vfajVPY3hV3E4cLhG+VsYJZZN7lGGLd1eN44svoYcDgceryctPtrv0mRPrv8irwN+mXz8y+Tzsawg8Y9jB5LPbx2vYlW9UVU3qOqGmppx89qMERF8NXNGBnGXjz6kJyIULWhIK++rzWyYxFXgo2J16pCcw+vBk4UhQABfTQVOnzclNufctbgLMxse81WWUrd+RUqseF4NhTXlo5afd+ZSxDnsJSmw6KK1OX3jLp1bSf261KGbhjNPm7FrVg2rG3GPSBQXvP5iPAXeMfbIPqfTyUWvuSjlQ4HT5WTtxVMfiqyZV8Wr3nZVSqy2voYFp9VPuc5MrDtnNb4R11Svf/e1FBRlb0i3praK9/7HO1JiFZVlrFi1NGtt5JssLhFyXbL8FhG5Q0SqM+5Drq5ZiUgV0AS0AQo4k9/fBHxEVV+WLPdD4GHgOeDrqnpJMv5K4AZVvXqitvLpmpXG40QDfsK9XYjDgbu0Aldh0ZhnBxqPE+7rZbCtDXE48NXU4i4tzfhsIhaOMNjRib/pGO7iIgrn1+Ety06yAgh29tB/4Cjh/gFKF9VTNK8Gpyf9U+VYQv1+eg82073vKCXza6hYUk9BZemoZTWu9BxupWXLPmLRGHPXnUbFgjk4XBmeaU7RQEcfx54/SOuuJuasqGfeqoUUV2XvZziRln3H2PPELoJ9AZZfcDrzljdM66xmKqKRKId3HOa5B7fg8rhYe+EaGlY0jD0qkIH+nn52PLObZx/eQsNp81l3wRrmNc7NYq9Ht/v5/Txy75P0dfdx0UvOY+WZy/H5spv8+/sGeOapLdxzx0M0LJzHxstflIvrVZAn16xE5GJgAPiZqq5Oxp4CPqCqD4jIO4BFqvqJ5N3ZbzteblgdLhL/PLxSVTtE5H+BQKZzGXKZrN4FnKWq7xoWe4DEbTl+TuL28j5gM/Bp4FfAbuAiVT0oIjcDZadasjLGmCyadLK6/twb0v4p+BdP3DjtfwoemYREpI/Ee7SKSANwp6quHCdZuUkkqw3Jfn0PeEZVb8yk/VwOA17HiRsWHvc74I3Ar4EtwM3AswCqGgT+BbhDRB4GWoHszBQwxpgXgGSiSlsiJBnPtkktEaKqEeCfga0kz7CAH2XaWM6uCKrqxlFi3xz29EOj7Hafqq6QxBjYdwA7XTLGmMyNt0RItm+59A7gmyLySeDPpC8R0iki64E/isgqIEgiWZ0J7Ae+BXwE+O9MGsu36Sv/JCJvBTwkzrh+MEF5Y4wxJ8zoEiHAFQAisgx4eTIeAkLJx0+LyPElQiQZ25fc59fAhzNtL6+Slap+Dfjaye6HMcacog6TGPobLZ5VIlKrqm2jLRECdKlqbPgSISTmKKwUkRpVbQdeAuzItL28SlbGGGOm5aMkrlkNHwrM1hIhG4FqEWkC/gsoFpH3JIv8ntQlQj4jIlEgxrAlQkTk08CDIhIBDgFvy7gPdtd1Y4zJW3kzG/Bks2RljDH5Ky/+zyof2BIhxhhj8p4lK2OMMXnPkpUxxpi8Z8nKGGNM3rNkZYwxJu9ZsjLGGDOuySwRkty2Nrnt+eR2XzK+Pvl8b3KB3YxnO1qyMsYYM5GfAleOiP0Q+LCqriFx0/IPwtBSIL8g8c/Aq0j8M3Ekuc/3gBtI3NVi6Sh1jsmSlTHGzCJrF17yxrULLzm4duEl8eT3ad9xXVUfBLpGhJcDDyYf3w28Nvn4CmCLqj6X3LczeeuluUCpqj6miX/w/RmJ1eQzYsnKGGNmiWRiSlsiJBsJaxRjLRGyDFARuVNEnhGR4ytszCexIO9xTclYRixZGWPM7DHeEiHZ9g7gPSLyNFDCiSVCXMCFJFaFvxB4tYi8mNHvxpHxLZTsRrbGGDN7nPQlQkicMT2gqh3JbX8DziJxHat+WBX1JBZhzIidWRljzOwx1lIgOVkiJPk9ZYkQ4E5grYgUJidbXAJsV9VmoF9EzkvOAnwL8KdM25uRMysR+RiJ5exjQBx4l6o+MRNt54qqEgsNotEoDrcbp9c3fvl4nGggQDwSwen14ioceaY+tsjAANFAEKfXi7ukGHFM/BkjHo0S6u4jFoniLS3GXZx5e6pKsLOXyEAQd3EBBVVljDfDVOOKv6OHwb4AvtJCimrKxy2fDfFYnL6WLgb7gxRWFlNaW5HV+v09A3Qf68LldVE5rxpPgSer9Q93aPcROpo7Ka8qpWFZPR5P7toaTUdbF8cOt1BYVED9onn4fN6s1h8JRziw7zB9vf3Ma5jLvPlzslq/SXHSlwhR1W4R+SrwFIlhvr+p6l+T5f6ZxMzCAuD25Fdmfcj1XddF5Hzgq8BGVQ2JSDXgUdWMT/8mMtN3Xdd4nFBPJ4Gjh0EVcTgpXrgYd0nZmOWDzc307t6VKO90Ur5mDb7KqgnbCrS00frYk2gkCg4H1evPoGRBPeJ0jrlPdDBE+zPb6dicWNfMXVzIwpddTEF15cTHpkrnjoPs/uMDxKMxHG4Xy665hKoVC0dNQBpXWrbu49mb/048EsXpcbHuTS+hbs3inCWsWCTKvke38/jP7iYei+Mp9LLxPa9k3qrGrNTfcaSdP3/pN3Qe6QBg9YvXcfGbLqWooiQr9Q+36f5n+cFnfkrQH8TldvHG/3ctF199Ab6i8T/8ZMueHfv55Hu/SHtLJyLC697xKl7/zldRWpadYw34A/zyZ3/kW1/6IbFYjMqqcr7xw89xxlmrslL/C8Ck/4iSkylSlgjZcuiBU36JkJkYBpwLdCSXOkZVO1T1WPKfwx4QkaeTs0bmAojI/SLydRF5VES2icg5M9DHSYmFBgk0HYJkotd4jIEjB4iFw6OWj/r99O7aeaJ8LEbv89uJDQ6O207UH6DtiU2JRAUQj9Ox6VnC/QPj7hds6xpKVACRgQAtjz1H7Hg94+3b2TeUqADikSi7//gAg119o5b3d/QMJSqAWDjK5pvvxt/eM2FbU9XT3MmjP72TeCwOQDgQ4sEf/BX/GH2cjFg0xqY/Pz6UqAC23bOZo7uaxtlrao4eaObHX/gFQX8QgGgkys+//isO7TmS9bZGEwwM8sOv/IL2lk4g8UHlVz/6I3ueP5C1Nnbv2MfXv/ADYrHE66mrs4fPfPjL9HT3Zq0Nk2rLoQdu2XLogcYthx5wJL+f8okKZiZZ3QU0iMhuEfmuiFwiIm7gW8C1qroe+DHwP8P2KVLVC4B/SW7LK/FIelLSaBSNRkYpzahJKR4Jj5ncjosOhoiHRpRRiAaC4+4X7venxfzH2ogNhsbdDyA84B9KVCf6GiXUHxi1/GCvfyhRHRcLRxnsG718Nvg7+9PmEA32BQj2Tr/NUGCQg5v3pcXbDrROu+6Rutt66OvuT4lpXOlsGfnvLLnR19vPc5u2p8Vbm9uz1saxo21psT279tPTZcnKTE7Ok5WqDgDrSfzXcjvwK+BdwGrgbhHZTOLi3PBZIrcm930QKBWR8pH1isgNIrJJRDa1t2fvjysTDrc7LSYuF+Ia/RKg05c+pONwe3BOcG3C5fPi8I4oI+AqGH+IyFOSfn2qaG4NzpF1jbZvcSEOV+oQo8PlxFNcMGp5X2kRDveI8m4XvtLMr5FNVlFlSdrgiK+0kIIstOkt9NF4xuK0eG1j9q+zVNSUUVJenBITh1A1Z+Lh2mwoLS1h7frT0+Jz5lZnrY2582rTYqctW0R55ehD5saMZUZmA6pqTFXvV9X/At5L4j+dn1fVdcmvNap6xfBdRlYxSp03quoGVd1QU1OTw96nc3oLKJy/EI5fk3E4KG5YhNMz+oVpV2EhpctXDJUXp5OylStHTWIp+xUVUnvu+hNJ0CFUr1+Hp3T86wkFNVVUnbFiWD0F1J2/DqcnPcmm7VtVxrJrLhlKWA6Xk2XXXEJB1ehvLkU15ax74+VDCcvhdnHmmy6nqLp8wramqmxeFee/9QoczsTL1+3zcNENL6eoqnSCPSfmdDnZ8MrzqJh/4nriqkvPYN7y+nH2mpr5i+fxzg9fj6/QO9T2m973Dyxc3jDBntlRUOTjn/7jzVQnk6OI8A9vfyWnrUxP1lO17PQlvO9D/4QjOSmovKKMT37hA5RXWLIykzMTEyyWA3FV3ZN8/t9AJYn5+W9W1ceSw4LLVPV5Ebkf2Kmq7xaRC4HvJe89NaaTsaz9idmAkcRZUqazAcNhnD7f5GYD9g8QDQZxeL14MpwNGItGCXf3EQtH8ZQV45nCbMBwfwBPSWFmswHbexjs8+MrK6Kouhxx5H42YG9LF4N9AYqqSimtLc9q/f7uAbqbu3B5XFTOr8JTkN0ZcsMd2nWY9uRswAXLG2Z8NmB7ayfNh1soLC6kYdE8vFmeDRgOhTm4/wi9Pf3Mb6hjXn1dVuuf5WxZ+6SZSFbrSVyfKgeiwF4SQ4L1wDeBMhJT6L+uqjclk9VjJObmlwLvUNUnx2vjZCQrY4yZAZasknL+f1aq+jRwwSibOoCLx9jtd6r6kdz1yhhjzKnE7mBhjDEm7+XdvQFVdePJ7oMxxpj8YmdWxhhj8p4lK2OMMXnPkpUxxpi8Z8nKGGNM3rNkZYwxJu9ZsjLGGJP3LFkZY4zJe5asjDHG5D1LVsYYY/KeJStjjDF5z5KVMcaYvGfJyhhjTN6zZGWMMSbvWbIyxhiT9yxZGWOMyXuWrIwxxuS9GVt8UUTqge8AK0kkyduAD6pqeIzy7wduVNXATPXxuGjQT7ivF41EcJdX4C4sQhxOAOLRKOGeHgY72nEWFOCrqsZdXDyJuoME2zoItrbjq6qgYE4t7uKijPcP9fYzcKSZQFsXJQ11FM2bg7uoYNSygY4euvc2EWjvpmJpA2UL6nAX+tLK9Ta107rtACF/kLlrFlPeWIfL4864TxMZ7A/SuvsITc/tp7y+hvo1iyibWznpelr3N7P3yd0E+wIsPW8F85bX4/ZOv59drd3sfWYP+7ceYPHaxSw98zQq5lQQj8fZv+MgT9//LJFIjLM3nsmS1YtwubL/Z3Ngz2Eev/9pWo62ccFlZ7P6rBUUFRdmvZ3hurt6ePqJLTx0/+MsXbaICzeeS+OSBTlt05ipElXNfSMiAjwBfE9VfyIiTuBGoEtVPzjGPgeBDaraMVH9GzZs0E2bNmWlr9FggL59OyEeH4oVLzwNT1k5AIFjx+jduWNom8PtpvKs9biLJk448WiMzi3b6N97YCjmrSxnzovOw1WQnkRGigSCHPrbgwTbOodilWuWMff8M3G4nCllB3v62frz2wl19w/FFl62gfoXrSXx60joPdrBo9/6HbFQZCh29jtfzpzViybsTyY0rmz96+M887uHh2Ilc8p56YdeT3FVacb1tB1o4ZaP/pTI4InPNq/+6Os57ezl0+pfcCDIrV/8Jc8/un0otubC1bz+Q6/n6IFjfPZdXyIWjQEgDuEj3/53Vq6fXpsjHTlwlPe/+RP0dvcNxT74P+/hpddcmtV2hovH4/zoOzfzrS//cCi2oLGem275CnPn1+WsXTNpMnGRF4aZGga8DBhU1Z8AqGoM+DfgHSJSJCJfFpGtIrJFRP6fiLwPmAfcJyL3zVAfAYgM9KUkKoBg2zHisRixUIj+/ftStsUjEaID/WQi4vfTv+9ASizU1UOkP7P9Q129KYkKoGvbHsJ96fv7W7tSEhXAkYc2M9gzkBLr3NuUkqgAdt/9FJHQqCe8kzbQ2ctzf3k8Jdbf2kP3kbZJ1XN426GURAXw2K8fJBwMTat/bUfaUxIVwNaHt9Hd0sXjf980lKggkXjv+vW9xEe8PqZrz/b9KYkK4CffvJXuzp6stjPcsaYWbvr2z1Nihw82sXvn/py1acx0zNQw4Crg6eEBVe0TkcPAPwKLgDNVNSoilaraJSL/Dlw61pmViNwA3ACwYEEWhy5GOdPUeBxQQJOPR27P8OxUNVHNFPcftZzqqPFRY7EYaGr/48PejI+LhaOQ6TFNQONKPJb+MxstNp5oJJIWi4SixKfZz3gs/fgh0e/QKIkwFAyN+jucjugov4NwKEJskj+jyYjF4kSj0Yz6Ykw+mKkzK2H0P3EBLga+r6pRAFXtyqRCVb1RVTeo6oaampqsddRVVMLIM29fTR0Opwun10fxgoUp28ThyPialauokIJ5dSNiBXhKM9vfW1mKuyR1uLFkUf2o+xfVVuDyeVJidRtW4i0rSYlVLZmPOFJfBksuOwt3gTejPk2kqKqU5RvPSIl5i3yU11dPqp4FqxsRR+rv5dzXvAhf0cTDp+Opqa+hfll9SqxhRQPlteWc/5Kz08q/5B8uxeHM7p/NaSsW4R3xu3rDO6+hunby1/UyNa9+Dte+8ZUpsbLyUpYuz87wrzHZNlPXrC4HPqmqFw+LlQIHgAeA76rq30fsc5CTcM1KVYn6BxjsaCUejeCrqsVdUoYjeVE9Fg4T6uggcLQJZ2EhRfUNeMrKMq4/MuBn4MhR/EeOUlBTTcnihXjKMr92E+zsoXvHPvzH2ig7bQFlpy3AW1oyatmBlk6an9rOQEsnc9Yuper0RrylqclO43G6DjSz/4HnCPUHWHTRWmpXLBh1IsZU+bv6OLRpD/seeZ7KxjmsuGwdVQvnTKqOeCzO0Z1H2PSXxwn0+ln/8nNpXLcYX/Hok0smo+1IO5vufpqdT+zg9PNWsuHys6hpqCESjrDr2T387Za7iERiXHXd5axcvwJfYXYS+XA7tuzhD7/4K0cPNXP166/gvEvWU1FVnvV2hms51sY9dz7In397J6vWLucfrn8Vp69amtM2zaTZNaukmZxg8RTwTVX9WXKCxfeBPmAPcDnwhhHDgFuBV6rqgbFrTshmsjpONQ5K2lnH0PZYDByOlMkKkxGPxhDn1PZXVeKxGM4MZqWpKvFoDKd7/LLxWAyNg9PtHLfcdEQjEZxOV9oZ0mTEojE0rrg82R/BjoQio84ujEajaBzcOWhzuFgsRiwaw+P1TFw4iwYHQ3g8bhxjvNbNSWXJKmlGXp2ayIivBv5BRPYAu4FB4KPAD4HDwBYReQ54Y3K3G4HbZ3qCxXEijjETFYA4nVNOVAAO19T3F5GMEtVQ2QkSFYDD6cxpogJwud3TSlQATpczJ4kKGHMavMvlynmiAnA6nTOeqAB8Pq8lKpP3ZuTMKtdycWZljDF5wM6skuzjlDHGmLxnycoYY0zes2RljDEm71myMsYYk/csWRljjMl7lqyMMcbkPUtWxhhj8p4lK2OMMXnPkpUxxpi8Z8nKGGNM3rNkZYwxJu9ZsjLGGJP3LFkZY4zJe5asjDHG5D1LVsYYY/KeJStjjDF5z5KVMcaYvJfTtbpFRIGvqup/JJ9/AChW1U/lst2JaDxGLBwGwOnxDi1fHxscJB6J4PB6cXqmtrx4PBIlEgggDgfuoqKMl3GPx2KE+wZAwVNahMPlItQ7QCQYwltSiLuoYMI6YpEogc4+RITCqlIcrqkvUx/oGSDYF6CgtJDC8uIp1xOLxOhp7UIVyudUTGtJ+oHuAXq7+iguKyIcjhD0D1I5p4Li0qIp15mp/t5+2lu6KCwuoG5+7aT2DYVCHD3cjDgc1C+Yi9vtzlEvjZm9cpqsgBDwGhH5vKp25LitjMTCIYItxwj3dAKCr7oWb3Utkd4+enfuIB6J4CwooHzlKjxlZZOqO9I/QOdz2wgca0EcDspXLqd0ySKc3vETX8QfpP3Z7XRu2w1xpXLtMrzV1ey77REigUF8VWUsv+ZiSsZ5kwx09bH7jidp2rQTEQeLLl7L4kvPxDeFN/LmHYd56Ka/Eejqp7CyhItueBlzVyyYdD3+7n6e/ONjPPPXJ1FVVm88gwvecAmlNZP7uQIc3nGYmz9/K06Xk5WXrObPP7udwECQxuUN3PCJt7Ngaf2k68zUvp0H+dLHv8PeHQcoLi3ifZ/4Jy56ybkZJZ3moy1856s/4S+/uxOn08Gb3nEtb73h9VTXVOWsv8bMRrkeBowCNwL/NnKDiCwUkXtEZEvy+wIRKRORgyLiSJYpFJEjIpK1j6Lh3p5kogJQBjtaifr9dG/bSjwSASAWDNLz/DZioVDG9aoqffsPEjjWkngej9O9bQeDnV0T7jtwtJXOLbsgrgA4nG52/e4+IoFBAAY7e9n5u/sIDwTGrKN5y36antoJmmh7//2b6dzTlHH/j+tv7+G+b/2RQFc/AIGufu771p/ob++ZdF2Hthxg058fJx6Lo3Fl672b2fPEzknX09fVx88+83M6jnawZuMafvnd3xMYCAJwcNcRfvqlmwn6g5OuNxMD/X6+/ukfsHfHgcTzPj+f/9A32L/rcEb733PHQ/z5t3egqkSjMf7vxl/x1GObc9JXY2azmbhm9R3gTSIy8uP0t4Gfqepa4Gbgm6raCzwHXJIs8wrgTlWNZKMjGo8T7klPHtFAAFRTYrHBwUklq1g4jP/I0bR4qKt7wn37D6YmlVg0hsbiqfX0DBDq84/edixG87O70+KtOw5N2PZIAx19hAOpxx32D+Lv7J90XfueSu/Tjoe2EYvGJlVPb3sv3W09AAwOpv9Odj+3j+6O3kn3LxNd7d3s2LInJaaqNB9pmXDfcDjM3/7097T4g/c8lrX+GfNCkfNkpap9wM+A943YdD5wS/Lxz4ELk49/Bbw++fgNyedpROQGEdkkIpva29sz6os4HLiK0ofFnF5velmnE4cr81FSh9OFp6I8Le4umfh6T0Ft6pDQaO06vW5cBen9BHA6nVQsmpsWL6+f3LUVAF9JAeJMfVk4nA68xRNfMxupbun8tNj8FQ04J3ktraCkEG9h4tjdo1xLrJpTSeEU+peJopIiqudUpsXLqyYeynS73azbsDotvmrt8qz0zZgXkpmaDfh14J3AeBdQjp/a/Bm4SkQqgfXAvaMWVr1RVTeo6oaampqMO+KtqEZcJ0YVHV4fzqIiihobU8qVrViBq7Aw43odLiflpy/D4TlRt7eqEl/1xNcmShvn46koHXo+2NVF/YvWniggwmkvfxEFw8qM1HDO6fjKT/x4i+sqqV25MOP+D/WlrpJzrrs0JXb2Gy+jrK5i0nUt2bCUqvrqE32qLmH1ZesmXU/1vCqu/bfXIg6hec8xzr1s/dA2l9vFOz96fUbJYyqqair490+/G5f7xAeIq1/3EhYvb5xwXxHh1a97OXPqTrw+lyxr5KLLzstFV42Z1URHDH9ltXKRAVUtTj7+XxJnSj9W1U+JyJ+B36jqz0XkbcCrVPXVybK/AQaBflX9l4na2bBhg27atCnjfsVCIWKhIIjg9Bbg9HiIR6NE/X5ioRCuggJcRUVDswQnI9LvJ9zfhzideEpLcRX4MtovPBAg1NWDquKrLMfhcRNo7yE8EMBXXkJhTTkO5/hnJIGuPvpbuhCHUFJXRcEUZ/FFwhF6j3bg7+qnqLKU8vlVuDxTu2zY39lHx+E24nGlekENZTXlU6onGonSeqiVrpZuSqtLCQSC+PsD1NXXMm/RXBxT+F1lKh6Pc2hfE0cPN1NWXkrj0gWUTGLiyrGmFvbtPoDD6eS05YtSkpcxE8hsOvELwEwmqznAAeB/k8mqEfgxUA20A29X1cPJstcCvwE2quoDE7Uz2WRljDGnCEtWSTmdun48USUftwKFw54fBC4bY7/fYr8kY4wxSXYHC2OMMXnPkpUxxpi8Z8nKGGNM3rNkZYwxJu9ZsjLGGJP3LFkZY4zJe5asjDHG5D1LVsYYY/KeJStjjDF5z5KVMcaYvGfJyhhjTN6zZGWMMSbvWbIyxhiT9yxZGWOMyXuWrIwxxuQ9S1bGGGPyniUrY4wxec+SlTHGmLyX02XtRSQGbAXcQBT4P+DrqhrPZbvTEY9FiQb8xAaDOD1eXAVFODyeMcvHIhEifX3E/H6cBQW4S0txer0ZtBMj3NVDqKcXp9eDt7ICd3FRxv0c7Owh0NYJqhTUVhENRRlo7sDpcVM8r4bC6rKM6xpo7abncCvRcISyhlrK62sQR/Y/x8TjcToPttJ5sAW3z0P1ormUza3MeP+uox007zlKeDBC3ZK5zFk8F4cz83729wxwZO9RDu9pQhxw2urFLFm1aCqHklVHDhxl59a9DAYGWbZ6Caedvgin0znhfrFYjB3bdrN9yy4KigpZs+50Ghc3zECPjZl5OU1WQFBV1wGISC1wC1AG/FeO250S1TiDHW0Mth4birlLyymqX4jD5U4vH48TaGpi4MD+oZi3poayFafjdKeXHy7Y0krrI08OPXeVFDP3ovMzSljB9m72/+nvxMMREKHm7DPYe/vjoJroc3EBa97yMgqryyesq7+1i8e/+0dCfQEAxOHg3He/kuql9RPuO1mtO49w15d/g8YT/SyoKOKlH3o95XOrJty3s6mdX33y5/i7BwBwOB38w39dz4I1jRm1HQqG2P7kTn74hZ8T9AcBcHvdfOTb/8aytadN7YCy4PD+Jv7j7Z+iu6MHAKfLyRdv+gTrzlk94b5PP/Ec77r+A8RiMQBq51Rz061fY9GSBbnssjEnxYwNA6pqG3AD8F5JcIrIl0TkKRHZIiLvOl5WRD4kIltF5DkR+cJM9TEWCjHY1pwSi/T1EAsNjlo+GgwycPBASizU3k7M7x+3nWgoROfmbamx/gFCPT0Z9bN3/+FEogKK59fRumXfUKICiAwE6T3UklFdXXuPDSUqSCTgPXc9RSxZf7ZEBiM8+8dHhxIVQLDbT+uupoz2P7Lt0FCiAojH4jz66weIhMIZ7d/V3MX2p3cOJSqASCjCPb97AB32s5tpzz35/FCiAohFY/z8u79hcDA07n6BQJDvfu0nQ4kKoK21g6efeC5XXTXmpMr1mVUKVd0vIg6gFngV0KuqZ4uIF3hERO4CVgDXAOeqakBERh0nEpEbSCQ/FizI0ifJeDzlTX+o37HRRy01Fhu1fDwWHb+dWJxYKP3NKB6ZYL+kSP+JZOjweogMewMeKjNKbDShgUBabLDXTywaw+kZ/+xwMmLRKMFhyWaorb709kcT6E3/ADDQ1U8sEsM98agr0WiU3u7+tHhHSxfxeDyjYbdc6O7sTYt1tHURDkXw+cY+sHAoTFtLR1q8q6snm90zJm+cjAkWkvx+BfAWEdkMPAFUAUuBy4GfqGoAQFW7RqtEVW9U1Q2quqGmpiYrHXN4vDgLUofhxOnE6fWNWt5VUICrpDS1vMuFq6Bw3HacPh+lixtTgyJ4ykpHLT9S2WkLhx4HjrVSfXpjepmFdRnVVblkflps4YVr8BSOfsxT5SsuYMXlZ6bF5yzPbLixYdXCtNiZV52Nr7ggo/1Lq0o5fd3StPilr7rwpCUqgDPOWZUWe9V1V1JaVjzufuUVZVz3ttekxdefvTZrfTMmn8zomZWILAZiQBuJpPX/VPXOEWWuBE7KuIzD5aK4oZFgWwuR/h6cBUUU1s0fc8KEw+2mfOVK/IcOMtjRgbu0lJLFS3AVjp+sxCGULl0CTif9+w/iKiigcu0qvOXlGfWzaF4t9S8+n9antkBcKVtYh7PAy7Ennsfl89D44rMpnpdZAq9YOIf173gZO//6KNFgmEUXr2Peutxcw1l07gri0Rjb73oaT5GX9ddeQvXizJJq3bL5XPPh1/HgL+4h5A+x/hXnsfyClRm3XVZVxupzTuf6f30dd/7mXmLRGFe/+aWse9GaqR5OVpy+dimf/uaH+NHXb2agz8+1b30FG6+8IKN9r7z6MsKhMDf/5LeUlpbwrx++gdXrTs9xj405OSSX4/UiMqCqxcnHNcDNwGOq+l/JYbyXAf+gqhERWQYcBS4CPglcfnwYcKyzq+M2bNigmzZtylq/NR4nHovicDiRDD51azxOPBxG3G4ck/iUrqrEBgcRp2tKQ27RYAhVxZ08Cwr1B3C4HLgLJn9WFA4MEo/G8JVmPiNxqgI9fpwuJ97iyfdzcCBILBKjqGL8M4+xhEMRejp6cHvcVNSUT6mOXOjvHSAaiVKRwaSYkTraOnF73ZRleGZuTikycZEXhlwnq5FT138OfFVV48lrV/8NvILEL6QduEZVe0Xkw8BbgDDwN1X96HjtZDtZGWNMnrBklZTTZDVTLFkZY2YpS1ZJdgcLY4wxec+SlTHGmLxnycoYY0zes2RljDEm71myMsYYk/csWRljjMl7lqyMMcbkPUtWxhhj8p4lK2OMMXlvVtzBQkTagUOT2KUaSF9f4dQ3W48LZu+xzdbjgtl7bDN5XB2qeuUMtZXXZkWymiwR2aSqG052P7Jtth4XzN5jm63HBbP32GbrceU7GwY0xhiT9yxZGWOMyXsv1GR148nuQI7M1uOC2Xtss/W4YPYe22w9rrz2grxmZYwx5tTyQj2zMsYYcwqxZGWMMSbvzbpkJSI/FpE2Edk2LFYpIneLyJ7k94ph2z4iIntFZJeIvPTk9DozItIgIveJyA4ReV5E/jUZP6WPT0R8IvKkiDyXPK5PJ+On9HEdJyJOEXlWRG5LPp8tx3VQRLaKyGYR2ZSMzZZjKxeR34rIzuTf2/mz5dhOWao6q76Ai4GzgG3DYv8LfDj5+MPAF5OPVwLPAV5gEbAPcJ7sYxjn2OYCZyUflwC7k8dwSh8fiaW7i5OP3cATwHmn+nENO75/B24Bbptlr8eDQPWI2Gw5tv8D/jH52AOUz5ZjO1W/Zt2Zlao+CHSNCL+KxIuP5PdrhsV/qaohVT0A7AXOmYl+ToWqNqvqM8nH/cAOYD6n+PFpwkDyqTv5pZzixwUgIvXAy4EfDguf8sc1jlP+2ESklMSH3h8BqGpYVXuYBcd2Kpt1yWoMc1S1GRJv+EBtMj4fODKsXFMylvdEpBE4k8RZyCl/fMmhss1AG3C3qs6K4wK+DnwIiA+LzYbjgsQHirtE5GkRuSEZmw3HthhoB36SHL79oYgUMTuO7ZT1QklWY5FRYnk/l19EioHfAe9X1b7xio4Sy8vjU9WYqq4D6oFzRGT1OMVPieMSkauBNlV9OtNdRonl3XEN8yJVPQu4CniPiFw8TtlT6dhcJC4lfE9VzwT8JIb9xnIqHdsp64WSrFpFZC5A8ntbMt4ENAwrVw8cm+G+TYqIuEkkqptV9ffJ8Kw5vuRwy/3AlZz6x/Ui4JUichD4JXCZiPyCU/+4AFDVY8nvbcAfSAx9zYZjawKakmf3AL8lkbxmw7Gdsl4oyerPwFuTj98K/GlY/A0i4hWRRcBS4MmT0L+MiIiQGEffoapfHbbplD4+EakRkfLk4wLgcmAnp/hxqepHVLVeVRuBNwD3qur1nOLHBSAiRSJScvwxcAWwjVlwbKraAhwRkeXJ0IuB7cyCYzulnewZHtn+Am4FmoEIiU887wSqgHuAPcnvlcPKf4zE7J1dwFUnu/8THNuFJIYXtgCbk18vO9WPD1gLPJs8rm3AJ5PxU/q4RhzjRk7MBjzlj4vEdZ3nkl/PAx+bLceW7Os6YFPyNflHoGK2HNup+mW3WzLGGJP3XijDgMYYY05hlqyMMcbkPUtWxhhj8p4lK2OMMXnPkpUxxpi8Z8nKGGNM3rNkZUweEBHnye6DMfnMkpU5pYlIY3LNof8TkS3JNYgKReSTIvKUiGwTkRuTd/9ARN4nItuTZX+ZjF2SXJNpc/LGpcfvzPDBZB1bhq2x1Zhc3+gmSay9dVfyrhuIyNnJso+JyJckuaZa8ia9XxpW17uS8Y2SWJ/sFmBr8q4Qf5XEul7bROT1J+FHakxesmRlZoPlwI2quhboA/4F+Laqnq2qq4EC4Opk2Q8DZybLvjsZ+wDwHk3cSPciICgiV5C4bc45JO5msH7YjVqXAt9R1VVAD/DaZPwnwLtV9XwgNqx/7wR6VfVs4Gzgn5K35SFZ/8dUdSWJ+yEeU9Uzkv2+Y/o/GmNmB0tWZjY4oqqPJB//gsRtqS4VkSdEZCtwGbAquX0LcLOIXA9Ek7FHgK+KyPuAclWNkrjX3RUkbgP1DLCCRJICOKCqm5OPnwYak/c2LFHVR5PxW4b17wrgLcklUJ4gcdue43U9qYk1kAC2ApeLyBdF5CJV7Z3yT8SYWcaSlZkNRt4zTIHvAteq6hrgJsCX3PZy4DvAeuBpEXGp6heAfyRxBva4iKwgsezD51V1XfLrNFX9UbKO0LC2YiSWlBhtmYjjBPh/w+papKp3Jbf5hzqtujvZr63A50Xkk5P5IRgzm1myMrPBAhE5P/n4OuDh5OOO5Npf1wKIiANoUNX7SCyIWA4Ui8gSVd2qql8kcfPSFcCdwDuS+yMi80WkljGoajfQLyLnJUNvGLb5TuCfk8u7ICLLkncqTyEi84CAqv4C+DKJZSmMMSQ+ERpzqtsBvFVEfkDijtjfI3GX7K3AQeCpZDkn8AsRKSNxtvM1Ve0Rkc+KyKUkzpK2A7erakhETgceS87NGACuJ/Va1EjvBG4SET+JNbmOD+P9EGgEnklO9GjnxJLow60BviQicRKrBvzz5H4Mxsxedtd1c0oTkUYSS2+Mt7LwTPWlWFUHko8/DMxV1X89yd0yZlawMytjsuflIvIREn9Xh4C3ndzuGDN72JmVMcaYvGcTLIwxxuQ9S1bGGGPyniUrY4wxec+SlTHGmLxnycoYY0ze+/8R4EMsMCz+VAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 421.5x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = sns.load_dataset(\"flights\")\n",
+    "sns.relplot(x = \"passengers\",y = \"month\",hue=\"year\",data =a )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x227524332b0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "b = sns.load_dataset(\"tips\")\n",
+    "sns.relplot(x =\"time\",y = \"tip\",data = b,kind= \"line\") # line plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x22757411cd0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(x=\"day\",y=\"total_bill\", data=b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x2275746bf70>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(x=\"day\",y=\"total_bill\", data=b, kind=\"violin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x2275752b460>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(x=\"day\",y=\"total_bill\", data=b, kind=\"box\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Univariate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Nikhil\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Density'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = np.random.normal(loc=5,size=100,scale=2)\n",
+    "sns.distplot(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x2275785b760>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Count'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bivariate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x2275984b4c0>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = sns.load_dataset(\"flights\")\n",
+    "sns.jointplot(x = \"passengers\",y = \"year\",data =a,kind='kde')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x227592e03a0>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "b = sns.load_dataset(\"tips\")\n",
+    "sns.jointplot(x =\"time\",y = \"tip\",data = b, kind='')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiplot Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x22759855700>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 648x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = sns.load_dataset(\"iris\")\n",
+    "b = sns.FacetGrid(a, col='species')\n",
+    "b.map(plt.hist, 'sepal_length')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2275957e3d0>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = sns.load_dataset(\"flights\")\n",
+    "b = sns.PairGrid(a)\n",
+    "b.map(plt.scatter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot-Aesthetics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2275aa91c10>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='darkgrid')         # background\n",
+    "a = sns.load_dataset(\"flights\")\n",
+    "b = sns.PairGrid(a)\n",
+    "b.map(plt.scatter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2275aba73a0>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='dark')         # background\n",
+    "a = sns.load_dataset(\"flights\")\n",
+    "b = sns.PairGrid(a)\n",
+    "b.map(plt.scatter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='white', color_codes = True)        \n",
+    "a = sns.load_dataset(\"tips\")\n",
+    "sns.boxplot(x='day',y=\"total_bill\",data=a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='white', color_codes = True)        \n",
+    "a = sns.load_dataset(\"tips\")\n",
+    "sns.boxplot(x='day',y=\"total_bill\",data=a)\n",
+    "sns.despine(offset=10,trim=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Color palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = sns.color_palette()\n",
+    "sns.palplot(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thank You"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/machine_learning/Statistics/Distribution.ipynb b/machine_learning/Statistics/Distribution.ipynb
new file mode 100644
index 0000000..13a1dd3
--- /dev/null
+++ b/machine_learning/Statistics/Distribution.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normal Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q3hztr0/1L0aX3LMb0KgbNWcx//ix/W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\nAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a sequence of numbers between -1 and 20 incrementing by 0.2.  \n",
+    "x <- seq(-1, 20, by = .2)  \n",
+    "# Choosing the mean as 5.0 and standard deviation as 0.5.  \n",
+    "y <- dnorm(x, mean = 5.0, sd = 0.5)   \n",
+    "#Plotting the graph  \n",
+    "plot(x,y)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AkChAQBQoIAIUGAkCBASBAgJAgQEgQI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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a sequence of numbers between -1 and 20 incrementing by 0.2.  \n",
+    "x <- seq(-1, 20, by = .1)  \n",
+    "# Choosing the mean as 2.0 and standard deviation as 0.5.  \n",
+    "y <- pnorm(x, mean = 2.0, sd = 0.5)  \n",
+    "#Plotting the graph  \n",
+    "plot(x,y)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CoqP/5+ufv7tYcfhWsIqaIa7yPdC2kU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\nCCAkCCAkCCAkCCAkCPAfY6EStFtyLYIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a sequence of numbers between -1 and 20 incrementing by 0.2.  \n",
+    "x <- seq(0, 1, by = .01)  \n",
+    "# Choosing the mean as 2.0 and standard deviation as 0.5.  \n",
+    "y <- qnorm(x, mean = 2.0, sd = 0.5)   \n",
+    "#Plotting the graph  \n",
+    "plot(y,x)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GkcRj+CQrsy6od0SJtT/y/Dpv8noTmcD03K/bkwf7G+brriUzWX+z+t+x+5Vniq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\ngg==",
+      "text/plain": [
+       "Plot with title \"Histogram of x\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a sequence of numbers between -1 and 20 incrementing by 0.2.  \n",
+    "x <- rnorm(1500, mean=80,  sd=15 )    \n",
+    "#Creating histogram  \n",
+    "hist(x,probability =TRUE,col=\"red\",border=\"black\")  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Binomial Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CpBq5Bar6m0ns/VPCOdTG287az1fG5Q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\nBAFCgoB/+LzuF3/V3NUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a sample of 100 numbers which are incremented by 1.5.  \n",
+    "x <- seq(0,100,by = 1)  \n",
+    "# Creating the binomial distribution.  \n",
+    "y <- dbinom(x,50,0.5)  \n",
+    "# Plotting the graph.  \n",
+    "plot(x,y) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.1561634\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Probability of getting 20 or fewer heads from 48 tosses of a coin.  \n",
+    "x <- pbinom(20,48,0.5)  \n",
+    "#Showing output  \n",
+    "print(x)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [1] 121 134 127 130 128 127 125 134 131 134 123 127 116 124 124 124 132 132\n",
+      " [19] 125 126 131 127 124 131 133 133 130 120 129 134 131 129 126 135 126 133\n",
+      " [37] 131 125 127 128 130 129 125 114 135 135 128 133 125 127 135 130 137 131\n",
+      " [55] 118 120 134 135 132 121 130 133 129 128 131 125 139 131 125 132 130 125\n",
+      " [73] 124 127 117 139 128 123 132 127 123 124 130 131 122 128 120 136 135 131\n",
+      " [91] 126 131 121 130 137 131 129 137 131 120\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finding random values    \n",
+    "x <- rbinom(100,160,0.8)  \n",
+    "#Showing output  \n",
+    "print(x)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/machine_learning/scipy/scipy.ipynb b/machine_learning/scipy/scipy.ipynb
new file mode 100644
index 0000000..d6f11e9
--- /dev/null
+++ b/machine_learning/scipy/scipy.ipynb
@@ -0,0 +1,68025 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Scipy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "from scipy import cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on package scipy.cluster in scipy:\n",
+      "\n",
+      "NAME\n",
+      "    scipy.cluster\n",
+      "\n",
+      "DESCRIPTION\n",
+      "    =========================================\n",
+      "    Clustering package (:mod:`scipy.cluster`)\n",
+      "    =========================================\n",
+      "    \n",
+      "    .. currentmodule:: scipy.cluster\n",
+      "    \n",
+      "    :mod:`scipy.cluster.vq`\n",
+      "    \n",
+      "    Clustering algorithms are useful in information theory, target detection,\n",
+      "    communications, compression, and other areas. The `vq` module only\n",
+      "    supports vector quantization and the k-means algorithms.\n",
+      "    \n",
+      "    :mod:`scipy.cluster.hierarchy`\n",
+      "    \n",
+      "    The `hierarchy` module provides functions for hierarchical and\n",
+      "    agglomerative clustering.  Its features include generating hierarchical\n",
+      "    clusters from distance matrices,\n",
+      "    calculating statistics on clusters, cutting linkages\n",
+      "    to generate flat clusters, and visualizing clusters with dendrograms.\n",
+      "\n",
+      "PACKAGE CONTENTS\n",
+      "    _hierarchy\n",
+      "    _optimal_leaf_ordering\n",
+      "    _vq\n",
+      "    hierarchy\n",
+      "    setup\n",
+      "    tests (package)\n",
+      "    vq\n",
+      "\n",
+      "DATA\n",
+      "    __all__ = ['vq', 'hierarchy']\n",
+      "\n",
+      "FILE\n",
+      "    c:\\users\\nikhil\\anaconda3\\lib\\site-packages\\scipy\\cluster\\__init__.py\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Welcome to Python 3.7's help utility!\n",
+      "\n",
+      "If this is your first time using Python, you should definitely check out\n",
+      "the tutorial on the Internet at https://docs.python.org/3.7/tutorial/.\n",
+      "\n",
+      "Enter the name of any module, keyword, or topic to get help on writing\n",
+      "Python programs and using Python modules.  To quit this help utility and\n",
+      "return to the interpreter, just type \"quit\".\n",
+      "\n",
+      "To get a list of available modules, keywords, symbols, or topics, type\n",
+      "\"modules\", \"keywords\", \"symbols\", or \"topics\".  Each module also comes\n",
+      "with a one-line summary of what it does; to list the modules whose name\n",
+      "or summary contain a given string such as \"spam\", type \"modules spam\".\n",
+      "\n",
+      "help> scipy\n",
+      "Help on package scipy:\n",
+      "\n",
+      "NAME\n",
+      "    scipy\n",
+      "\n",
+      "DESCRIPTION\n",
+      "    SciPy: A scientific computing package for Python\n",
+      "    ================================================\n",
+      "    \n",
+      "    Documentation is available in the docstrings and\n",
+      "    online at https://docs.scipy.org.\n",
+      "    \n",
+      "    Contents\n",
+      "    --------\n",
+      "    SciPy imports all the functions from the NumPy namespace, and in\n",
+      "    addition provides:\n",
+      "    \n",
+      "    Subpackages\n",
+      "    -----------\n",
+      "    Using any of these subpackages requires an explicit import. For example,\n",
+      "    ``import scipy.cluster``.\n",
+      "    \n",
+      "    ::\n",
+      "    \n",
+      "     cluster                      --- Vector Quantization / Kmeans\n",
+      "     fft                          --- Discrete Fourier transforms\n",
+      "     fftpack                      --- Legacy discrete Fourier transforms\n",
+      "     integrate                    --- Integration routines\n",
+      "     interpolate                  --- Interpolation Tools\n",
+      "     io                           --- Data input and output\n",
+      "     linalg                       --- Linear algebra routines\n",
+      "     linalg.blas                  --- Wrappers to BLAS library\n",
+      "     linalg.lapack                --- Wrappers to LAPACK library\n",
+      "     misc                         --- Various utilities that don't have\n",
+      "                                      another home.\n",
+      "     ndimage                      --- N-D image package\n",
+      "     odr                          --- Orthogonal Distance Regression\n",
+      "     optimize                     --- Optimization Tools\n",
+      "     signal                       --- Signal Processing Tools\n",
+      "     signal.windows               --- Window functions\n",
+      "     sparse                       --- Sparse Matrices\n",
+      "     sparse.linalg                --- Sparse Linear Algebra\n",
+      "     sparse.linalg.dsolve         --- Linear Solvers\n",
+      "     sparse.linalg.dsolve.umfpack --- :Interface to the UMFPACK library:\n",
+      "                                      Conjugate Gradient Method (LOBPCG)\n",
+      "     sparse.linalg.eigen          --- Sparse Eigenvalue Solvers\n",
+      "     sparse.linalg.eigen.lobpcg   --- Locally Optimal Block Preconditioned\n",
+      "                                      Conjugate Gradient Method (LOBPCG)\n",
+      "     spatial                      --- Spatial data structures and algorithms\n",
+      "     special                      --- Special functions\n",
+      "     stats                        --- Statistical Functions\n",
+      "    \n",
+      "    Utility tools\n",
+      "    -------------\n",
+      "    ::\n",
+      "    \n",
+      "     test              --- Run scipy unittests\n",
+      "     show_config       --- Show scipy build configuration\n",
+      "     show_numpy_config --- Show numpy build configuration\n",
+      "     __version__       --- SciPy version string\n",
+      "     __numpy_version__ --- Numpy version string\n",
+      "\n",
+      "PACKAGE CONTENTS\n",
+      "    __config__\n",
+      "    _build_utils (package)\n",
+      "    _distributor_init\n",
+      "    _lib (package)\n",
+      "    cluster (package)\n",
+      "    conftest\n",
+      "    constants (package)\n",
+      "    fft (package)\n",
+      "    fftpack (package)\n",
+      "    integrate (package)\n",
+      "    interpolate (package)\n",
+      "    io (package)\n",
+      "    linalg (package)\n",
+      "    misc (package)\n",
+      "    ndimage (package)\n",
+      "    odr (package)\n",
+      "    optimize (package)\n",
+      "    setup\n",
+      "    signal (package)\n",
+      "    sparse (package)\n",
+      "    spatial (package)\n",
+      "    special (package)\n",
+      "    stats (package)\n",
+      "    version\n",
+      "\n",
+      "CLASSES\n",
+      "    builtins.DeprecationWarning(builtins.Warning)\n",
+      "        numpy.ModuleDeprecationWarning\n",
+      "    builtins.IndexError(builtins.LookupError)\n",
+      "        numpy.AxisError(builtins.ValueError, builtins.IndexError)\n",
+      "    builtins.RuntimeError(builtins.Exception)\n",
+      "        numpy.TooHardError\n",
+      "    builtins.RuntimeWarning(builtins.Warning)\n",
+      "        numpy.ComplexWarning\n",
+      "    builtins.UserWarning(builtins.Warning)\n",
+      "        numpy.RankWarning\n",
+      "        numpy.VisibleDeprecationWarning\n",
+      "    builtins.ValueError(builtins.Exception)\n",
+      "        numpy.AxisError(builtins.ValueError, builtins.IndexError)\n",
+      "    builtins.bytes(builtins.object)\n",
+      "        numpy.bytes_(builtins.bytes, numpy.character)\n",
+      "    builtins.object\n",
+      "        numpy.DataSource\n",
+      "        numpy.MachAr\n",
+      "        numpy.broadcast\n",
+      "        numpy.busdaycalendar\n",
+      "        numpy.dtype\n",
+      "        numpy.finfo\n",
+      "        numpy.flatiter\n",
+      "        numpy.format_parser\n",
+      "        numpy.generic\n",
+      "            numpy.bool_\n",
+      "            numpy.datetime64\n",
+      "            numpy.flexible\n",
+      "                numpy.character\n",
+      "                    numpy.bytes_(builtins.bytes, numpy.character)\n",
+      "                    numpy.str_(builtins.str, numpy.character)\n",
+      "                numpy.void\n",
+      "                    numpy.record\n",
+      "            numpy.number\n",
+      "                numpy.inexact\n",
+      "                    numpy.complexfloating\n",
+      "                        numpy.clongdouble\n",
+      "                        numpy.complex128(numpy.complexfloating, builtins.complex)\n",
+      "                        numpy.complex64\n",
+      "                    numpy.floating\n",
+      "                        numpy.float16\n",
+      "                        numpy.float32\n",
+      "                        numpy.float64(numpy.floating, builtins.float)\n",
+      "                        numpy.longdouble\n",
+      "                numpy.integer\n",
+      "                    numpy.signedinteger\n",
+      "                        numpy.int16\n",
+      "                        numpy.int32\n",
+      "                        numpy.int64\n",
+      "                        numpy.int8\n",
+      "                        numpy.intc\n",
+      "                        numpy.timedelta64\n",
+      "                    numpy.unsignedinteger\n",
+      "                        numpy.uint16\n",
+      "                        numpy.uint32\n",
+      "                        numpy.uint64\n",
+      "                        numpy.uint8\n",
+      "                        numpy.uintc\n",
+      "            numpy.object_\n",
+      "        numpy.iinfo\n",
+      "        numpy.ndarray\n",
+      "            numpy.chararray\n",
+      "            numpy.matrix\n",
+      "            numpy.memmap\n",
+      "            numpy.recarray\n",
+      "        numpy.ndenumerate\n",
+      "        numpy.ndindex\n",
+      "        numpy.nditer\n",
+      "        numpy.poly1d\n",
+      "        numpy.ufunc\n",
+      "        numpy.vectorize\n",
+      "    builtins.str(builtins.object)\n",
+      "        numpy.str_(builtins.str, numpy.character)\n",
+      "    contextlib.ContextDecorator(builtins.object)\n",
+      "        numpy.errstate\n",
+      "    \n",
+      "    class AxisError(builtins.ValueError, builtins.IndexError)\n",
+      "     |  AxisError(axis, ndim=None, msg_prefix=None)\n",
+      "     |  \n",
+      "     |  Axis supplied was invalid.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      AxisError\n",
+      "     |      builtins.ValueError\n",
+      "     |      builtins.IndexError\n",
+      "     |      builtins.LookupError\n",
+      "     |      builtins.Exception\n",
+      "     |      builtins.BaseException\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __init__(self, axis, ndim=None, msg_prefix=None)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.ValueError:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  with_traceback(...)\n",
+      "     |      Exception.with_traceback(tb) --\n",
+      "     |      set self.__traceback__ to tb and return self.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __cause__\n",
+      "     |      exception cause\n",
+      "     |  \n",
+      "     |  __context__\n",
+      "     |      exception context\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |  \n",
+      "     |  __suppress_context__\n",
+      "     |  \n",
+      "     |  __traceback__\n",
+      "     |  \n",
+      "     |  args\n",
+      "    \n",
+      "    class ComplexWarning(builtins.RuntimeWarning)\n",
+      "     |  The warning raised when casting a complex dtype to a real dtype.\n",
+      "     |  \n",
+      "     |  As implemented, casting a complex number to a real discards its imaginary\n",
+      "     |  part, but this behavior may not be what the user actually wants.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      ComplexWarning\n",
+      "     |      builtins.RuntimeWarning\n",
+      "     |      builtins.Warning\n",
+      "     |      builtins.Exception\n",
+      "     |      builtins.BaseException\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.RuntimeWarning:\n",
+      "     |  \n",
+      "     |  __init__(self, /, *args, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.RuntimeWarning:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  with_traceback(...)\n",
+      "     |      Exception.with_traceback(tb) --\n",
+      "     |      set self.__traceback__ to tb and return self.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __cause__\n",
+      "     |      exception cause\n",
+      "     |  \n",
+      "     |  __context__\n",
+      "     |      exception context\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |  \n",
+      "     |  __suppress_context__\n",
+      "     |  \n",
+      "     |  __traceback__\n",
+      "     |  \n",
+      "     |  args\n",
+      "    \n",
+      "    class DataSource(builtins.object)\n",
+      "     |  DataSource(destpath='.')\n",
+      "     |  \n",
+      "     |  DataSource(destpath='.')\n",
+      "     |  \n",
+      "     |  A generic data source file (file, http, ftp, ...).\n",
+      "     |  \n",
+      "     |  DataSources can be local files or remote files/URLs.  The files may\n",
+      "     |  also be compressed or uncompressed. DataSource hides some of the\n",
+      "     |  low-level details of downloading the file, allowing you to simply pass\n",
+      "     |  in a valid file path (or URL) and obtain a file object.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  destpath : str or None, optional\n",
+      "     |      Path to the directory where the source file gets downloaded to for\n",
+      "     |      use.  If `destpath` is None, a temporary directory will be created.\n",
+      "     |      The default path is the current directory.\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  URLs require a scheme string (``http://``) to be used, without it they\n",
+      "     |  will fail::\n",
+      "     |  \n",
+      "     |      >>> repos = np.DataSource()\n",
+      "     |      >>> repos.exists('www.google.com/index.html')\n",
+      "     |      False\n",
+      "     |      >>> repos.exists('http://www.google.com/index.html')\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  Temporary directories are deleted when the DataSource is deleted.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  ::\n",
+      "     |  \n",
+      "     |      >>> ds = np.DataSource('/home/guido')\n",
+      "     |      >>> urlname = 'http://www.google.com/'\n",
+      "     |      >>> gfile = ds.open('http://www.google.com/')\n",
+      "     |      >>> ds.abspath(urlname)\n",
+      "     |      '/home/guido/www.google.com/index.html'\n",
+      "     |  \n",
+      "     |      >>> ds = np.DataSource(None)  # use with temporary file\n",
+      "     |      >>> ds.open('/home/guido/foobar.txt')\n",
+      "     |      <open file '/home/guido.foobar.txt', mode 'r' at 0x91d4430>\n",
+      "     |      >>> ds.abspath('/home/guido/foobar.txt')\n",
+      "     |      '/tmp/.../home/guido/foobar.txt'\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __del__(self)\n",
+      "     |  \n",
+      "     |  __init__(self, destpath='.')\n",
+      "     |      Create a DataSource with a local path at destpath.\n",
+      "     |  \n",
+      "     |  abspath(self, path)\n",
+      "     |      Return absolute path of file in the DataSource directory.\n",
+      "     |      \n",
+      "     |      If `path` is an URL, then `abspath` will return either the location\n",
+      "     |      the file exists locally or the location it would exist when opened\n",
+      "     |      using the `open` method.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      path : str\n",
+      "     |          Can be a local file or a remote URL.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : str\n",
+      "     |          Complete path, including the `DataSource` destination directory.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The functionality is based on `os.path.abspath`.\n",
+      "     |  \n",
+      "     |  exists(self, path)\n",
+      "     |      Test if path exists.\n",
+      "     |      \n",
+      "     |      Test if `path` exists as (and in this order):\n",
+      "     |      \n",
+      "     |      - a local file.\n",
+      "     |      - a remote URL that has been downloaded and stored locally in the\n",
+      "     |        `DataSource` directory.\n",
+      "     |      - a remote URL that has not been downloaded, but is valid and\n",
+      "     |        accessible.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      path : str\n",
+      "     |          Can be a local file or a remote URL.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : bool\n",
+      "     |          True if `path` exists.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      When `path` is an URL, `exists` will return True if it's either\n",
+      "     |      stored locally in the `DataSource` directory, or is a valid remote\n",
+      "     |      URL.  `DataSource` does not discriminate between the two, the file\n",
+      "     |      is accessible if it exists in either location.\n",
+      "     |  \n",
+      "     |  open(self, path, mode='r', encoding=None, newline=None)\n",
+      "     |      Open and return file-like object.\n",
+      "     |      \n",
+      "     |      If `path` is an URL, it will be downloaded, stored in the\n",
+      "     |      `DataSource` directory and opened from there.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      path : str\n",
+      "     |          Local file path or URL to open.\n",
+      "     |      mode : {'r', 'w', 'a'}, optional\n",
+      "     |          Mode to open `path`.  Mode 'r' for reading, 'w' for writing,\n",
+      "     |          'a' to append. Available modes depend on the type of object\n",
+      "     |          specified by `path`. Default is 'r'.\n",
+      "     |      encoding : {None, str}, optional\n",
+      "     |          Open text file with given encoding. The default encoding will be\n",
+      "     |          what `io.open` uses.\n",
+      "     |      newline : {None, str}, optional\n",
+      "     |          Newline to use when reading text file.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : file object\n",
+      "     |          File object.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class MachAr(builtins.object)\n",
+      "     |  MachAr(float_conv=<class 'float'>, int_conv=<class 'int'>, float_to_float=<class 'float'>, float_to_str=<function MachAr.<lambda> at 0x0000018C0DFDDA68>, title='Python floating point number')\n",
+      "     |  \n",
+      "     |  Diagnosing machine parameters.\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  ibeta : int\n",
+      "     |      Radix in which numbers are represented.\n",
+      "     |  it : int\n",
+      "     |      Number of base-`ibeta` digits in the floating point mantissa M.\n",
+      "     |  machep : int\n",
+      "     |      Exponent of the smallest (most negative) power of `ibeta` that,\n",
+      "     |      added to 1.0, gives something different from 1.0\n",
+      "     |  eps : float\n",
+      "     |      Floating-point number ``beta**machep`` (floating point precision)\n",
+      "     |  negep : int\n",
+      "     |      Exponent of the smallest power of `ibeta` that, subtracted\n",
+      "     |      from 1.0, gives something different from 1.0.\n",
+      "     |  epsneg : float\n",
+      "     |      Floating-point number ``beta**negep``.\n",
+      "     |  iexp : int\n",
+      "     |      Number of bits in the exponent (including its sign and bias).\n",
+      "     |  minexp : int\n",
+      "     |      Smallest (most negative) power of `ibeta` consistent with there\n",
+      "     |      being no leading zeros in the mantissa.\n",
+      "     |  xmin : float\n",
+      "     |      Floating point number ``beta**minexp`` (the smallest [in\n",
+      "     |      magnitude] usable floating value).\n",
+      "     |  maxexp : int\n",
+      "     |      Smallest (positive) power of `ibeta` that causes overflow.\n",
+      "     |  xmax : float\n",
+      "     |      ``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]\n",
+      "     |      usable floating value).\n",
+      "     |  irnd : int\n",
+      "     |      In ``range(6)``, information on what kind of rounding is done\n",
+      "     |      in addition, and on how underflow is handled.\n",
+      "     |  ngrd : int\n",
+      "     |      Number of 'guard digits' used when truncating the product\n",
+      "     |      of two mantissas to fit the representation.\n",
+      "     |  epsilon : float\n",
+      "     |      Same as `eps`.\n",
+      "     |  tiny : float\n",
+      "     |      Same as `xmin`.\n",
+      "     |  huge : float\n",
+      "     |      Same as `xmax`.\n",
+      "     |  precision : float\n",
+      "     |      ``- int(-log10(eps))``\n",
+      "     |  resolution : float\n",
+      "     |      ``- 10**(-precision)``\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  float_conv : function, optional\n",
+      "     |      Function that converts an integer or integer array to a float\n",
+      "     |      or float array. Default is `float`.\n",
+      "     |  int_conv : function, optional\n",
+      "     |      Function that converts a float or float array to an integer or\n",
+      "     |      integer array. Default is `int`.\n",
+      "     |  float_to_float : function, optional\n",
+      "     |      Function that converts a float array to float. Default is `float`.\n",
+      "     |      Note that this does not seem to do anything useful in the current\n",
+      "     |      implementation.\n",
+      "     |  float_to_str : function, optional\n",
+      "     |      Function that converts a single float to a string. Default is\n",
+      "     |      ``lambda v:'%24.16e' %v``.\n",
+      "     |  title : str, optional\n",
+      "     |      Title that is printed in the string representation of `MachAr`.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  finfo : Machine limits for floating point types.\n",
+      "     |  iinfo : Machine limits for integer types.\n",
+      "     |  \n",
+      "     |  References\n",
+      "     |  ----------\n",
+      "     |  .. [1] Press, Teukolsky, Vetterling and Flannery,\n",
+      "     |         \"Numerical Recipes in C++,\" 2nd ed,\n",
+      "     |         Cambridge University Press, 2002, p. 31.\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __init__(self, float_conv=<class 'float'>, int_conv=<class 'int'>, float_to_float=<class 'float'>, float_to_str=<function MachAr.<lambda> at 0x0000018C0DFDDA68>, title='Python floating point number')\n",
+      "     |      float_conv - convert integer to float (array)\n",
+      "     |      int_conv   - convert float (array) to integer\n",
+      "     |      float_to_float - convert float array to float\n",
+      "     |      float_to_str - convert array float to str\n",
+      "     |      title        - description of used floating point numbers\n",
+      "     |  \n",
+      "     |  __str__(self)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class ModuleDeprecationWarning(builtins.DeprecationWarning)\n",
+      "     |  Module deprecation warning.\n",
+      "     |  \n",
+      "     |  The nose tester turns ordinary Deprecation warnings into test failures.\n",
+      "     |  That makes it hard to deprecate whole modules, because they get\n",
+      "     |  imported by default. So this is a special Deprecation warning that the\n",
+      "     |  nose tester will let pass without making tests fail.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      ModuleDeprecationWarning\n",
+      "     |      builtins.DeprecationWarning\n",
+      "     |      builtins.Warning\n",
+      "     |      builtins.Exception\n",
+      "     |      builtins.BaseException\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.DeprecationWarning:\n",
+      "     |  \n",
+      "     |  __init__(self, /, *args, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.DeprecationWarning:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  with_traceback(...)\n",
+      "     |      Exception.with_traceback(tb) --\n",
+      "     |      set self.__traceback__ to tb and return self.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __cause__\n",
+      "     |      exception cause\n",
+      "     |  \n",
+      "     |  __context__\n",
+      "     |      exception context\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |  \n",
+      "     |  __suppress_context__\n",
+      "     |  \n",
+      "     |  __traceback__\n",
+      "     |  \n",
+      "     |  args\n",
+      "    \n",
+      "    class RankWarning(builtins.UserWarning)\n",
+      "     |  Issued by `polyfit` when the Vandermonde matrix is rank deficient.\n",
+      "     |  \n",
+      "     |  For more information, a way to suppress the warning, and an example of\n",
+      "     |  `RankWarning` being issued, see `polyfit`.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      RankWarning\n",
+      "     |      builtins.UserWarning\n",
+      "     |      builtins.Warning\n",
+      "     |      builtins.Exception\n",
+      "     |      builtins.BaseException\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.UserWarning:\n",
+      "     |  \n",
+      "     |  __init__(self, /, *args, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.UserWarning:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  with_traceback(...)\n",
+      "     |      Exception.with_traceback(tb) --\n",
+      "     |      set self.__traceback__ to tb and return self.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __cause__\n",
+      "     |      exception cause\n",
+      "     |  \n",
+      "     |  __context__\n",
+      "     |      exception context\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |  \n",
+      "     |  __suppress_context__\n",
+      "     |  \n",
+      "     |  __traceback__\n",
+      "     |  \n",
+      "     |  args\n",
+      "    \n",
+      "    class TooHardError(builtins.RuntimeError)\n",
+      "     |  Unspecified run-time error.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      TooHardError\n",
+      "     |      builtins.RuntimeError\n",
+      "     |      builtins.Exception\n",
+      "     |      builtins.BaseException\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.RuntimeError:\n",
+      "     |  \n",
+      "     |  __init__(self, /, *args, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.RuntimeError:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  with_traceback(...)\n",
+      "     |      Exception.with_traceback(tb) --\n",
+      "     |      set self.__traceback__ to tb and return self.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __cause__\n",
+      "     |      exception cause\n",
+      "     |  \n",
+      "     |  __context__\n",
+      "     |      exception context\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |  \n",
+      "     |  __suppress_context__\n",
+      "     |  \n",
+      "     |  __traceback__\n",
+      "     |  \n",
+      "     |  args\n",
+      "    \n",
+      "    class VisibleDeprecationWarning(builtins.UserWarning)\n",
+      "     |  Visible deprecation warning.\n",
+      "     |  \n",
+      "     |  By default, python will not show deprecation warnings, so this class\n",
+      "     |  can be used when a very visible warning is helpful, for example because\n",
+      "     |  the usage is most likely a user bug.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      VisibleDeprecationWarning\n",
+      "     |      builtins.UserWarning\n",
+      "     |      builtins.Warning\n",
+      "     |      builtins.Exception\n",
+      "     |      builtins.BaseException\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.UserWarning:\n",
+      "     |  \n",
+      "     |  __init__(self, /, *args, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.UserWarning:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  with_traceback(...)\n",
+      "     |      Exception.with_traceback(tb) --\n",
+      "     |      set self.__traceback__ to tb and return self.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from builtins.BaseException:\n",
+      "     |  \n",
+      "     |  __cause__\n",
+      "     |      exception cause\n",
+      "     |  \n",
+      "     |  __context__\n",
+      "     |      exception context\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |  \n",
+      "     |  __suppress_context__\n",
+      "     |  \n",
+      "     |  __traceback__\n",
+      "     |  \n",
+      "     |  args\n",
+      "    \n",
+      "    bool8 = class bool_(generic)\n",
+      "     |  Boolean type (True or False), stored as a byte.\n",
+      "     |  Character code: ``'?'``.\n",
+      "     |  Alias: ``np.bool8``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      bool_\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class bool_(generic)\n",
+      "     |  Boolean type (True or False), stored as a byte.\n",
+      "     |  Character code: ``'?'``.\n",
+      "     |  Alias: ``np.bool8``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      bool_\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class broadcast(builtins.object)\n",
+      "     |  Produce an object that mimics broadcasting.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  in1, in2, ... : array_like\n",
+      "     |      Input parameters.\n",
+      "     |  \n",
+      "     |  Returns\n",
+      "     |  -------\n",
+      "     |  b : broadcast object\n",
+      "     |      Broadcast the input parameters against one another, and\n",
+      "     |      return an object that encapsulates the result.\n",
+      "     |      Amongst others, it has ``shape`` and ``nd`` properties, and\n",
+      "     |      may be used as an iterator.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  broadcast_arrays\n",
+      "     |  broadcast_to\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  \n",
+      "     |  Manually adding two vectors, using broadcasting:\n",
+      "     |  \n",
+      "     |  >>> x = np.array([[1], [2], [3]])\n",
+      "     |  >>> y = np.array([4, 5, 6])\n",
+      "     |  >>> b = np.broadcast(x, y)\n",
+      "     |  \n",
+      "     |  >>> out = np.empty(b.shape)\n",
+      "     |  >>> out.flat = [u+v for (u,v) in b]\n",
+      "     |  >>> out\n",
+      "     |  array([[5.,  6.,  7.],\n",
+      "     |         [6.,  7.,  8.],\n",
+      "     |         [7.,  8.,  9.]])\n",
+      "     |  \n",
+      "     |  Compare against built-in broadcasting:\n",
+      "     |  \n",
+      "     |  >>> x + y\n",
+      "     |  array([[5, 6, 7],\n",
+      "     |         [6, 7, 8],\n",
+      "     |         [7, 8, 9]])\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __next__(self, /)\n",
+      "     |      Implement next(self).\n",
+      "     |  \n",
+      "     |  reset(...)\n",
+      "     |      reset()\n",
+      "     |      \n",
+      "     |      Reset the broadcasted result's iterator(s).\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> y = np.array([[4], [5], [6]])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> b.index\n",
+      "     |      0\n",
+      "     |      >>> next(b), next(b), next(b)\n",
+      "     |      ((1, 4), (2, 4), (3, 4))\n",
+      "     |      >>> b.index\n",
+      "     |      3\n",
+      "     |      >>> b.reset()\n",
+      "     |      >>> b.index\n",
+      "     |      0\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  index\n",
+      "     |      current index in broadcasted result\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1], [2], [3]])\n",
+      "     |      >>> y = np.array([4, 5, 6])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> b.index\n",
+      "     |      0\n",
+      "     |      >>> next(b), next(b), next(b)\n",
+      "     |      ((1, 4), (1, 5), (1, 6))\n",
+      "     |      >>> b.index\n",
+      "     |      3\n",
+      "     |  \n",
+      "     |  iters\n",
+      "     |      tuple of iterators along ``self``'s \"components.\"\n",
+      "     |      \n",
+      "     |      Returns a tuple of `numpy.flatiter` objects, one for each \"component\"\n",
+      "     |      of ``self``.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.flatiter\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> y = np.array([[4], [5], [6]])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> row, col = b.iters\n",
+      "     |      >>> next(row), next(col)\n",
+      "     |      (1, 4)\n",
+      "     |  \n",
+      "     |  nd\n",
+      "     |      Number of dimensions of broadcasted result. For code intended for NumPy\n",
+      "     |      1.12.0 and later the more consistent `ndim` is preferred.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> y = np.array([[4], [5], [6]])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> b.nd\n",
+      "     |      2\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      Number of dimensions of broadcasted result. Alias for `nd`.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.12.0\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> y = np.array([[4], [5], [6]])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> b.ndim\n",
+      "     |      2\n",
+      "     |  \n",
+      "     |  numiter\n",
+      "     |      Number of iterators possessed by the broadcasted result.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> y = np.array([[4], [5], [6]])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> b.numiter\n",
+      "     |      2\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      Shape of broadcasted result.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> y = np.array([[4], [5], [6]])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> b.shape\n",
+      "     |      (3, 3)\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      Total size of broadcasted result.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> y = np.array([[4], [5], [6]])\n",
+      "     |      >>> b = np.broadcast(x, y)\n",
+      "     |      >>> b.size\n",
+      "     |      9\n",
+      "    \n",
+      "    class busdaycalendar(builtins.object)\n",
+      "     |  busdaycalendar(weekmask='1111100', holidays=None)\n",
+      "     |  \n",
+      "     |  A business day calendar object that efficiently stores information\n",
+      "     |  defining valid days for the busday family of functions.\n",
+      "     |  \n",
+      "     |  The default valid days are Monday through Friday (\"business days\").\n",
+      "     |  A busdaycalendar object can be specified with any set of weekly\n",
+      "     |  valid days, plus an optional \"holiday\" dates that always will be invalid.\n",
+      "     |  \n",
+      "     |  Once a busdaycalendar object is created, the weekmask and holidays\n",
+      "     |  cannot be modified.\n",
+      "     |  \n",
+      "     |  .. versionadded:: 1.7.0\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  weekmask : str or array_like of bool, optional\n",
+      "     |      A seven-element array indicating which of Monday through Sunday are\n",
+      "     |      valid days. May be specified as a length-seven list or array, like\n",
+      "     |      [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n",
+      "     |      like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n",
+      "     |      weekdays, optionally separated by white space. Valid abbreviations\n",
+      "     |      are: Mon Tue Wed Thu Fri Sat Sun\n",
+      "     |  holidays : array_like of datetime64[D], optional\n",
+      "     |      An array of dates to consider as invalid dates, no matter which\n",
+      "     |      weekday they fall upon.  Holiday dates may be specified in any\n",
+      "     |      order, and NaT (not-a-time) dates are ignored.  This list is\n",
+      "     |      saved in a normalized form that is suited for fast calculations\n",
+      "     |      of valid days.\n",
+      "     |  \n",
+      "     |  Returns\n",
+      "     |  -------\n",
+      "     |  out : busdaycalendar\n",
+      "     |      A business day calendar object containing the specified\n",
+      "     |      weekmask and holidays values.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  is_busday : Returns a boolean array indicating valid days.\n",
+      "     |  busday_offset : Applies an offset counted in valid days.\n",
+      "     |  busday_count : Counts how many valid days are in a half-open date range.\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  Note: once a busdaycalendar object is created, you cannot modify the\n",
+      "     |  weekmask or holidays.  The attributes return copies of internal data.\n",
+      "     |  weekmask : (copy) seven-element array of bool\n",
+      "     |  holidays : (copy) sorted array of datetime64[D]\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> # Some important days in July\n",
+      "     |  ... bdd = np.busdaycalendar(\n",
+      "     |  ...             holidays=['2011-07-01', '2011-07-04', '2011-07-17'])\n",
+      "     |  >>> # Default is Monday to Friday weekdays\n",
+      "     |  ... bdd.weekmask\n",
+      "     |  array([ True,  True,  True,  True,  True, False, False])\n",
+      "     |  >>> # Any holidays already on the weekend are removed\n",
+      "     |  ... bdd.holidays\n",
+      "     |  array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]')\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __init__(self, /, *args, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  holidays\n",
+      "     |      A copy of the holiday array indicating additional invalid days.\n",
+      "     |  \n",
+      "     |  weekmask\n",
+      "     |      A copy of the seven-element boolean mask indicating valid days.\n",
+      "    \n",
+      "    byte = class int8(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``char``.\n",
+      "     |  Character code: ``'b'``.\n",
+      "     |  Canonical name: ``np.byte``.\n",
+      "     |  Alias *on this platform*: ``np.int8``: 8-bit signed integer (-128 to 127).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int8\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    bytes0 = class bytes_(builtins.bytes, character)\n",
+      "     |  bytes(iterable_of_ints) -> bytes\n",
+      "     |  bytes(string, encoding[, errors]) -> bytes\n",
+      "     |  bytes(bytes_or_buffer) -> immutable copy of bytes_or_buffer\n",
+      "     |  bytes(int) -> bytes object of size given by the parameter initialized with null bytes\n",
+      "     |  bytes() -> empty bytes object\n",
+      "     |  \n",
+      "     |  Construct an immutable array of bytes from:\n",
+      "     |    - an iterable yielding integers in range(256)\n",
+      "     |    - a text string encoded using the specified encoding\n",
+      "     |    - any object implementing the buffer API.\n",
+      "     |    - an integer\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      bytes_\n",
+      "     |      builtins.bytes\n",
+      "     |      character\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  capitalize(...)\n",
+      "     |      B.capitalize() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with only its first character capitalized (ASCII)\n",
+      "     |      and the rest lower-cased.\n",
+      "     |  \n",
+      "     |  center(...)\n",
+      "     |      B.center(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B centered in a string of length width.  Padding is\n",
+      "     |      done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  count(...)\n",
+      "     |      B.count(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the number of non-overlapping occurrences of subsection sub in\n",
+      "     |      bytes B[start:end].  Optional arguments start and end are interpreted\n",
+      "     |      as in slice notation.\n",
+      "     |  \n",
+      "     |  decode(self, /, encoding='utf-8', errors='strict')\n",
+      "     |      Decode the bytes using the codec registered for encoding.\n",
+      "     |      \n",
+      "     |      encoding\n",
+      "     |        The encoding with which to decode the bytes.\n",
+      "     |      errors\n",
+      "     |        The error handling scheme to use for the handling of decoding errors.\n",
+      "     |        The default is 'strict' meaning that decoding errors raise a\n",
+      "     |        UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n",
+      "     |        as well as any other name registered with codecs.register_error that\n",
+      "     |        can handle UnicodeDecodeErrors.\n",
+      "     |  \n",
+      "     |  endswith(...)\n",
+      "     |      B.endswith(suffix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if B ends with the specified suffix, False otherwise.\n",
+      "     |      With optional start, test B beginning at that position.\n",
+      "     |      With optional end, stop comparing B at that position.\n",
+      "     |      suffix can also be a tuple of bytes to try.\n",
+      "     |  \n",
+      "     |  expandtabs(...)\n",
+      "     |      B.expandtabs(tabsize=8) -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B where all tab characters are expanded using spaces.\n",
+      "     |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
+      "     |  \n",
+      "     |  find(...)\n",
+      "     |      B.find(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  hex(...)\n",
+      "     |      B.hex() -> string\n",
+      "     |      \n",
+      "     |      Create a string of hexadecimal numbers from a bytes object.\n",
+      "     |      Example: b'\\xb9\\x01\\xef'.hex() -> 'b901ef'.\n",
+      "     |  \n",
+      "     |  index(...)\n",
+      "     |      B.index(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the subsection is not found.\n",
+      "     |  \n",
+      "     |  isalnum(...)\n",
+      "     |      B.isalnum() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are alphanumeric\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isalpha(...)\n",
+      "     |      B.isalpha() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are alphabetic\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isascii(...)\n",
+      "     |      B.isascii() -> bool\n",
+      "     |      \n",
+      "     |      Return True if B is empty or all characters in B are ASCII,\n",
+      "     |      False otherwise.\n",
+      "     |  \n",
+      "     |  isdigit(...)\n",
+      "     |      B.isdigit() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are digits\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  islower(...)\n",
+      "     |      B.islower() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all cased characters in B are lowercase and there is\n",
+      "     |      at least one cased character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isspace(...)\n",
+      "     |      B.isspace() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are whitespace\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  istitle(...)\n",
+      "     |      B.istitle() -> bool\n",
+      "     |      \n",
+      "     |      Return True if B is a titlecased string and there is at least one\n",
+      "     |      character in B, i.e. uppercase characters may only follow uncased\n",
+      "     |      characters and lowercase characters only cased ones. Return False\n",
+      "     |      otherwise.\n",
+      "     |  \n",
+      "     |  isupper(...)\n",
+      "     |      B.isupper() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all cased characters in B are uppercase and there is\n",
+      "     |      at least one cased character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  join(self, iterable_of_bytes, /)\n",
+      "     |      Concatenate any number of bytes objects.\n",
+      "     |      \n",
+      "     |      The bytes whose method is called is inserted in between each pair.\n",
+      "     |      \n",
+      "     |      The result is returned as a new bytes object.\n",
+      "     |      \n",
+      "     |      Example: b'.'.join([b'ab', b'pq', b'rs']) -> b'ab.pq.rs'.\n",
+      "     |  \n",
+      "     |  ljust(...)\n",
+      "     |      B.ljust(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B left justified in a string of length width. Padding is\n",
+      "     |      done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  lower(...)\n",
+      "     |      B.lower() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with all ASCII characters converted to lowercase.\n",
+      "     |  \n",
+      "     |  lstrip(self, bytes=None, /)\n",
+      "     |      Strip leading bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip leading  ASCII whitespace.\n",
+      "     |  \n",
+      "     |  partition(self, sep, /)\n",
+      "     |      Partition the bytes into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator sep in the bytes. If the separator is found,\n",
+      "     |      returns a 3-tuple containing the part before the separator, the separator\n",
+      "     |      itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing the original bytes\n",
+      "     |      object and two empty bytes objects.\n",
+      "     |  \n",
+      "     |  replace(self, old, new, count=-1, /)\n",
+      "     |      Return a copy with all occurrences of substring old replaced by new.\n",
+      "     |      \n",
+      "     |        count\n",
+      "     |          Maximum number of occurrences to replace.\n",
+      "     |          -1 (the default value) means replace all occurrences.\n",
+      "     |      \n",
+      "     |      If the optional argument count is given, only the first count occurrences are\n",
+      "     |      replaced.\n",
+      "     |  \n",
+      "     |  rfind(...)\n",
+      "     |      B.rfind(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  rindex(...)\n",
+      "     |      B.rindex(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raise ValueError when the subsection is not found.\n",
+      "     |  \n",
+      "     |  rjust(...)\n",
+      "     |      B.rjust(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B right justified in a string of length width. Padding is\n",
+      "     |      done using the specified fill character (default is a space)\n",
+      "     |  \n",
+      "     |  rpartition(self, sep, /)\n",
+      "     |      Partition the bytes into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator sep in the bytes, starting at the end. If\n",
+      "     |      the separator is found, returns a 3-tuple containing the part before the\n",
+      "     |      separator, the separator itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing two empty bytes\n",
+      "     |      objects and the original bytes object.\n",
+      "     |  \n",
+      "     |  rsplit(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the sections in the bytes, using sep as the delimiter.\n",
+      "     |      \n",
+      "     |        sep\n",
+      "     |          The delimiter according which to split the bytes.\n",
+      "     |          None (the default value) means split on ASCII whitespace characters\n",
+      "     |          (space, tab, return, newline, formfeed, vertical tab).\n",
+      "     |        maxsplit\n",
+      "     |          Maximum number of splits to do.\n",
+      "     |          -1 (the default value) means no limit.\n",
+      "     |      \n",
+      "     |      Splitting is done starting at the end of the bytes and working to the front.\n",
+      "     |  \n",
+      "     |  rstrip(self, bytes=None, /)\n",
+      "     |      Strip trailing bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip trailing ASCII whitespace.\n",
+      "     |  \n",
+      "     |  split(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the sections in the bytes, using sep as the delimiter.\n",
+      "     |      \n",
+      "     |      sep\n",
+      "     |        The delimiter according which to split the bytes.\n",
+      "     |        None (the default value) means split on ASCII whitespace characters\n",
+      "     |        (space, tab, return, newline, formfeed, vertical tab).\n",
+      "     |      maxsplit\n",
+      "     |        Maximum number of splits to do.\n",
+      "     |        -1 (the default value) means no limit.\n",
+      "     |  \n",
+      "     |  splitlines(self, /, keepends=False)\n",
+      "     |      Return a list of the lines in the bytes, breaking at line boundaries.\n",
+      "     |      \n",
+      "     |      Line breaks are not included in the resulting list unless keepends is given and\n",
+      "     |      true.\n",
+      "     |  \n",
+      "     |  startswith(...)\n",
+      "     |      B.startswith(prefix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if B starts with the specified prefix, False otherwise.\n",
+      "     |      With optional start, test B beginning at that position.\n",
+      "     |      With optional end, stop comparing B at that position.\n",
+      "     |      prefix can also be a tuple of bytes to try.\n",
+      "     |  \n",
+      "     |  strip(self, bytes=None, /)\n",
+      "     |      Strip leading and trailing bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip leading and trailing ASCII whitespace.\n",
+      "     |  \n",
+      "     |  swapcase(...)\n",
+      "     |      B.swapcase() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with uppercase ASCII characters converted\n",
+      "     |      to lowercase ASCII and vice versa.\n",
+      "     |  \n",
+      "     |  title(...)\n",
+      "     |      B.title() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a titlecased version of B, i.e. ASCII words start with uppercase\n",
+      "     |      characters, all remaining cased characters have lowercase.\n",
+      "     |  \n",
+      "     |  translate(self, table, /, delete=b'')\n",
+      "     |      Return a copy with each character mapped by the given translation table.\n",
+      "     |      \n",
+      "     |        table\n",
+      "     |          Translation table, which must be a bytes object of length 256.\n",
+      "     |      \n",
+      "     |      All characters occurring in the optional argument delete are removed.\n",
+      "     |      The remaining characters are mapped through the given translation table.\n",
+      "     |  \n",
+      "     |  upper(...)\n",
+      "     |      B.upper() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with all ASCII characters converted to uppercase.\n",
+      "     |  \n",
+      "     |  zfill(...)\n",
+      "     |      B.zfill(width) -> copy of B\n",
+      "     |      \n",
+      "     |      Pad a numeric string B with zeros on the left, to fill a field\n",
+      "     |      of the specified width.  B is never truncated.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Class methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  fromhex(string, /) from builtins.type\n",
+      "     |      Create a bytes object from a string of hexadecimal numbers.\n",
+      "     |      \n",
+      "     |      Spaces between two numbers are accepted.\n",
+      "     |      Example: bytes.fromhex('B9 01EF') -> b'\\\\xb9\\\\x01\\\\xef'.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  maketrans(frm, to, /)\n",
+      "     |      Return a translation table useable for the bytes or bytearray translate method.\n",
+      "     |      \n",
+      "     |      The returned table will be one where each byte in frm is mapped to the byte at\n",
+      "     |      the same position in to.\n",
+      "     |      \n",
+      "     |      The bytes objects frm and to must be of the same length.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class bytes_(builtins.bytes, character)\n",
+      "     |  bytes(iterable_of_ints) -> bytes\n",
+      "     |  bytes(string, encoding[, errors]) -> bytes\n",
+      "     |  bytes(bytes_or_buffer) -> immutable copy of bytes_or_buffer\n",
+      "     |  bytes(int) -> bytes object of size given by the parameter initialized with null bytes\n",
+      "     |  bytes() -> empty bytes object\n",
+      "     |  \n",
+      "     |  Construct an immutable array of bytes from:\n",
+      "     |    - an iterable yielding integers in range(256)\n",
+      "     |    - a text string encoded using the specified encoding\n",
+      "     |    - any object implementing the buffer API.\n",
+      "     |    - an integer\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      bytes_\n",
+      "     |      builtins.bytes\n",
+      "     |      character\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  capitalize(...)\n",
+      "     |      B.capitalize() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with only its first character capitalized (ASCII)\n",
+      "     |      and the rest lower-cased.\n",
+      "     |  \n",
+      "     |  center(...)\n",
+      "     |      B.center(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B centered in a string of length width.  Padding is\n",
+      "     |      done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  count(...)\n",
+      "     |      B.count(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the number of non-overlapping occurrences of subsection sub in\n",
+      "     |      bytes B[start:end].  Optional arguments start and end are interpreted\n",
+      "     |      as in slice notation.\n",
+      "     |  \n",
+      "     |  decode(self, /, encoding='utf-8', errors='strict')\n",
+      "     |      Decode the bytes using the codec registered for encoding.\n",
+      "     |      \n",
+      "     |      encoding\n",
+      "     |        The encoding with which to decode the bytes.\n",
+      "     |      errors\n",
+      "     |        The error handling scheme to use for the handling of decoding errors.\n",
+      "     |        The default is 'strict' meaning that decoding errors raise a\n",
+      "     |        UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n",
+      "     |        as well as any other name registered with codecs.register_error that\n",
+      "     |        can handle UnicodeDecodeErrors.\n",
+      "     |  \n",
+      "     |  endswith(...)\n",
+      "     |      B.endswith(suffix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if B ends with the specified suffix, False otherwise.\n",
+      "     |      With optional start, test B beginning at that position.\n",
+      "     |      With optional end, stop comparing B at that position.\n",
+      "     |      suffix can also be a tuple of bytes to try.\n",
+      "     |  \n",
+      "     |  expandtabs(...)\n",
+      "     |      B.expandtabs(tabsize=8) -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B where all tab characters are expanded using spaces.\n",
+      "     |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
+      "     |  \n",
+      "     |  find(...)\n",
+      "     |      B.find(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  hex(...)\n",
+      "     |      B.hex() -> string\n",
+      "     |      \n",
+      "     |      Create a string of hexadecimal numbers from a bytes object.\n",
+      "     |      Example: b'\\xb9\\x01\\xef'.hex() -> 'b901ef'.\n",
+      "     |  \n",
+      "     |  index(...)\n",
+      "     |      B.index(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the subsection is not found.\n",
+      "     |  \n",
+      "     |  isalnum(...)\n",
+      "     |      B.isalnum() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are alphanumeric\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isalpha(...)\n",
+      "     |      B.isalpha() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are alphabetic\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isascii(...)\n",
+      "     |      B.isascii() -> bool\n",
+      "     |      \n",
+      "     |      Return True if B is empty or all characters in B are ASCII,\n",
+      "     |      False otherwise.\n",
+      "     |  \n",
+      "     |  isdigit(...)\n",
+      "     |      B.isdigit() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are digits\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  islower(...)\n",
+      "     |      B.islower() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all cased characters in B are lowercase and there is\n",
+      "     |      at least one cased character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isspace(...)\n",
+      "     |      B.isspace() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are whitespace\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  istitle(...)\n",
+      "     |      B.istitle() -> bool\n",
+      "     |      \n",
+      "     |      Return True if B is a titlecased string and there is at least one\n",
+      "     |      character in B, i.e. uppercase characters may only follow uncased\n",
+      "     |      characters and lowercase characters only cased ones. Return False\n",
+      "     |      otherwise.\n",
+      "     |  \n",
+      "     |  isupper(...)\n",
+      "     |      B.isupper() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all cased characters in B are uppercase and there is\n",
+      "     |      at least one cased character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  join(self, iterable_of_bytes, /)\n",
+      "     |      Concatenate any number of bytes objects.\n",
+      "     |      \n",
+      "     |      The bytes whose method is called is inserted in between each pair.\n",
+      "     |      \n",
+      "     |      The result is returned as a new bytes object.\n",
+      "     |      \n",
+      "     |      Example: b'.'.join([b'ab', b'pq', b'rs']) -> b'ab.pq.rs'.\n",
+      "     |  \n",
+      "     |  ljust(...)\n",
+      "     |      B.ljust(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B left justified in a string of length width. Padding is\n",
+      "     |      done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  lower(...)\n",
+      "     |      B.lower() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with all ASCII characters converted to lowercase.\n",
+      "     |  \n",
+      "     |  lstrip(self, bytes=None, /)\n",
+      "     |      Strip leading bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip leading  ASCII whitespace.\n",
+      "     |  \n",
+      "     |  partition(self, sep, /)\n",
+      "     |      Partition the bytes into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator sep in the bytes. If the separator is found,\n",
+      "     |      returns a 3-tuple containing the part before the separator, the separator\n",
+      "     |      itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing the original bytes\n",
+      "     |      object and two empty bytes objects.\n",
+      "     |  \n",
+      "     |  replace(self, old, new, count=-1, /)\n",
+      "     |      Return a copy with all occurrences of substring old replaced by new.\n",
+      "     |      \n",
+      "     |        count\n",
+      "     |          Maximum number of occurrences to replace.\n",
+      "     |          -1 (the default value) means replace all occurrences.\n",
+      "     |      \n",
+      "     |      If the optional argument count is given, only the first count occurrences are\n",
+      "     |      replaced.\n",
+      "     |  \n",
+      "     |  rfind(...)\n",
+      "     |      B.rfind(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  rindex(...)\n",
+      "     |      B.rindex(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raise ValueError when the subsection is not found.\n",
+      "     |  \n",
+      "     |  rjust(...)\n",
+      "     |      B.rjust(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B right justified in a string of length width. Padding is\n",
+      "     |      done using the specified fill character (default is a space)\n",
+      "     |  \n",
+      "     |  rpartition(self, sep, /)\n",
+      "     |      Partition the bytes into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator sep in the bytes, starting at the end. If\n",
+      "     |      the separator is found, returns a 3-tuple containing the part before the\n",
+      "     |      separator, the separator itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing two empty bytes\n",
+      "     |      objects and the original bytes object.\n",
+      "     |  \n",
+      "     |  rsplit(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the sections in the bytes, using sep as the delimiter.\n",
+      "     |      \n",
+      "     |        sep\n",
+      "     |          The delimiter according which to split the bytes.\n",
+      "     |          None (the default value) means split on ASCII whitespace characters\n",
+      "     |          (space, tab, return, newline, formfeed, vertical tab).\n",
+      "     |        maxsplit\n",
+      "     |          Maximum number of splits to do.\n",
+      "     |          -1 (the default value) means no limit.\n",
+      "     |      \n",
+      "     |      Splitting is done starting at the end of the bytes and working to the front.\n",
+      "     |  \n",
+      "     |  rstrip(self, bytes=None, /)\n",
+      "     |      Strip trailing bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip trailing ASCII whitespace.\n",
+      "     |  \n",
+      "     |  split(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the sections in the bytes, using sep as the delimiter.\n",
+      "     |      \n",
+      "     |      sep\n",
+      "     |        The delimiter according which to split the bytes.\n",
+      "     |        None (the default value) means split on ASCII whitespace characters\n",
+      "     |        (space, tab, return, newline, formfeed, vertical tab).\n",
+      "     |      maxsplit\n",
+      "     |        Maximum number of splits to do.\n",
+      "     |        -1 (the default value) means no limit.\n",
+      "     |  \n",
+      "     |  splitlines(self, /, keepends=False)\n",
+      "     |      Return a list of the lines in the bytes, breaking at line boundaries.\n",
+      "     |      \n",
+      "     |      Line breaks are not included in the resulting list unless keepends is given and\n",
+      "     |      true.\n",
+      "     |  \n",
+      "     |  startswith(...)\n",
+      "     |      B.startswith(prefix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if B starts with the specified prefix, False otherwise.\n",
+      "     |      With optional start, test B beginning at that position.\n",
+      "     |      With optional end, stop comparing B at that position.\n",
+      "     |      prefix can also be a tuple of bytes to try.\n",
+      "     |  \n",
+      "     |  strip(self, bytes=None, /)\n",
+      "     |      Strip leading and trailing bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip leading and trailing ASCII whitespace.\n",
+      "     |  \n",
+      "     |  swapcase(...)\n",
+      "     |      B.swapcase() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with uppercase ASCII characters converted\n",
+      "     |      to lowercase ASCII and vice versa.\n",
+      "     |  \n",
+      "     |  title(...)\n",
+      "     |      B.title() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a titlecased version of B, i.e. ASCII words start with uppercase\n",
+      "     |      characters, all remaining cased characters have lowercase.\n",
+      "     |  \n",
+      "     |  translate(self, table, /, delete=b'')\n",
+      "     |      Return a copy with each character mapped by the given translation table.\n",
+      "     |      \n",
+      "     |        table\n",
+      "     |          Translation table, which must be a bytes object of length 256.\n",
+      "     |      \n",
+      "     |      All characters occurring in the optional argument delete are removed.\n",
+      "     |      The remaining characters are mapped through the given translation table.\n",
+      "     |  \n",
+      "     |  upper(...)\n",
+      "     |      B.upper() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with all ASCII characters converted to uppercase.\n",
+      "     |  \n",
+      "     |  zfill(...)\n",
+      "     |      B.zfill(width) -> copy of B\n",
+      "     |      \n",
+      "     |      Pad a numeric string B with zeros on the left, to fill a field\n",
+      "     |      of the specified width.  B is never truncated.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Class methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  fromhex(string, /) from builtins.type\n",
+      "     |      Create a bytes object from a string of hexadecimal numbers.\n",
+      "     |      \n",
+      "     |      Spaces between two numbers are accepted.\n",
+      "     |      Example: bytes.fromhex('B9 01EF') -> b'\\\\xb9\\\\x01\\\\xef'.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  maketrans(frm, to, /)\n",
+      "     |      Return a translation table useable for the bytes or bytearray translate method.\n",
+      "     |      \n",
+      "     |      The returned table will be one where each byte in frm is mapped to the byte at\n",
+      "     |      the same position in to.\n",
+      "     |      \n",
+      "     |      The bytes objects frm and to must be of the same length.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    cdouble = class complex128(complexfloating, builtins.complex)\n",
+      "     |  cdouble(real=0, imag=0)\n",
+      "     |  \n",
+      "     |  Complex number type composed of two double-precision floating-point\n",
+      "     |  numbers, compatible with Python `complex`.\n",
+      "     |  Character code: ``'D'``.\n",
+      "     |  Canonical name: ``np.cdouble``.\n",
+      "     |  Alias: ``np.cfloat``.\n",
+      "     |  Alias: ``np.complex_``.\n",
+      "     |  Alias *on this platform*: ``np.complex128``: Complex number type composed of 2 64-bit-precision floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complex128\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.complex\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.complex:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "    \n",
+      "    cfloat = class complex128(complexfloating, builtins.complex)\n",
+      "     |  cfloat(real=0, imag=0)\n",
+      "     |  \n",
+      "     |  Complex number type composed of two double-precision floating-point\n",
+      "     |  numbers, compatible with Python `complex`.\n",
+      "     |  Character code: ``'D'``.\n",
+      "     |  Canonical name: ``np.cdouble``.\n",
+      "     |  Alias: ``np.cfloat``.\n",
+      "     |  Alias: ``np.complex_``.\n",
+      "     |  Alias *on this platform*: ``np.complex128``: Complex number type composed of 2 64-bit-precision floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complex128\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.complex\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.complex:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "    \n",
+      "    class character(flexible)\n",
+      "     |  Abstract base class of all character string scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      character\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class chararray(ndarray)\n",
+      "     |  chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0, strides=None, order='C')\n",
+      "     |  \n",
+      "     |  chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,\n",
+      "     |            strides=None, order=None)\n",
+      "     |  \n",
+      "     |  Provides a convenient view on arrays of string and unicode values.\n",
+      "     |  \n",
+      "     |  .. note::\n",
+      "     |     The `chararray` class exists for backwards compatibility with\n",
+      "     |     Numarray, it is not recommended for new development. Starting from numpy\n",
+      "     |     1.4, if one needs arrays of strings, it is recommended to use arrays of\n",
+      "     |     `dtype` `object_`, `string_` or `unicode_`, and use the free functions\n",
+      "     |     in the `numpy.char` module for fast vectorized string operations.\n",
+      "     |  \n",
+      "     |  Versus a regular NumPy array of type `str` or `unicode`, this\n",
+      "     |  class adds the following functionality:\n",
+      "     |  \n",
+      "     |    1) values automatically have whitespace removed from the end\n",
+      "     |       when indexed\n",
+      "     |  \n",
+      "     |    2) comparison operators automatically remove whitespace from the\n",
+      "     |       end when comparing values\n",
+      "     |  \n",
+      "     |    3) vectorized string operations are provided as methods\n",
+      "     |       (e.g. `.endswith`) and infix operators (e.g. ``\"+\", \"*\", \"%\"``)\n",
+      "     |  \n",
+      "     |  chararrays should be created using `numpy.char.array` or\n",
+      "     |  `numpy.char.asarray`, rather than this constructor directly.\n",
+      "     |  \n",
+      "     |  This constructor creates the array, using `buffer` (with `offset`\n",
+      "     |  and `strides`) if it is not ``None``. If `buffer` is ``None``, then\n",
+      "     |  constructs a new array with `strides` in \"C order\", unless both\n",
+      "     |  ``len(shape) >= 2`` and ``order='F'``, in which case `strides`\n",
+      "     |  is in \"Fortran order\".\n",
+      "     |  \n",
+      "     |  Methods\n",
+      "     |  -------\n",
+      "     |  astype\n",
+      "     |  argsort\n",
+      "     |  copy\n",
+      "     |  count\n",
+      "     |  decode\n",
+      "     |  dump\n",
+      "     |  dumps\n",
+      "     |  encode\n",
+      "     |  endswith\n",
+      "     |  expandtabs\n",
+      "     |  fill\n",
+      "     |  find\n",
+      "     |  flatten\n",
+      "     |  getfield\n",
+      "     |  index\n",
+      "     |  isalnum\n",
+      "     |  isalpha\n",
+      "     |  isdecimal\n",
+      "     |  isdigit\n",
+      "     |  islower\n",
+      "     |  isnumeric\n",
+      "     |  isspace\n",
+      "     |  istitle\n",
+      "     |  isupper\n",
+      "     |  item\n",
+      "     |  join\n",
+      "     |  ljust\n",
+      "     |  lower\n",
+      "     |  lstrip\n",
+      "     |  nonzero\n",
+      "     |  put\n",
+      "     |  ravel\n",
+      "     |  repeat\n",
+      "     |  replace\n",
+      "     |  reshape\n",
+      "     |  resize\n",
+      "     |  rfind\n",
+      "     |  rindex\n",
+      "     |  rjust\n",
+      "     |  rsplit\n",
+      "     |  rstrip\n",
+      "     |  searchsorted\n",
+      "     |  setfield\n",
+      "     |  setflags\n",
+      "     |  sort\n",
+      "     |  split\n",
+      "     |  splitlines\n",
+      "     |  squeeze\n",
+      "     |  startswith\n",
+      "     |  strip\n",
+      "     |  swapaxes\n",
+      "     |  swapcase\n",
+      "     |  take\n",
+      "     |  title\n",
+      "     |  tofile\n",
+      "     |  tolist\n",
+      "     |  tostring\n",
+      "     |  translate\n",
+      "     |  transpose\n",
+      "     |  upper\n",
+      "     |  view\n",
+      "     |  zfill\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  shape : tuple\n",
+      "     |      Shape of the array.\n",
+      "     |  itemsize : int, optional\n",
+      "     |      Length of each array element, in number of characters. Default is 1.\n",
+      "     |  unicode : bool, optional\n",
+      "     |      Are the array elements of type unicode (True) or string (False).\n",
+      "     |      Default is False.\n",
+      "     |  buffer : object exposing the buffer interface or str, optional\n",
+      "     |      Memory address of the start of the array data.  Default is None,\n",
+      "     |      in which case a new array is created.\n",
+      "     |  offset : int, optional\n",
+      "     |      Fixed stride displacement from the beginning of an axis?\n",
+      "     |      Default is 0. Needs to be >=0.\n",
+      "     |  strides : array_like of ints, optional\n",
+      "     |      Strides for the array (see `ndarray.strides` for full description).\n",
+      "     |      Default is None.\n",
+      "     |  order : {'C', 'F'}, optional\n",
+      "     |      The order in which the array data is stored in memory: 'C' ->\n",
+      "     |      \"row major\" order (the default), 'F' -> \"column major\"\n",
+      "     |      (Fortran) order.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> charar = np.chararray((3, 3))\n",
+      "     |  >>> charar[:] = 'a'\n",
+      "     |  >>> charar\n",
+      "     |  chararray([[b'a', b'a', b'a'],\n",
+      "     |             [b'a', b'a', b'a'],\n",
+      "     |             [b'a', b'a', b'a']], dtype='|S1')\n",
+      "     |  \n",
+      "     |  >>> charar = np.chararray(charar.shape, itemsize=5)\n",
+      "     |  >>> charar[:] = 'abc'\n",
+      "     |  >>> charar\n",
+      "     |  chararray([[b'abc', b'abc', b'abc'],\n",
+      "     |             [b'abc', b'abc', b'abc'],\n",
+      "     |             [b'abc', b'abc', b'abc']], dtype='|S5')\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      chararray\n",
+      "     |      ndarray\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __add__(self, other)\n",
+      "     |      Return (self + other), that is string concatenation,\n",
+      "     |      element-wise for a pair of array_likes of str or unicode.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      add\n",
+      "     |  \n",
+      "     |  __array_finalize__(self, obj)\n",
+      "     |      None.\n",
+      "     |  \n",
+      "     |  __eq__(self, other)\n",
+      "     |      Return (self == other) element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      equal\n",
+      "     |  \n",
+      "     |  __ge__(self, other)\n",
+      "     |      Return (self >= other) element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      greater_equal\n",
+      "     |  \n",
+      "     |  __getitem__(self, obj)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, other)\n",
+      "     |      Return (self > other) element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      greater\n",
+      "     |  \n",
+      "     |  __le__(self, other)\n",
+      "     |      Return (self <= other) element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      less_equal\n",
+      "     |  \n",
+      "     |  __lt__(self, other)\n",
+      "     |      Return (self < other) element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      less\n",
+      "     |  \n",
+      "     |  __mod__(self, i)\n",
+      "     |      Return (self % i), that is pre-Python 2.6 string formatting\n",
+      "     |      (interpolation), element-wise for a pair of array_likes of `string_`\n",
+      "     |      or `unicode_`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      mod\n",
+      "     |  \n",
+      "     |  __mul__(self, i)\n",
+      "     |      Return (self * i), that is string multiple concatenation,\n",
+      "     |      element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      multiply\n",
+      "     |  \n",
+      "     |  __ne__(self, other)\n",
+      "     |      Return (self != other) element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      not_equal\n",
+      "     |  \n",
+      "     |  __radd__(self, other)\n",
+      "     |      Return (other + self), that is string concatenation,\n",
+      "     |      element-wise for a pair of array_likes of `string_` or `unicode_`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      add\n",
+      "     |  \n",
+      "     |  __rmod__(self, other)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, i)\n",
+      "     |      Return (self * i), that is string multiple concatenation,\n",
+      "     |      element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      multiply\n",
+      "     |  \n",
+      "     |  argsort(self, axis=-1, kind=None, order=None)\n",
+      "     |      a.argsort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would sort this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argsort` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argsort : equivalent function\n",
+      "     |  \n",
+      "     |  capitalize(self)\n",
+      "     |      Return a copy of `self` with only the first character of each element\n",
+      "     |      capitalized.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.capitalize\n",
+      "     |  \n",
+      "     |  center(self, width, fillchar=' ')\n",
+      "     |      Return a copy of `self` with its elements centered in a\n",
+      "     |      string of length `width`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      center\n",
+      "     |  \n",
+      "     |  count(self, sub, start=0, end=None)\n",
+      "     |      Returns an array with the number of non-overlapping occurrences of\n",
+      "     |      substring `sub` in the range [`start`, `end`].\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.count\n",
+      "     |  \n",
+      "     |  decode(self, encoding=None, errors=None)\n",
+      "     |      Calls `str.decode` element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.decode\n",
+      "     |  \n",
+      "     |  encode(self, encoding=None, errors=None)\n",
+      "     |      Calls `str.encode` element-wise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.encode\n",
+      "     |  \n",
+      "     |  endswith(self, suffix, start=0, end=None)\n",
+      "     |      Returns a boolean array which is `True` where the string element\n",
+      "     |      in `self` ends with `suffix`, otherwise `False`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.endswith\n",
+      "     |  \n",
+      "     |  expandtabs(self, tabsize=8)\n",
+      "     |      Return a copy of each string element where all tab characters are\n",
+      "     |      replaced by one or more spaces.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.expandtabs\n",
+      "     |  \n",
+      "     |  find(self, sub, start=0, end=None)\n",
+      "     |      For each element, return the lowest index in the string where\n",
+      "     |      substring `sub` is found.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.find\n",
+      "     |  \n",
+      "     |  index(self, sub, start=0, end=None)\n",
+      "     |      Like `find`, but raises `ValueError` when the substring is not found.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.index\n",
+      "     |  \n",
+      "     |  isalnum(self)\n",
+      "     |      Returns true for each element if all characters in the string\n",
+      "     |      are alphanumeric and there is at least one character, false\n",
+      "     |      otherwise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.isalnum\n",
+      "     |  \n",
+      "     |  isalpha(self)\n",
+      "     |      Returns true for each element if all characters in the string\n",
+      "     |      are alphabetic and there is at least one character, false\n",
+      "     |      otherwise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.isalpha\n",
+      "     |  \n",
+      "     |  isdecimal(self)\n",
+      "     |      For each element in `self`, return True if there are only\n",
+      "     |      decimal characters in the element.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.isdecimal\n",
+      "     |  \n",
+      "     |  isdigit(self)\n",
+      "     |      Returns true for each element if all characters in the string are\n",
+      "     |      digits and there is at least one character, false otherwise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.isdigit\n",
+      "     |  \n",
+      "     |  islower(self)\n",
+      "     |      Returns true for each element if all cased characters in the\n",
+      "     |      string are lowercase and there is at least one cased character,\n",
+      "     |      false otherwise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.islower\n",
+      "     |  \n",
+      "     |  isnumeric(self)\n",
+      "     |      For each element in `self`, return True if there are only\n",
+      "     |      numeric characters in the element.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.isnumeric\n",
+      "     |  \n",
+      "     |  isspace(self)\n",
+      "     |      Returns true for each element if there are only whitespace\n",
+      "     |      characters in the string and there is at least one character,\n",
+      "     |      false otherwise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.isspace\n",
+      "     |  \n",
+      "     |  istitle(self)\n",
+      "     |      Returns true for each element if the element is a titlecased\n",
+      "     |      string and there is at least one character, false otherwise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.istitle\n",
+      "     |  \n",
+      "     |  isupper(self)\n",
+      "     |      Returns true for each element if all cased characters in the\n",
+      "     |      string are uppercase and there is at least one character, false\n",
+      "     |      otherwise.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.isupper\n",
+      "     |  \n",
+      "     |  join(self, seq)\n",
+      "     |      Return a string which is the concatenation of the strings in the\n",
+      "     |      sequence `seq`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.join\n",
+      "     |  \n",
+      "     |  ljust(self, width, fillchar=' ')\n",
+      "     |      Return an array with the elements of `self` left-justified in a\n",
+      "     |      string of length `width`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.ljust\n",
+      "     |  \n",
+      "     |  lower(self)\n",
+      "     |      Return an array with the elements of `self` converted to\n",
+      "     |      lowercase.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.lower\n",
+      "     |  \n",
+      "     |  lstrip(self, chars=None)\n",
+      "     |      For each element in `self`, return a copy with the leading characters\n",
+      "     |      removed.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.lstrip\n",
+      "     |  \n",
+      "     |  partition(self, sep)\n",
+      "     |      Partition each element in `self` around `sep`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      partition\n",
+      "     |  \n",
+      "     |  replace(self, old, new, count=None)\n",
+      "     |      For each element in `self`, return a copy of the string with all\n",
+      "     |      occurrences of substring `old` replaced by `new`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.replace\n",
+      "     |  \n",
+      "     |  rfind(self, sub, start=0, end=None)\n",
+      "     |      For each element in `self`, return the highest index in the string\n",
+      "     |      where substring `sub` is found, such that `sub` is contained\n",
+      "     |      within [`start`, `end`].\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.rfind\n",
+      "     |  \n",
+      "     |  rindex(self, sub, start=0, end=None)\n",
+      "     |      Like `rfind`, but raises `ValueError` when the substring `sub` is\n",
+      "     |      not found.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.rindex\n",
+      "     |  \n",
+      "     |  rjust(self, width, fillchar=' ')\n",
+      "     |      Return an array with the elements of `self`\n",
+      "     |      right-justified in a string of length `width`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.rjust\n",
+      "     |  \n",
+      "     |  rpartition(self, sep)\n",
+      "     |      Partition each element in `self` around `sep`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      rpartition\n",
+      "     |  \n",
+      "     |  rsplit(self, sep=None, maxsplit=None)\n",
+      "     |      For each element in `self`, return a list of the words in\n",
+      "     |      the string, using `sep` as the delimiter string.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.rsplit\n",
+      "     |  \n",
+      "     |  rstrip(self, chars=None)\n",
+      "     |      For each element in `self`, return a copy with the trailing\n",
+      "     |      characters removed.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.rstrip\n",
+      "     |  \n",
+      "     |  split(self, sep=None, maxsplit=None)\n",
+      "     |      For each element in `self`, return a list of the words in the\n",
+      "     |      string, using `sep` as the delimiter string.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.split\n",
+      "     |  \n",
+      "     |  splitlines(self, keepends=None)\n",
+      "     |      For each element in `self`, return a list of the lines in the\n",
+      "     |      element, breaking at line boundaries.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.splitlines\n",
+      "     |  \n",
+      "     |  startswith(self, prefix, start=0, end=None)\n",
+      "     |      Returns a boolean array which is `True` where the string element\n",
+      "     |      in `self` starts with `prefix`, otherwise `False`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.startswith\n",
+      "     |  \n",
+      "     |  strip(self, chars=None)\n",
+      "     |      For each element in `self`, return a copy with the leading and\n",
+      "     |      trailing characters removed.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.strip\n",
+      "     |  \n",
+      "     |  swapcase(self)\n",
+      "     |      For each element in `self`, return a copy of the string with\n",
+      "     |      uppercase characters converted to lowercase and vice versa.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.swapcase\n",
+      "     |  \n",
+      "     |  title(self)\n",
+      "     |      For each element in `self`, return a titlecased version of the\n",
+      "     |      string: words start with uppercase characters, all remaining cased\n",
+      "     |      characters are lowercase.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.title\n",
+      "     |  \n",
+      "     |  translate(self, table, deletechars=None)\n",
+      "     |      For each element in `self`, return a copy of the string where\n",
+      "     |      all characters occurring in the optional argument\n",
+      "     |      `deletechars` are removed, and the remaining characters have\n",
+      "     |      been mapped through the given translation table.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.translate\n",
+      "     |  \n",
+      "     |  upper(self)\n",
+      "     |      Return an array with the elements of `self` converted to\n",
+      "     |      uppercase.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.upper\n",
+      "     |  \n",
+      "     |  zfill(self, width)\n",
+      "     |      Return the numeric string left-filled with zeros in a string of\n",
+      "     |      length `width`.\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      char.zfill\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(subtype, shape, itemsize=1, unicode=False, buffer=None, offset=0, strides=None, order='C')\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      a.__array__([dtype], /) -> reference if type unchanged, copy otherwise.\n",
+      "     |      \n",
+      "     |      Returns either a new reference to self if dtype is not given or a new array\n",
+      "     |      of provided data type if dtype is different from the current dtype of the\n",
+      "     |      array.\n",
+      "     |  \n",
+      "     |  __array_function__(...)\n",
+      "     |  \n",
+      "     |  __array_prepare__(...)\n",
+      "     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n",
+      "     |  \n",
+      "     |  __array_ufunc__(...)\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |      a.__copy__()\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.copy` is called on an array. Returns a copy of the array.\n",
+      "     |      \n",
+      "     |      Equivalent to ``a.copy(order='K')``.\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |      a.__deepcopy__(memo, /) -> Deep copy of array.\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.deepcopy` is called on an array.\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      Default object formatter.\n",
+      "     |  \n",
+      "     |  __iadd__(self, value, /)\n",
+      "     |      Return self+=value.\n",
+      "     |  \n",
+      "     |  __iand__(self, value, /)\n",
+      "     |      Return self&=value.\n",
+      "     |  \n",
+      "     |  __ifloordiv__(self, value, /)\n",
+      "     |      Return self//=value.\n",
+      "     |  \n",
+      "     |  __ilshift__(self, value, /)\n",
+      "     |      Return self<<=value.\n",
+      "     |  \n",
+      "     |  __imatmul__(self, value, /)\n",
+      "     |      Return self@=value.\n",
+      "     |  \n",
+      "     |  __imod__(self, value, /)\n",
+      "     |      Return self%=value.\n",
+      "     |  \n",
+      "     |  __imul__(self, value, /)\n",
+      "     |      Return self*=value.\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __ior__(self, value, /)\n",
+      "     |      Return self|=value.\n",
+      "     |  \n",
+      "     |  __ipow__(self, value, /)\n",
+      "     |      Return self**=value.\n",
+      "     |  \n",
+      "     |  __irshift__(self, value, /)\n",
+      "     |      Return self>>=value.\n",
+      "     |  \n",
+      "     |  __isub__(self, value, /)\n",
+      "     |      Return self-=value.\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __itruediv__(self, value, /)\n",
+      "     |      Return self/=value.\n",
+      "     |  \n",
+      "     |  __ixor__(self, value, /)\n",
+      "     |      Return self^=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __matmul__(self, value, /)\n",
+      "     |      Return self@value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      a.__reduce__()\n",
+      "     |      \n",
+      "     |      For pickling.\n",
+      "     |  \n",
+      "     |  __reduce_ex__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmatmul__(self, value, /)\n",
+      "     |      Return value@self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |      a.__setstate__(state, /)\n",
+      "     |      \n",
+      "     |      For unpickling.\n",
+      "     |      \n",
+      "     |      The `state` argument must be a sequence that contains the following\n",
+      "     |      elements:\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      version : int\n",
+      "     |          optional pickle version. If omitted defaults to 0.\n",
+      "     |      shape : tuple\n",
+      "     |      dtype : data-type\n",
+      "     |      isFortran : bool\n",
+      "     |      rawdata : string or list\n",
+      "     |          a binary string with the data (or a list if 'a' is an object array)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      a.all(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if all elements evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.all` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.all : equivalent function\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      a.any(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if any of the elements of `a` evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.any` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.any : equivalent function\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      a.argmax(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the maximum values along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmax : equivalent function\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      a.argmin(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the minimum values along the given axis of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmin` for detailed documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmin : equivalent function\n",
+      "     |  \n",
+      "     |  argpartition(...)\n",
+      "     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would partition this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argpartition` for full documentation.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argpartition : equivalent function\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n",
+      "     |      \n",
+      "     |      Copy of the array, cast to a specified type.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          Typecode or data-type to which the array is cast.\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout order of the result.\n",
+      "     |          'C' means C order, 'F' means Fortran order, 'A'\n",
+      "     |          means 'F' order if all the arrays are Fortran contiguous,\n",
+      "     |          'C' order otherwise, and 'K' means as close to the\n",
+      "     |          order the array elements appear in memory as possible.\n",
+      "     |          Default is 'K'.\n",
+      "     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n",
+      "     |          Controls what kind of data casting may occur. Defaults to 'unsafe'\n",
+      "     |          for backwards compatibility.\n",
+      "     |      \n",
+      "     |            * 'no' means the data types should not be cast at all.\n",
+      "     |            * 'equiv' means only byte-order changes are allowed.\n",
+      "     |            * 'safe' means only casts which can preserve values are allowed.\n",
+      "     |            * 'same_kind' means only safe casts or casts within a kind,\n",
+      "     |              like float64 to float32, are allowed.\n",
+      "     |            * 'unsafe' means any data conversions may be done.\n",
+      "     |      subok : bool, optional\n",
+      "     |          If True, then sub-classes will be passed-through (default), otherwise\n",
+      "     |          the returned array will be forced to be a base-class array.\n",
+      "     |      copy : bool, optional\n",
+      "     |          By default, astype always returns a newly allocated array. If this\n",
+      "     |          is set to false, and the `dtype`, `order`, and `subok`\n",
+      "     |          requirements are satisfied, the input array is returned instead\n",
+      "     |          of a copy.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      arr_t : ndarray\n",
+      "     |          Unless `copy` is False and the other conditions for returning the input\n",
+      "     |          array are satisfied (see description for `copy` input parameter), `arr_t`\n",
+      "     |          is a new array of the same shape as the input array, with dtype, order\n",
+      "     |          given by `dtype`, `order`.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      .. versionchanged:: 1.17.0\n",
+      "     |         Casting between a simple data type and a structured one is possible only\n",
+      "     |         for \"unsafe\" casting.  Casting to multiple fields is allowed, but\n",
+      "     |         casting from multiple fields is not.\n",
+      "     |      \n",
+      "     |      .. versionchanged:: 1.9.0\n",
+      "     |         Casting from numeric to string types in 'safe' casting mode requires\n",
+      "     |         that the string dtype length is long enough to store the max\n",
+      "     |         integer/float value converted.\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ComplexWarning\n",
+      "     |          When casting from complex to float or int. To avoid this,\n",
+      "     |          one should use ``a.real.astype(t)``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 2.5])\n",
+      "     |      >>> x\n",
+      "     |      array([1. ,  2. ,  2.5])\n",
+      "     |      \n",
+      "     |      >>> x.astype(int)\n",
+      "     |      array([1, 2, 2])\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      a.byteswap(inplace=False)\n",
+      "     |      \n",
+      "     |      Swap the bytes of the array elements\n",
+      "     |      \n",
+      "     |      Toggle between low-endian and big-endian data representation by\n",
+      "     |      returning a byteswapped array, optionally swapped in-place.\n",
+      "     |      Arrays of byte-strings are not swapped. The real and imaginary\n",
+      "     |      parts of a complex number are swapped individually.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      inplace : bool, optional\n",
+      "     |          If ``True``, swap bytes in-place, default is ``False``.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          The byteswapped array. If `inplace` is ``True``, this is\n",
+      "     |          a view to self.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x1', '0x100', '0x2233']\n",
+      "     |      >>> A.byteswap(inplace=True)\n",
+      "     |      array([  256,     1, 13090], dtype=int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x100', '0x1', '0x3322']\n",
+      "     |      \n",
+      "     |      Arrays of byte-strings are not swapped\n",
+      "     |      \n",
+      "     |      >>> A = np.array([b'ceg', b'fac'])\n",
+      "     |      >>> A.byteswap()\n",
+      "     |      array([b'ceg', b'fac'], dtype='|S3')\n",
+      "     |      \n",
+      "     |      ``A.newbyteorder().byteswap()`` produces an array with the same values\n",
+      "     |        but different representation in memory\n",
+      "     |      \n",
+      "     |      >>> A = np.array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,\n",
+      "     |             0, 0], dtype=uint8)\n",
+      "     |      >>> A.newbyteorder().byteswap(inplace=True)\n",
+      "     |      array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,\n",
+      "     |             0, 3], dtype=uint8)\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      a.choose(choices, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Use an index array to construct a new array from a set of choices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.choose` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.choose : equivalent function\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      a.clip(min=None, max=None, out=None, **kwargs)\n",
+      "     |      \n",
+      "     |      Return an array whose values are limited to ``[min, max]``.\n",
+      "     |      One of max or min must be given.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.clip` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.clip : equivalent function\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      a.compress(condition, axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return selected slices of this array along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.compress` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.compress : equivalent function\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |      a.conj()\n",
+      "     |      \n",
+      "     |      Complex-conjugate all elements.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      a.conjugate()\n",
+      "     |      \n",
+      "     |      Return the complex conjugate, element-wise.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      a.copy(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout of the copy. 'C' means C-order,\n",
+      "     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n",
+      "     |          'C' otherwise. 'K' means match the layout of `a` as closely\n",
+      "     |          as possible. (Note that this function and :func:`numpy.copy` are very\n",
+      "     |          similar, but have different default values for their order=\n",
+      "     |          arguments.)\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      numpy.copy\n",
+      "     |      numpy.copyto\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n",
+      "     |      \n",
+      "     |      >>> y = x.copy()\n",
+      "     |      \n",
+      "     |      >>> x.fill(0)\n",
+      "     |      \n",
+      "     |      >>> x\n",
+      "     |      array([[0, 0, 0],\n",
+      "     |             [0, 0, 0]])\n",
+      "     |      \n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      \n",
+      "     |      >>> y.flags['C_CONTIGUOUS']\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      a.cumprod(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative product of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumprod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumprod : equivalent function\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      a.cumsum(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative sum of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumsum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumsum : equivalent function\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      a.diagonal(offset=0, axis1=0, axis2=1)\n",
+      "     |      \n",
+      "     |      Return specified diagonals. In NumPy 1.9 the returned array is a\n",
+      "     |      read-only view instead of a copy as in previous NumPy versions.  In\n",
+      "     |      a future version the read-only restriction will be removed.\n",
+      "     |      \n",
+      "     |      Refer to :func:`numpy.diagonal` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.diagonal : equivalent function\n",
+      "     |  \n",
+      "     |  dot(...)\n",
+      "     |      a.dot(b, out=None)\n",
+      "     |      \n",
+      "     |      Dot product of two arrays.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.dot` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dot : equivalent function\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.eye(2)\n",
+      "     |      >>> b = np.ones((2, 2)) * 2\n",
+      "     |      >>> a.dot(b)\n",
+      "     |      array([[2.,  2.],\n",
+      "     |             [2.,  2.]])\n",
+      "     |      \n",
+      "     |      This array method can be conveniently chained:\n",
+      "     |      \n",
+      "     |      >>> a.dot(b).dot(b)\n",
+      "     |      array([[8.,  8.],\n",
+      "     |             [8.,  8.]])\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      a.dump(file)\n",
+      "     |      \n",
+      "     |      Dump a pickle of the array to the specified file.\n",
+      "     |      The array can be read back with pickle.load or numpy.load.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      file : str or Path\n",
+      "     |          A string naming the dump file.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      a.dumps()\n",
+      "     |      \n",
+      "     |      Returns the pickle of the array as a string.\n",
+      "     |      pickle.loads or numpy.loads will convert the string back to an array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      a.fill(value)\n",
+      "     |      \n",
+      "     |      Fill the array with a scalar value.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      value : scalar\n",
+      "     |          All elements of `a` will be assigned this value.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([1, 2])\n",
+      "     |      >>> a.fill(0)\n",
+      "     |      >>> a\n",
+      "     |      array([0, 0])\n",
+      "     |      >>> a = np.empty(2)\n",
+      "     |      >>> a.fill(1)\n",
+      "     |      >>> a\n",
+      "     |      array([1.,  1.])\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      a.flatten(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          'C' means to flatten in row-major (C-style) order.\n",
+      "     |          'F' means to flatten in column-major (Fortran-\n",
+      "     |          style) order. 'A' means to flatten in column-major\n",
+      "     |          order if `a` is Fortran *contiguous* in memory,\n",
+      "     |          row-major order otherwise. 'K' means to flatten\n",
+      "     |          `a` in the order the elements occur in memory.\n",
+      "     |          The default is 'C'.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : ndarray\n",
+      "     |          A copy of the input array, flattened to one dimension.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ravel : Return a flattened array.\n",
+      "     |      flat : A 1-D flat iterator over the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,2], [3,4]])\n",
+      "     |      >>> a.flatten()\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |      >>> a.flatten('F')\n",
+      "     |      array([1, 3, 2, 4])\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      a.getfield(dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Returns a field of the given array as a certain type.\n",
+      "     |      \n",
+      "     |      A field is a view of the array data with a given data-type. The values in\n",
+      "     |      the view are determined by the given type and the offset into the current\n",
+      "     |      array in bytes. The offset needs to be such that the view dtype fits in the\n",
+      "     |      array dtype; for example an array of dtype complex128 has 16-byte elements.\n",
+      "     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n",
+      "     |      between 0 and 12 bytes.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          The data type of the view. The dtype size of the view can not be larger\n",
+      "     |          than that of the array itself.\n",
+      "     |      offset : int\n",
+      "     |          Number of bytes to skip before beginning the element view.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.diag([1.+1.j]*2)\n",
+      "     |      >>> x[1, 1] = 2 + 4.j\n",
+      "     |      >>> x\n",
+      "     |      array([[1.+1.j,  0.+0.j],\n",
+      "     |             [0.+0.j,  2.+4.j]])\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  2.]])\n",
+      "     |      \n",
+      "     |      By choosing an offset of 8 bytes we can select the complex part of the\n",
+      "     |      array for our view:\n",
+      "     |      \n",
+      "     |      >>> x.getfield(np.float64, offset=8)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  4.]])\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      a.item(*args)\n",
+      "     |      \n",
+      "     |      Copy an element of an array to a standard Python scalar and return it.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments (variable number and type)\n",
+      "     |      \n",
+      "     |          * none: in this case, the method only works for arrays\n",
+      "     |            with one element (`a.size == 1`), which element is\n",
+      "     |            copied into a standard Python scalar object and returned.\n",
+      "     |      \n",
+      "     |          * int_type: this argument is interpreted as a flat index into\n",
+      "     |            the array, specifying which element to copy and return.\n",
+      "     |      \n",
+      "     |          * tuple of int_types: functions as does a single int_type argument,\n",
+      "     |            except that the argument is interpreted as an nd-index into the\n",
+      "     |            array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      z : Standard Python scalar object\n",
+      "     |          A copy of the specified element of the array as a suitable\n",
+      "     |          Python scalar\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      When the data type of `a` is longdouble or clongdouble, item() returns\n",
+      "     |      a scalar array object because there is no available Python scalar that\n",
+      "     |      would not lose information. Void arrays return a buffer object for item(),\n",
+      "     |      unless fields are defined, in which case a tuple is returned.\n",
+      "     |      \n",
+      "     |      `item` is very similar to a[args], except, instead of an array scalar,\n",
+      "     |      a standard Python scalar is returned. This can be useful for speeding up\n",
+      "     |      access to elements of the array and doing arithmetic on elements of the\n",
+      "     |      array using Python's optimized math.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.item(3)\n",
+      "     |      1\n",
+      "     |      >>> x.item(7)\n",
+      "     |      0\n",
+      "     |      >>> x.item((0, 1))\n",
+      "     |      2\n",
+      "     |      >>> x.item((2, 2))\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      a.itemset(*args)\n",
+      "     |      \n",
+      "     |      Insert scalar into an array (scalar is cast to array's dtype, if possible)\n",
+      "     |      \n",
+      "     |      There must be at least 1 argument, and define the last argument\n",
+      "     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster\n",
+      "     |      than ``a[args] = item``.  The item should be a scalar value and `args`\n",
+      "     |      must select a single item in the array `a`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments\n",
+      "     |          If one argument: a scalar, only used in case `a` is of size 1.\n",
+      "     |          If two arguments: the last argument is the value to be set\n",
+      "     |          and must be a scalar, the first argument specifies a single array\n",
+      "     |          element location. It is either an int or a tuple.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Compared to indexing syntax, `itemset` provides some speed increase\n",
+      "     |      for placing a scalar into a particular location in an `ndarray`,\n",
+      "     |      if you must do this.  However, generally this is discouraged:\n",
+      "     |      among other problems, it complicates the appearance of the code.\n",
+      "     |      Also, when using `itemset` (and `item`) inside a loop, be sure\n",
+      "     |      to assign the methods to a local variable to avoid the attribute\n",
+      "     |      look-up at each loop iteration.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.itemset(4, 0)\n",
+      "     |      >>> x.itemset((2, 2), 9)\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 0, 6],\n",
+      "     |             [1, 0, 9]])\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      a.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the maximum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amax : equivalent function\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      a.mean(axis=None, dtype=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the average of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.mean` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.mean : equivalent function\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      a.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the minimum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amin` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amin : equivalent function\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      arr.newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return the array with the same data viewed with a different byte order.\n",
+      "     |      \n",
+      "     |      Equivalent to::\n",
+      "     |      \n",
+      "     |          arr.view(arr.dtype.newbytorder(new_order))\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the array data\n",
+      "     |      type.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : string, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          below. `new_order` codes can be any of:\n",
+      "     |      \n",
+      "     |          * 'S' - swap dtype from current to opposite endian\n",
+      "     |          * {'<', 'L'} - little endian\n",
+      "     |          * {'>', 'B'} - big endian\n",
+      "     |          * {'=', 'N'} - native order\n",
+      "     |          * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |          The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_arr : array\n",
+      "     |          New array object with the dtype reflecting given change to the\n",
+      "     |          byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      a.nonzero()\n",
+      "     |      \n",
+      "     |      Return the indices of the elements that are non-zero.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.nonzero` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.nonzero : equivalent function\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)\n",
+      "     |      \n",
+      "     |      Return the product of the array elements over the given axis\n",
+      "     |      \n",
+      "     |      Refer to `numpy.prod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.prod : equivalent function\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      a.ptp(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Peak to peak (maximum - minimum) value along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ptp` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ptp : equivalent function\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      a.put(indices, values, mode='raise')\n",
+      "     |      \n",
+      "     |      Set ``a.flat[n] = values[n]`` for all `n` in indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.put` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.put : equivalent function\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      a.ravel([order])\n",
+      "     |      \n",
+      "     |      Return a flattened array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ravel` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ravel : equivalent function\n",
+      "     |      \n",
+      "     |      ndarray.flat : a flat iterator on the array.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      a.repeat(repeats, axis=None)\n",
+      "     |      \n",
+      "     |      Repeat elements of an array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.repeat` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.repeat : equivalent function\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      a.reshape(shape, order='C')\n",
+      "     |      \n",
+      "     |      Returns an array containing the same data with a new shape.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.reshape` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : equivalent function\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Unlike the free function `numpy.reshape`, this method on `ndarray` allows\n",
+      "     |      the elements of the shape parameter to be passed in as separate arguments.\n",
+      "     |      For example, ``a.reshape(10, 11)`` is equivalent to\n",
+      "     |      ``a.reshape((10, 11))``.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      a.resize(new_shape, refcheck=True)\n",
+      "     |      \n",
+      "     |      Change shape and size of array in-place.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_shape : tuple of ints, or `n` ints\n",
+      "     |          Shape of resized array.\n",
+      "     |      refcheck : bool, optional\n",
+      "     |          If False, reference count will not be checked. Default is True.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ValueError\n",
+      "     |          If `a` does not own its own data or references or views to it exist,\n",
+      "     |          and the data memory must be changed.\n",
+      "     |          PyPy only: will always raise if the data memory must be changed, since\n",
+      "     |          there is no reliable way to determine if references or views to it\n",
+      "     |          exist.\n",
+      "     |      \n",
+      "     |      SystemError\n",
+      "     |          If the `order` keyword argument is specified. This behaviour is a\n",
+      "     |          bug in NumPy.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      resize : Return a new array with the specified shape.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This reallocates space for the data area if necessary.\n",
+      "     |      \n",
+      "     |      Only contiguous arrays (data elements consecutive in memory) can be\n",
+      "     |      resized.\n",
+      "     |      \n",
+      "     |      The purpose of the reference count check is to make sure you\n",
+      "     |      do not use this array as a buffer for another Python object and then\n",
+      "     |      reallocate the memory. However, reference counts can increase in\n",
+      "     |      other ways so if you are sure that you have not shared the memory\n",
+      "     |      for this array with another Python object, then you may safely set\n",
+      "     |      `refcheck` to False.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      Shrinking an array: array is flattened (in the order that the data are\n",
+      "     |      stored in memory), resized, and reshaped:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='C')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [1]])\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='F')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [2]])\n",
+      "     |      \n",
+      "     |      Enlarging an array: as above, but missing entries are filled with zeros:\n",
+      "     |      \n",
+      "     |      >>> b = np.array([[0, 1], [2, 3]])\n",
+      "     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n",
+      "     |      >>> b\n",
+      "     |      array([[0, 1, 2],\n",
+      "     |             [3, 0, 0]])\n",
+      "     |      \n",
+      "     |      Referencing an array prevents resizing...\n",
+      "     |      \n",
+      "     |      >>> c = a\n",
+      "     |      >>> a.resize((1, 1))\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |      ...\n",
+      "     |      ValueError: cannot resize an array that references or is referenced ...\n",
+      "     |      \n",
+      "     |      Unless `refcheck` is False:\n",
+      "     |      \n",
+      "     |      >>> a.resize((1, 1), refcheck=False)\n",
+      "     |      >>> a\n",
+      "     |      array([[0]])\n",
+      "     |      >>> c\n",
+      "     |      array([[0]])\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      a.round(decimals=0, out=None)\n",
+      "     |      \n",
+      "     |      Return `a` with each element rounded to the given number of decimals.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.around` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.around : equivalent function\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      a.searchsorted(v, side='left', sorter=None)\n",
+      "     |      \n",
+      "     |      Find indices where elements of v should be inserted in a to maintain order.\n",
+      "     |      \n",
+      "     |      For full documentation, see `numpy.searchsorted`\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.searchsorted : equivalent function\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      a.setfield(val, dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Put a value into a specified place in a field defined by a data-type.\n",
+      "     |      \n",
+      "     |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n",
+      "     |      bytes into the field.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      val : object\n",
+      "     |          Value to be placed in field.\n",
+      "     |      dtype : dtype object\n",
+      "     |          Data-type of the field in which to place `val`.\n",
+      "     |      offset : int, optional\n",
+      "     |          The number of bytes into the field at which to place `val`.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      getfield\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.eye(3)\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |      >>> x.setfield(3, np.int32)\n",
+      "     |      >>> x.getfield(np.int32)\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3],\n",
+      "     |             [3, 3, 3]], dtype=int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.0e+000, 1.5e-323, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.0e+000, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.5e-323, 1.0e+000]])\n",
+      "     |      >>> x.setfield(np.eye(3), np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      a.setflags(write=None, align=None, uic=None)\n",
+      "     |      \n",
+      "     |      Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),\n",
+      "     |      respectively.\n",
+      "     |      \n",
+      "     |      These Boolean-valued flags affect how numpy interprets the memory\n",
+      "     |      area used by `a` (see Notes below). The ALIGNED flag can only\n",
+      "     |      be set to True if the data is actually aligned according to the type.\n",
+      "     |      The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set\n",
+      "     |      to True. The flag WRITEABLE can only be set to True if the array owns its\n",
+      "     |      own memory, or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |      interface, or is a string. (The exception for string is made so that\n",
+      "     |      unpickling can be done without copying memory.)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      write : bool, optional\n",
+      "     |          Describes whether or not `a` can be written to.\n",
+      "     |      align : bool, optional\n",
+      "     |          Describes whether or not `a` is aligned properly for its type.\n",
+      "     |      uic : bool, optional\n",
+      "     |          Describes whether or not `a` is a copy of another \"base\" array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Array flags provide information about how the memory area used\n",
+      "     |      for the array is to be interpreted. There are 7 Boolean flags\n",
+      "     |      in use, only four of which can be changed by the user:\n",
+      "     |      WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n",
+      "     |      \n",
+      "     |      WRITEABLE (W) the data area can be written to;\n",
+      "     |      \n",
+      "     |      ALIGNED (A) the data and strides are aligned appropriately for the hardware\n",
+      "     |      (as determined by the compiler);\n",
+      "     |      \n",
+      "     |      UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;\n",
+      "     |      \n",
+      "     |      WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced\n",
+      "     |      by .base). When the C-API function PyArray_ResolveWritebackIfCopy is\n",
+      "     |      called, the base array will be updated with the contents of this array.\n",
+      "     |      \n",
+      "     |      All flags can be accessed using the single (upper case) letter as well\n",
+      "     |      as the full name.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.array([[3, 1, 7],\n",
+      "     |      ...               [2, 0, 0],\n",
+      "     |      ...               [8, 5, 9]])\n",
+      "     |      >>> y\n",
+      "     |      array([[3, 1, 7],\n",
+      "     |             [2, 0, 0],\n",
+      "     |             [8, 5, 9]])\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : True\n",
+      "     |        ALIGNED : True\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(write=0, align=0)\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : False\n",
+      "     |        ALIGNED : False\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(uic=1)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: cannot set WRITEBACKIFCOPY flag to True\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      a.sort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Sort an array in-place. Refer to `numpy.sort` for full documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n",
+      "     |          Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n",
+      "     |          and 'mergesort' use timsort under the covers and, in general, the\n",
+      "     |          actual implementation will vary with datatype. The 'mergesort' option\n",
+      "     |          is retained for backwards compatibility.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.15.0.\n",
+      "     |             The 'stable' option was added.\n",
+      "     |      \n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc.  A single field can\n",
+      "     |          be specified as a string, and not all fields need be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sort : Return a sorted copy of an array.\n",
+      "     |      numpy.argsort : Indirect sort.\n",
+      "     |      numpy.lexsort : Indirect stable sort on multiple keys.\n",
+      "     |      numpy.searchsorted : Find elements in sorted array.\n",
+      "     |      numpy.partition: Partial sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See `numpy.sort` for notes on the different sorting algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,4], [3,1]])\n",
+      "     |      >>> a.sort(axis=1)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 4],\n",
+      "     |             [1, 3]])\n",
+      "     |      >>> a.sort(axis=0)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 3],\n",
+      "     |             [1, 4]])\n",
+      "     |      \n",
+      "     |      Use the `order` keyword to specify a field to use when sorting a\n",
+      "     |      structured array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n",
+      "     |      >>> a.sort(order='y')\n",
+      "     |      >>> a\n",
+      "     |      array([(b'c', 1), (b'a', 2)],\n",
+      "     |            dtype=[('x', 'S1'), ('y', '<i8')])\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      a.squeeze(axis=None)\n",
+      "     |      \n",
+      "     |      Remove single-dimensional entries from the shape of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.squeeze` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.squeeze : equivalent function\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the standard deviation of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.std` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.std : equivalent function\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)\n",
+      "     |      \n",
+      "     |      Return the sum of the array elements over the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.sum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sum : equivalent function\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      a.swapaxes(axis1, axis2)\n",
+      "     |      \n",
+      "     |      Return a view of the array with `axis1` and `axis2` interchanged.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.swapaxes` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.swapaxes : equivalent function\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      a.take(indices, axis=None, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Return an array formed from the elements of `a` at the given indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.take` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.take : equivalent function\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |      a.tobytes(order='C')\n",
+      "     |      \n",
+      "     |      Construct Python bytes containing the raw data bytes in the array.\n",
+      "     |      \n",
+      "     |      Constructs Python bytes showing a copy of the raw contents of\n",
+      "     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',\n",
+      "     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n",
+      "     |      unless the F_CONTIGUOUS flag in the array is set, in which case it\n",
+      "     |      means 'Fortran' order.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.9.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', None}, optional\n",
+      "     |          Order of the data for multidimensional arrays:\n",
+      "     |          C, Fortran, or the same as for the original array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      s : bytes\n",
+      "     |          Python bytes exhibiting a copy of `a`'s raw data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype='<u2')\n",
+      "     |      >>> x.tobytes()\n",
+      "     |      b'\\x00\\x00\\x01\\x00\\x02\\x00\\x03\\x00'\n",
+      "     |      >>> x.tobytes('C') == x.tobytes()\n",
+      "     |      True\n",
+      "     |      >>> x.tobytes('F')\n",
+      "     |      b'\\x00\\x00\\x02\\x00\\x01\\x00\\x03\\x00'\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      a.tofile(fid, sep=\"\", format=\"%s\")\n",
+      "     |      \n",
+      "     |      Write array to a file as text or binary (default).\n",
+      "     |      \n",
+      "     |      Data is always written in 'C' order, independent of the order of `a`.\n",
+      "     |      The data produced by this method can be recovered using the function\n",
+      "     |      fromfile().\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      fid : file or str or Path\n",
+      "     |          An open file object, or a string containing a filename.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |      \n",
+      "     |      sep : str\n",
+      "     |          Separator between array items for text output.\n",
+      "     |          If \"\" (empty), a binary file is written, equivalent to\n",
+      "     |          ``file.write(a.tobytes())``.\n",
+      "     |      format : str\n",
+      "     |          Format string for text file output.\n",
+      "     |          Each entry in the array is formatted to text by first converting\n",
+      "     |          it to the closest Python type, and then using \"format\" % item.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is a convenience function for quick storage of array data.\n",
+      "     |      Information on endianness and precision is lost, so this method is not a\n",
+      "     |      good choice for files intended to archive data or transport data between\n",
+      "     |      machines with different endianness. Some of these problems can be overcome\n",
+      "     |      by outputting the data as text files, at the expense of speed and file\n",
+      "     |      size.\n",
+      "     |      \n",
+      "     |      When fid is a file object, array contents are directly written to the\n",
+      "     |      file, bypassing the file object's ``write`` method. As a result, tofile\n",
+      "     |      cannot be used with files objects supporting compression (e.g., GzipFile)\n",
+      "     |      or file-like objects that do not support ``fileno()`` (e.g., BytesIO).\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      a.tolist()\n",
+      "     |      \n",
+      "     |      Return the array as an ``a.ndim``-levels deep nested list of Python scalars.\n",
+      "     |      \n",
+      "     |      Return a copy of the array data as a (nested) Python list.\n",
+      "     |      Data items are converted to the nearest compatible builtin Python type, via\n",
+      "     |      the `~numpy.ndarray.item` function.\n",
+      "     |      \n",
+      "     |      If ``a.ndim`` is 0, then since the depth of the nested list is 0, it will\n",
+      "     |      not be a list at all, but a simple Python scalar.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      none\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : object, or list of object, or list of list of object, or ...\n",
+      "     |          The possibly nested list of array elements.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The array may be recreated via ``a = np.array(a.tolist())``, although this\n",
+      "     |      may sometimes lose precision.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      For a 1D array, ``a.tolist()`` is almost the same as ``list(a)``,\n",
+      "     |      except that ``tolist`` changes numpy scalars to Python scalars:\n",
+      "     |      \n",
+      "     |      >>> a = np.uint32([1, 2])\n",
+      "     |      >>> a_list = list(a)\n",
+      "     |      >>> a_list\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_list[0])\n",
+      "     |      <class 'numpy.uint32'>\n",
+      "     |      >>> a_tolist = a.tolist()\n",
+      "     |      >>> a_tolist\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_tolist[0])\n",
+      "     |      <class 'int'>\n",
+      "     |      \n",
+      "     |      Additionally, for a 2D array, ``tolist`` applies recursively:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> list(a)\n",
+      "     |      [array([1, 2]), array([3, 4])]\n",
+      "     |      >>> a.tolist()\n",
+      "     |      [[1, 2], [3, 4]]\n",
+      "     |      \n",
+      "     |      The base case for this recursion is a 0D array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array(1)\n",
+      "     |      >>> list(a)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        ...\n",
+      "     |      TypeError: iteration over a 0-d array\n",
+      "     |      >>> a.tolist()\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      a.tostring(order='C')\n",
+      "     |      \n",
+      "     |      A compatibility alias for `tobytes`, with exactly the same behavior.\n",
+      "     |      \n",
+      "     |      Despite its name, it returns `bytes` not `str`\\ s.\n",
+      "     |      \n",
+      "     |      .. deprecated:: 1.19.0\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the sum along diagonals of the array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.trace` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.trace : equivalent function\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      a.transpose(*axes)\n",
+      "     |      \n",
+      "     |      Returns a view of the array with axes transposed.\n",
+      "     |      \n",
+      "     |      For a 1-D array this has no effect, as a transposed vector is simply the\n",
+      "     |      same vector. To convert a 1-D array into a 2D column vector, an additional\n",
+      "     |      dimension must be added. `np.atleast2d(a).T` achieves this, as does\n",
+      "     |      `a[:, np.newaxis]`.\n",
+      "     |      For a 2-D array, this is a standard matrix transpose.\n",
+      "     |      For an n-D array, if axes are given, their order indicates how the\n",
+      "     |      axes are permuted (see Examples). If axes are not provided and\n",
+      "     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n",
+      "     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axes : None, tuple of ints, or `n` ints\n",
+      "     |      \n",
+      "     |       * None or no argument: reverses the order of the axes.\n",
+      "     |      \n",
+      "     |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n",
+      "     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n",
+      "     |      \n",
+      "     |       * `n` ints: same as an n-tuple of the same ints (this form is\n",
+      "     |         intended simply as a \"convenience\" alternative to the tuple form)\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          View of `a`, with axes suitably permuted.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ndarray.T : Array property returning the array transposed.\n",
+      "     |      ndarray.reshape : Give a new shape to an array without changing its data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]])\n",
+      "     |      >>> a.transpose()\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose((1, 0))\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose(1, 0)\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the variance of the array elements, along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.var` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.var : equivalent function\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      a.view([dtype][, type])\n",
+      "     |      \n",
+      "     |      New view of array with the same data.\n",
+      "     |      \n",
+      "     |      .. note::\n",
+      "     |          Passing None for ``dtype`` is different from omitting the parameter,\n",
+      "     |          since the former invokes ``dtype(None)`` which is an alias for\n",
+      "     |          ``dtype('float_')``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : data-type or ndarray sub-class, optional\n",
+      "     |          Data-type descriptor of the returned view, e.g., float32 or int16.\n",
+      "     |          Omitting it results in the view having the same data-type as `a`.\n",
+      "     |          This argument can also be specified as an ndarray sub-class, which\n",
+      "     |          then specifies the type of the returned object (this is equivalent to\n",
+      "     |          setting the ``type`` parameter).\n",
+      "     |      type : Python type, optional\n",
+      "     |          Type of the returned view, e.g., ndarray or matrix.  Again, omission\n",
+      "     |          of the parameter results in type preservation.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      ``a.view()`` is used two different ways:\n",
+      "     |      \n",
+      "     |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n",
+      "     |      of the array's memory with a different data-type.  This can cause a\n",
+      "     |      reinterpretation of the bytes of memory.\n",
+      "     |      \n",
+      "     |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n",
+      "     |      returns an instance of `ndarray_subclass` that looks at the same array\n",
+      "     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the\n",
+      "     |      memory.\n",
+      "     |      \n",
+      "     |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n",
+      "     |      bytes per entry than the previous dtype (for example, converting a\n",
+      "     |      regular array to a structured array), then the behavior of the view\n",
+      "     |      cannot be predicted just from the superficial appearance of ``a`` (shown\n",
+      "     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in\n",
+      "     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n",
+      "     |      defined as a slice or transpose, etc., the view may give different\n",
+      "     |      results.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      \n",
+      "     |      Viewing array data using a different type and dtype:\n",
+      "     |      \n",
+      "     |      >>> y = x.view(dtype=np.int16, type=np.matrix)\n",
+      "     |      >>> y\n",
+      "     |      matrix([[513]], dtype=int16)\n",
+      "     |      >>> print(type(y))\n",
+      "     |      <class 'numpy.matrix'>\n",
+      "     |      \n",
+      "     |      Creating a view on a structured array so it can be used in calculations\n",
+      "     |      \n",
+      "     |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n",
+      "     |      >>> xv\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]], dtype=int8)\n",
+      "     |      >>> xv.mean(0)\n",
+      "     |      array([2.,  3.])\n",
+      "     |      \n",
+      "     |      Making changes to the view changes the underlying array\n",
+      "     |      \n",
+      "     |      >>> xv[0,1] = 20\n",
+      "     |      >>> x\n",
+      "     |      array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])\n",
+      "     |      \n",
+      "     |      Using a view to convert an array to a recarray:\n",
+      "     |      \n",
+      "     |      >>> z = x.view(np.recarray)\n",
+      "     |      >>> z.a\n",
+      "     |      array([1, 3], dtype=int8)\n",
+      "     |      \n",
+      "     |      Views share data:\n",
+      "     |      \n",
+      "     |      >>> x[0] = (9, 10)\n",
+      "     |      >>> z[0]\n",
+      "     |      (9, 10)\n",
+      "     |      \n",
+      "     |      Views that change the dtype size (bytes per entry) should normally be\n",
+      "     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n",
+      "     |      >>> y = x[:, 0:2]\n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2],\n",
+      "     |             [4, 5]], dtype=int16)\n",
+      "     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |          ...\n",
+      "     |      ValueError: To change to a dtype of a different size, the array must be C-contiguous\n",
+      "     |      >>> z = y.copy()\n",
+      "     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      array([[(1, 2)],\n",
+      "     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from ndarray:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      The transposed array.\n",
+      "     |      \n",
+      "     |      Same as ``self.transpose()``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1.,2.],[3.,4.]])\n",
+      "     |      >>> x\n",
+      "     |      array([[ 1.,  2.],\n",
+      "     |             [ 3.,  4.]])\n",
+      "     |      >>> x.T\n",
+      "     |      array([[ 1.,  3.],\n",
+      "     |             [ 2.,  4.]])\n",
+      "     |      >>> x = np.array([1.,2.,3.,4.])\n",
+      "     |      >>> x\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      >>> x.T\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side.\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: C-struct side.\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      Base object if memory is from some other object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      The base of an array that owns its memory is None:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([1,2,3,4])\n",
+      "     |      >>> x.base is None\n",
+      "     |      True\n",
+      "     |      \n",
+      "     |      Slicing creates a view, whose memory is shared with x:\n",
+      "     |      \n",
+      "     |      >>> y = x[2:]\n",
+      "     |      >>> y.base is x\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  ctypes\n",
+      "     |      An object to simplify the interaction of the array with the ctypes\n",
+      "     |      module.\n",
+      "     |      \n",
+      "     |      This attribute creates an object that makes it easier to use arrays\n",
+      "     |      when calling shared libraries with the ctypes module. The returned\n",
+      "     |      object has, among others, data, shape, and strides attributes (see\n",
+      "     |      Notes below) which themselves return ctypes objects that can be used\n",
+      "     |      as arguments to a shared library.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      c : Python object\n",
+      "     |          Possessing attributes data, shape, strides, etc.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ctypeslib\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Below are the public attributes of this object which were documented\n",
+      "     |      in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n",
+      "     |      as well as documented private attributes):\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.data\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.shape\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.strides\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.data_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.shape_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.strides_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      If the ctypes module is not available, then the ctypes attribute\n",
+      "     |      of array objects still returns something useful, but ctypes objects\n",
+      "     |      are not returned and errors may be raised instead. In particular,\n",
+      "     |      the object will still have the ``as_parameter`` attribute which will\n",
+      "     |      return an integer equal to the data attribute.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> import ctypes\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]], dtype=int32)\n",
+      "     |      >>> x.ctypes.data\n",
+      "     |      31962608 # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))\n",
+      "     |      <__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents\n",
+      "     |      c_uint(0)\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents\n",
+      "     |      c_ulong(4294967296)\n",
+      "     |      >>> x.ctypes.shape\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary\n",
+      "     |      >>> x.ctypes.strides\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      Python buffer object pointing to the start of the array's data.\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      Data-type of the array's elements.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      d : numpy dtype object\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dtype\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]])\n",
+      "     |      >>> x.dtype\n",
+      "     |      dtype('int32')\n",
+      "     |      >>> type(x.dtype)\n",
+      "     |      <type 'numpy.dtype'>\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      Information about the memory layout of the array.\n",
+      "     |      \n",
+      "     |      Attributes\n",
+      "     |      ----------\n",
+      "     |      C_CONTIGUOUS (C)\n",
+      "     |          The data is in a single, C-style contiguous segment.\n",
+      "     |      F_CONTIGUOUS (F)\n",
+      "     |          The data is in a single, Fortran-style contiguous segment.\n",
+      "     |      OWNDATA (O)\n",
+      "     |          The array owns the memory it uses or borrows it from another object.\n",
+      "     |      WRITEABLE (W)\n",
+      "     |          The data area can be written to.  Setting this to False locks\n",
+      "     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE\n",
+      "     |          from its base array at creation time, but a view of a writeable\n",
+      "     |          array may be subsequently locked while the base array remains writeable.\n",
+      "     |          (The opposite is not true, in that a view of a locked array may not\n",
+      "     |          be made writeable.  However, currently, locking a base object does not\n",
+      "     |          lock any views that already reference it, so under that circumstance it\n",
+      "     |          is possible to alter the contents of a locked array via a previously\n",
+      "     |          created writeable view onto it.)  Attempting to change a non-writeable\n",
+      "     |          array raises a RuntimeError exception.\n",
+      "     |      ALIGNED (A)\n",
+      "     |          The data and all elements are aligned appropriately for the hardware.\n",
+      "     |      WRITEBACKIFCOPY (X)\n",
+      "     |          This array is a copy of some other array. The C-API function\n",
+      "     |          PyArray_ResolveWritebackIfCopy must be called before deallocating\n",
+      "     |          to the base array will be updated with the contents of this array.\n",
+      "     |      UPDATEIFCOPY (U)\n",
+      "     |          (Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array.\n",
+      "     |          When this array is\n",
+      "     |          deallocated, the base array will be updated with the contents of\n",
+      "     |          this array.\n",
+      "     |      FNC\n",
+      "     |          F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      FORC\n",
+      "     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n",
+      "     |      BEHAVED (B)\n",
+      "     |          ALIGNED and WRITEABLE.\n",
+      "     |      CARRAY (CA)\n",
+      "     |          BEHAVED and C_CONTIGUOUS.\n",
+      "     |      FARRAY (FA)\n",
+      "     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n",
+      "     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n",
+      "     |      names are only supported in dictionary access.\n",
+      "     |      \n",
+      "     |      Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be\n",
+      "     |      changed by the user, via direct assignment to the attribute or dictionary\n",
+      "     |      entry, or by calling `ndarray.setflags`.\n",
+      "     |      \n",
+      "     |      The array flags cannot be set arbitrarily:\n",
+      "     |      \n",
+      "     |      - UPDATEIFCOPY can only be set ``False``.\n",
+      "     |      - WRITEBACKIFCOPY can only be set ``False``.\n",
+      "     |      - ALIGNED can only be set ``True`` if the data is truly aligned.\n",
+      "     |      - WRITEABLE can only be set ``True`` if the array owns its own memory\n",
+      "     |        or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |        interface or is a string.\n",
+      "     |      \n",
+      "     |      Arrays can be both C-style and Fortran-style contiguous simultaneously.\n",
+      "     |      This is clear for 1-dimensional arrays, but can also be true for higher\n",
+      "     |      dimensional arrays.\n",
+      "     |      \n",
+      "     |      Even for contiguous arrays a stride for a given dimension\n",
+      "     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n",
+      "     |      or the array has no elements.\n",
+      "     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n",
+      "     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n",
+      "     |      Fortran-style contiguous arrays is true.\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      A 1-D iterator over the array.\n",
+      "     |      \n",
+      "     |      This is a `numpy.flatiter` instance, which acts similarly to, but is not\n",
+      "     |      a subclass of, Python's built-in iterator object.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      flatten : Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      flatiter\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(1, 7).reshape(2, 3)\n",
+      "     |      >>> x\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      >>> x.flat[3]\n",
+      "     |      4\n",
+      "     |      >>> x.T\n",
+      "     |      array([[1, 4],\n",
+      "     |             [2, 5],\n",
+      "     |             [3, 6]])\n",
+      "     |      >>> x.T.flat[3]\n",
+      "     |      5\n",
+      "     |      >>> type(x.flat)\n",
+      "     |      <class 'numpy.flatiter'>\n",
+      "     |      \n",
+      "     |      An assignment example:\n",
+      "     |      \n",
+      "     |      >>> x.flat = 3; x\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3]])\n",
+      "     |      >>> x.flat[[1,4]] = 1; x\n",
+      "     |      array([[3, 1, 3],\n",
+      "     |             [3, 1, 3]])\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      The imaginary part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.imag\n",
+      "     |      array([ 0.        ,  0.70710678])\n",
+      "     |      >>> x.imag.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      Length of one array element in bytes.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.float64)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      8\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.complex128)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      16\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      Total bytes consumed by the elements of the array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Does not include memory consumed by non-element attributes of the\n",
+      "     |      array object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)\n",
+      "     |      >>> x.nbytes\n",
+      "     |      480\n",
+      "     |      >>> np.prod(x.shape) * x.itemsize\n",
+      "     |      480\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      Number of array dimensions.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> x.ndim\n",
+      "     |      1\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.ndim\n",
+      "     |      3\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      The real part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.real\n",
+      "     |      array([ 1.        ,  0.70710678])\n",
+      "     |      >>> x.real.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.real : equivalent function\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      Tuple of array dimensions.\n",
+      "     |      \n",
+      "     |      The shape property is usually used to get the current shape of an array,\n",
+      "     |      but may also be used to reshape the array in-place by assigning a tuple of\n",
+      "     |      array dimensions to it.  As with `numpy.reshape`, one of the new shape\n",
+      "     |      dimensions can be -1, in which case its value is inferred from the size of\n",
+      "     |      the array and the remaining dimensions. Reshaping an array in-place will\n",
+      "     |      fail if a copy is required.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3, 4])\n",
+      "     |      >>> x.shape\n",
+      "     |      (4,)\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.shape\n",
+      "     |      (2, 3, 4)\n",
+      "     |      >>> y.shape = (3, 8)\n",
+      "     |      >>> y\n",
+      "     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
+      "     |      >>> y.shape = (3, 6)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: total size of new array must be unchanged\n",
+      "     |      >>> np.zeros((4,2))[::2].shape = (-1,)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      AttributeError: Incompatible shape for in-place modification. Use\n",
+      "     |      `.reshape()` to make a copy with the desired shape.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : similar function\n",
+      "     |      ndarray.reshape : similar method\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      Number of elements in the array.\n",
+      "     |      \n",
+      "     |      Equal to ``np.prod(a.shape)``, i.e., the product of the array's\n",
+      "     |      dimensions.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      `a.size` returns a standard arbitrary precision Python integer. This\n",
+      "     |      may not be the case with other methods of obtaining the same value\n",
+      "     |      (like the suggested ``np.prod(a.shape)``, which returns an instance\n",
+      "     |      of ``np.int_``), and may be relevant if the value is used further in\n",
+      "     |      calculations that may overflow a fixed size integer type.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n",
+      "     |      >>> x.size\n",
+      "     |      30\n",
+      "     |      >>> np.prod(x.shape)\n",
+      "     |      30\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      Tuple of bytes to step in each dimension when traversing an array.\n",
+      "     |      \n",
+      "     |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n",
+      "     |      is::\n",
+      "     |      \n",
+      "     |          offset = sum(np.array(i) * a.strides)\n",
+      "     |      \n",
+      "     |      A more detailed explanation of strides can be found in the\n",
+      "     |      \"ndarray.rst\" file in the NumPy reference guide.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Imagine an array of 32-bit integers (each 4 bytes)::\n",
+      "     |      \n",
+      "     |        x = np.array([[0, 1, 2, 3, 4],\n",
+      "     |                      [5, 6, 7, 8, 9]], dtype=np.int32)\n",
+      "     |      \n",
+      "     |      This array is stored in memory as 40 bytes, one after the other\n",
+      "     |      (known as a contiguous block of memory).  The strides of an array tell\n",
+      "     |      us how many bytes we have to skip in memory to move to the next position\n",
+      "     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to\n",
+      "     |      move to the next column, but 20 bytes (5 values) to get to the same\n",
+      "     |      position in the next row.  As such, the strides for the array `x` will be\n",
+      "     |      ``(20, 4)``.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.lib.stride_tricks.as_strided\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n",
+      "     |      >>> y\n",
+      "     |      array([[[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]],\n",
+      "     |             [[12, 13, 14, 15],\n",
+      "     |              [16, 17, 18, 19],\n",
+      "     |              [20, 21, 22, 23]]])\n",
+      "     |      >>> y.strides\n",
+      "     |      (48, 16, 4)\n",
+      "     |      >>> y[1,1,1]\n",
+      "     |      17\n",
+      "     |      >>> offset=sum(y.strides * np.array((1,1,1)))\n",
+      "     |      >>> offset/y.itemsize\n",
+      "     |      17\n",
+      "     |      \n",
+      "     |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n",
+      "     |      >>> x.strides\n",
+      "     |      (32, 4, 224, 1344)\n",
+      "     |      >>> i = np.array([3,5,2,2])\n",
+      "     |      >>> offset = sum(i * x.strides)\n",
+      "     |      >>> x[3,5,2,2]\n",
+      "     |      813\n",
+      "     |      >>> offset / x.itemsize\n",
+      "     |      813\n",
+      "    \n",
+      "    class clongdouble(complexfloating)\n",
+      "     |  Complex number type composed of two extended-precision floating-point\n",
+      "     |  numbers.\n",
+      "     |  Character code: ``'G'``.\n",
+      "     |  Alias: ``np.clongfloat``.\n",
+      "     |  Alias: ``np.longcomplex``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      clongdouble\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    clongfloat = class clongdouble(complexfloating)\n",
+      "     |  Complex number type composed of two extended-precision floating-point\n",
+      "     |  numbers.\n",
+      "     |  Character code: ``'G'``.\n",
+      "     |  Alias: ``np.clongfloat``.\n",
+      "     |  Alias: ``np.longcomplex``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      clongdouble\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class complex128(complexfloating, builtins.complex)\n",
+      "     |  complex128(real=0, imag=0)\n",
+      "     |  \n",
+      "     |  Complex number type composed of two double-precision floating-point\n",
+      "     |  numbers, compatible with Python `complex`.\n",
+      "     |  Character code: ``'D'``.\n",
+      "     |  Canonical name: ``np.cdouble``.\n",
+      "     |  Alias: ``np.cfloat``.\n",
+      "     |  Alias: ``np.complex_``.\n",
+      "     |  Alias *on this platform*: ``np.complex128``: Complex number type composed of 2 64-bit-precision floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complex128\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.complex\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.complex:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "    \n",
+      "    class complex64(complexfloating)\n",
+      "     |  Complex number type composed of two single-precision floating-point\n",
+      "     |  numbers.\n",
+      "     |  Character code: ``'F'``.\n",
+      "     |  Canonical name: ``np.csingle``.\n",
+      "     |  Alias: ``np.singlecomplex``.\n",
+      "     |  Alias *on this platform*: ``np.complex64``: Complex number type composed of 2 32-bit-precision floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complex64\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    complex_ = class complex128(complexfloating, builtins.complex)\n",
+      "     |  complex_(real=0, imag=0)\n",
+      "     |  \n",
+      "     |  Complex number type composed of two double-precision floating-point\n",
+      "     |  numbers, compatible with Python `complex`.\n",
+      "     |  Character code: ``'D'``.\n",
+      "     |  Canonical name: ``np.cdouble``.\n",
+      "     |  Alias: ``np.cfloat``.\n",
+      "     |  Alias: ``np.complex_``.\n",
+      "     |  Alias *on this platform*: ``np.complex128``: Complex number type composed of 2 64-bit-precision floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complex128\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.complex\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.complex:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "    \n",
+      "    class complexfloating(inexact)\n",
+      "     |  Abstract base class of all complex number scalar types that are made up of\n",
+      "     |  floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    csingle = class complex64(complexfloating)\n",
+      "     |  Complex number type composed of two single-precision floating-point\n",
+      "     |  numbers.\n",
+      "     |  Character code: ``'F'``.\n",
+      "     |  Canonical name: ``np.csingle``.\n",
+      "     |  Alias: ``np.singlecomplex``.\n",
+      "     |  Alias *on this platform*: ``np.complex64``: Complex number type composed of 2 32-bit-precision floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complex64\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class datetime64(generic)\n",
+      "     |  Base class for numpy scalar types.\n",
+      "     |  \n",
+      "     |  Class from which most (all?) numpy scalar types are derived.  For\n",
+      "     |  consistency, exposes the same API as `ndarray`, despite many\n",
+      "     |  consequent attributes being either \"get-only,\" or completely irrelevant.\n",
+      "     |  This is the class from which it is strongly suggested users should derive\n",
+      "     |  custom scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      datetime64\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    double = class float64(floating, builtins.float)\n",
+      "     |  double(x=0, /)\n",
+      "     |  \n",
+      "     |  Double-precision floating-point number type, compatible with Python `float`\n",
+      "     |  and C ``double``.\n",
+      "     |  Character code: ``'d'``.\n",
+      "     |  Canonical name: ``np.double``.\n",
+      "     |  Alias: ``np.float_``.\n",
+      "     |  Alias *on this platform*: ``np.float64``: 64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      float64\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.float\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      double.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.double(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.double(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.double(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.float:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getnewargs__(self, /)\n",
+      "     |  \n",
+      "     |  __trunc__(self, /)\n",
+      "     |      Return the Integral closest to x between 0 and x.\n",
+      "     |  \n",
+      "     |  hex(self, /)\n",
+      "     |      Return a hexadecimal representation of a floating-point number.\n",
+      "     |      \n",
+      "     |      >>> (-0.1).hex()\n",
+      "     |      '-0x1.999999999999ap-4'\n",
+      "     |      >>> 3.14159.hex()\n",
+      "     |      '0x1.921f9f01b866ep+1'\n",
+      "     |  \n",
+      "     |  is_integer(self, /)\n",
+      "     |      Return True if the float is an integer.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Class methods inherited from builtins.float:\n",
+      "     |  \n",
+      "     |  __getformat__(typestr, /) from builtins.type\n",
+      "     |      You probably don't want to use this function.\n",
+      "     |      \n",
+      "     |        typestr\n",
+      "     |          Must be 'double' or 'float'.\n",
+      "     |      \n",
+      "     |      It exists mainly to be used in Python's test suite.\n",
+      "     |      \n",
+      "     |      This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,\n",
+      "     |      little-endian' best describes the format of floating point numbers used by the\n",
+      "     |      C type named by typestr.\n",
+      "     |  \n",
+      "     |  __set_format__(typestr, fmt, /) from builtins.type\n",
+      "     |      You probably don't want to use this function.\n",
+      "     |      \n",
+      "     |        typestr\n",
+      "     |          Must be 'double' or 'float'.\n",
+      "     |        fmt\n",
+      "     |          Must be one of 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian',\n",
+      "     |          and in addition can only be one of the latter two if it appears to\n",
+      "     |          match the underlying C reality.\n",
+      "     |      \n",
+      "     |      It exists mainly to be used in Python's test suite.\n",
+      "     |      \n",
+      "     |      Override the automatic determination of C-level floating point type.\n",
+      "     |      This affects how floats are converted to and from binary strings.\n",
+      "     |  \n",
+      "     |  fromhex(string, /) from builtins.type\n",
+      "     |      Create a floating-point number from a hexadecimal string.\n",
+      "     |      \n",
+      "     |      >>> float.fromhex('0x1.ffffp10')\n",
+      "     |      2047.984375\n",
+      "     |      >>> float.fromhex('-0x1p-1074')\n",
+      "     |      -5e-324\n",
+      "    \n",
+      "    class dtype(builtins.object)\n",
+      "     |  dtype(obj, align=False, copy=False)\n",
+      "     |  \n",
+      "     |  Create a data type object.\n",
+      "     |  \n",
+      "     |  A numpy array is homogeneous, and contains elements described by a\n",
+      "     |  dtype object. A dtype object can be constructed from different\n",
+      "     |  combinations of fundamental numeric types.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  obj\n",
+      "     |      Object to be converted to a data type object.\n",
+      "     |  align : bool, optional\n",
+      "     |      Add padding to the fields to match what a C compiler would output\n",
+      "     |      for a similar C-struct. Can be ``True`` only if `obj` is a dictionary\n",
+      "     |      or a comma-separated string. If a struct dtype is being created,\n",
+      "     |      this also sets a sticky alignment flag ``isalignedstruct``.\n",
+      "     |  copy : bool, optional\n",
+      "     |      Make a new copy of the data-type object. If ``False``, the result\n",
+      "     |      may just be a reference to a built-in data-type object.\n",
+      "     |  \n",
+      "     |  See also\n",
+      "     |  --------\n",
+      "     |  result_type\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  Using array-scalar type:\n",
+      "     |  \n",
+      "     |  >>> np.dtype(np.int16)\n",
+      "     |  dtype('int16')\n",
+      "     |  \n",
+      "     |  Structured type, one field name 'f1', containing int16:\n",
+      "     |  \n",
+      "     |  >>> np.dtype([('f1', np.int16)])\n",
+      "     |  dtype([('f1', '<i2')])\n",
+      "     |  \n",
+      "     |  Structured type, one field named 'f1', in itself containing a structured\n",
+      "     |  type with one field:\n",
+      "     |  \n",
+      "     |  >>> np.dtype([('f1', [('f1', np.int16)])])\n",
+      "     |  dtype([('f1', [('f1', '<i2')])])\n",
+      "     |  \n",
+      "     |  Structured type, two fields: the first field contains an unsigned int, the\n",
+      "     |  second an int32:\n",
+      "     |  \n",
+      "     |  >>> np.dtype([('f1', np.uint64), ('f2', np.int32)])\n",
+      "     |  dtype([('f1', '<u8'), ('f2', '<i4')])\n",
+      "     |  \n",
+      "     |  Using array-protocol type strings:\n",
+      "     |  \n",
+      "     |  >>> np.dtype([('a','f8'),('b','S10')])\n",
+      "     |  dtype([('a', '<f8'), ('b', 'S10')])\n",
+      "     |  \n",
+      "     |  Using comma-separated field formats.  The shape is (2,3):\n",
+      "     |  \n",
+      "     |  >>> np.dtype(\"i4, (2,3)f8\")\n",
+      "     |  dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])\n",
+      "     |  \n",
+      "     |  Using tuples.  ``int`` is a fixed type, 3 the field's shape.  ``void``\n",
+      "     |  is a flexible type, here of size 10:\n",
+      "     |  \n",
+      "     |  >>> np.dtype([('hello',(np.int64,3)),('world',np.void,10)])\n",
+      "     |  dtype([('hello', '<i8', (3,)), ('world', 'V10')])\n",
+      "     |  \n",
+      "     |  Subdivide ``int16`` into 2 ``int8``'s, called x and y.  0 and 1 are\n",
+      "     |  the offsets in bytes:\n",
+      "     |  \n",
+      "     |  >>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))\n",
+      "     |  dtype((numpy.int16, [('x', 'i1'), ('y', 'i1')]))\n",
+      "     |  \n",
+      "     |  Using dictionaries.  Two fields named 'gender' and 'age':\n",
+      "     |  \n",
+      "     |  >>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})\n",
+      "     |  dtype([('gender', 'S1'), ('age', 'u1')])\n",
+      "     |  \n",
+      "     |  Offsets in bytes, here 0 and 25:\n",
+      "     |  \n",
+      "     |  >>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})\n",
+      "     |  dtype([('surname', 'S25'), ('age', 'u1')])\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new dtype with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : string, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          below.  The default value ('S') results in swapping the current\n",
+      "     |          byte order.  `new_order` codes can be any of:\n",
+      "     |      \n",
+      "     |          * 'S' - swap dtype from current to opposite endian\n",
+      "     |          * {'<', 'L'} - little endian\n",
+      "     |          * {'>', 'B'} - big endian\n",
+      "     |          * {'=', 'N'} - native order\n",
+      "     |          * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |          The code does a case-insensitive check on the first letter of\n",
+      "     |          `new_order` for these alternatives.  For example, any of '>'\n",
+      "     |          or 'B' or 'b' or 'brian' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New dtype object with the given change to the byte order.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> import sys\n",
+      "     |      >>> sys_is_le = sys.byteorder == 'little'\n",
+      "     |      >>> native_code = sys_is_le and '<' or '>'\n",
+      "     |      >>> swapped_code = sys_is_le and '>' or '<'\n",
+      "     |      >>> native_dt = np.dtype(native_code+'i2')\n",
+      "     |      >>> swapped_dt = np.dtype(swapped_code+'i2')\n",
+      "     |      >>> native_dt.newbyteorder('S') == swapped_dt\n",
+      "     |      True\n",
+      "     |      >>> native_dt.newbyteorder() == swapped_dt\n",
+      "     |      True\n",
+      "     |      >>> native_dt == swapped_dt.newbyteorder('S')\n",
+      "     |      True\n",
+      "     |      >>> native_dt == swapped_dt.newbyteorder('=')\n",
+      "     |      True\n",
+      "     |      >>> native_dt == swapped_dt.newbyteorder('N')\n",
+      "     |      True\n",
+      "     |      >>> native_dt == native_dt.newbyteorder('|')\n",
+      "     |      True\n",
+      "     |      >>> np.dtype('<i2') == native_dt.newbyteorder('<')\n",
+      "     |      True\n",
+      "     |      >>> np.dtype('<i2') == native_dt.newbyteorder('L')\n",
+      "     |      True\n",
+      "     |      >>> np.dtype('>i2') == native_dt.newbyteorder('>')\n",
+      "     |      True\n",
+      "     |      >>> np.dtype('>i2') == native_dt.newbyteorder('B')\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  alignment\n",
+      "     |      The required alignment (bytes) of this data-type according to the compiler.\n",
+      "     |      \n",
+      "     |      More information is available in the C-API section of the manual.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype('i4')\n",
+      "     |      >>> x.alignment\n",
+      "     |      4\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype(float)\n",
+      "     |      >>> x.alignment\n",
+      "     |      8\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      Returns dtype for the base element of the subarrays,\n",
+      "     |      regardless of their dimension or shape.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      dtype.subdtype\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = numpy.dtype('8f')\n",
+      "     |      >>> x.base\n",
+      "     |      dtype('float32')\n",
+      "     |      \n",
+      "     |      >>> x =  numpy.dtype('i2')\n",
+      "     |      >>> x.base\n",
+      "     |      dtype('int16')\n",
+      "     |  \n",
+      "     |  byteorder\n",
+      "     |      A character indicating the byte-order of this data-type object.\n",
+      "     |      \n",
+      "     |      One of:\n",
+      "     |      \n",
+      "     |      ===  ==============\n",
+      "     |      '='  native\n",
+      "     |      '<'  little-endian\n",
+      "     |      '>'  big-endian\n",
+      "     |      '|'  not applicable\n",
+      "     |      ===  ==============\n",
+      "     |      \n",
+      "     |      All built-in data-type objects have byteorder either '=' or '|'.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype('i2')\n",
+      "     |      >>> dt.byteorder\n",
+      "     |      '='\n",
+      "     |      >>> # endian is not relevant for 8 bit numbers\n",
+      "     |      >>> np.dtype('i1').byteorder\n",
+      "     |      '|'\n",
+      "     |      >>> # or ASCII strings\n",
+      "     |      >>> np.dtype('S2').byteorder\n",
+      "     |      '|'\n",
+      "     |      >>> # Even if specific code is given, and it is native\n",
+      "     |      >>> # '=' is the byteorder\n",
+      "     |      >>> import sys\n",
+      "     |      >>> sys_is_le = sys.byteorder == 'little'\n",
+      "     |      >>> native_code = sys_is_le and '<' or '>'\n",
+      "     |      >>> swapped_code = sys_is_le and '>' or '<'\n",
+      "     |      >>> dt = np.dtype(native_code + 'i2')\n",
+      "     |      >>> dt.byteorder\n",
+      "     |      '='\n",
+      "     |      >>> # Swapped code shows up as itself\n",
+      "     |      >>> dt = np.dtype(swapped_code + 'i2')\n",
+      "     |      >>> dt.byteorder == swapped_code\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  char\n",
+      "     |      A unique character code for each of the 21 different built-in types.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype(float)\n",
+      "     |      >>> x.char\n",
+      "     |      'd'\n",
+      "     |  \n",
+      "     |  descr\n",
+      "     |      `__array_interface__` description of the data-type.\n",
+      "     |      \n",
+      "     |      The format is that required by the 'descr' key in the\n",
+      "     |      `__array_interface__` attribute.\n",
+      "     |      \n",
+      "     |      Warning: This attribute exists specifically for `__array_interface__`,\n",
+      "     |      and passing it directly to `np.dtype` will not accurately reconstruct\n",
+      "     |      some dtypes (e.g., scalar and subarray dtypes).\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype(float)\n",
+      "     |      >>> x.descr\n",
+      "     |      [('', '<f8')]\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n",
+      "     |      >>> dt.descr\n",
+      "     |      [('name', '<U16'), ('grades', '<f8', (2,))]\n",
+      "     |  \n",
+      "     |  fields\n",
+      "     |      Dictionary of named fields defined for this data type, or ``None``.\n",
+      "     |      \n",
+      "     |      The dictionary is indexed by keys that are the names of the fields.\n",
+      "     |      Each entry in the dictionary is a tuple fully describing the field::\n",
+      "     |      \n",
+      "     |        (dtype, offset[, title])\n",
+      "     |      \n",
+      "     |      Offset is limited to C int, which is signed and usually 32 bits.\n",
+      "     |      If present, the optional title can be any object (if it is a string\n",
+      "     |      or unicode then it will also be a key in the fields dictionary,\n",
+      "     |      otherwise it's meta-data). Notice also that the first two elements\n",
+      "     |      of the tuple can be passed directly as arguments to the ``ndarray.getfield``\n",
+      "     |      and ``ndarray.setfield`` methods.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ndarray.getfield, ndarray.setfield\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n",
+      "     |      >>> print(dt.fields)\n",
+      "     |      {'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      Bit-flags describing how this data type is to be interpreted.\n",
+      "     |      \n",
+      "     |      Bit-masks are in `numpy.core.multiarray` as the constants\n",
+      "     |      `ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,\n",
+      "     |      `NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation\n",
+      "     |      of these flags is in C-API documentation; they are largely useful\n",
+      "     |      for user-defined data-types.\n",
+      "     |      \n",
+      "     |      The following example demonstrates that operations on this particular\n",
+      "     |      dtype requires Python C-API.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)])\n",
+      "     |      >>> x.flags\n",
+      "     |      16\n",
+      "     |      >>> np.core.multiarray.NEEDS_PYAPI\n",
+      "     |      16\n",
+      "     |  \n",
+      "     |  hasobject\n",
+      "     |      Boolean indicating whether this dtype contains any reference-counted\n",
+      "     |      objects in any fields or sub-dtypes.\n",
+      "     |      \n",
+      "     |      Recall that what is actually in the ndarray memory representing\n",
+      "     |      the Python object is the memory address of that object (a pointer).\n",
+      "     |      Special handling may be required, and this attribute is useful for\n",
+      "     |      distinguishing data types that may contain arbitrary Python objects\n",
+      "     |      and data-types that won't.\n",
+      "     |  \n",
+      "     |  isalignedstruct\n",
+      "     |      Boolean indicating whether the dtype is a struct which maintains\n",
+      "     |      field alignment. This flag is sticky, so when combining multiple\n",
+      "     |      structs together, it is preserved and produces new dtypes which\n",
+      "     |      are also aligned.\n",
+      "     |  \n",
+      "     |  isbuiltin\n",
+      "     |      Integer indicating how this dtype relates to the built-in dtypes.\n",
+      "     |      \n",
+      "     |      Read-only.\n",
+      "     |      \n",
+      "     |      =  ========================================================================\n",
+      "     |      0  if this is a structured array type, with fields\n",
+      "     |      1  if this is a dtype compiled into numpy (such as ints, floats etc)\n",
+      "     |      2  if the dtype is for a user-defined numpy type\n",
+      "     |         A user-defined type uses the numpy C-API machinery to extend\n",
+      "     |         numpy to handle a new array type. See\n",
+      "     |         :ref:`user.user-defined-data-types` in the NumPy manual.\n",
+      "     |      =  ========================================================================\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> dt = np.dtype('i2')\n",
+      "     |      >>> dt.isbuiltin\n",
+      "     |      1\n",
+      "     |      >>> dt = np.dtype('f8')\n",
+      "     |      >>> dt.isbuiltin\n",
+      "     |      1\n",
+      "     |      >>> dt = np.dtype([('field1', 'f8')])\n",
+      "     |      >>> dt.isbuiltin\n",
+      "     |      0\n",
+      "     |  \n",
+      "     |  isnative\n",
+      "     |      Boolean indicating whether the byte order of this dtype is native\n",
+      "     |      to the platform.\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      The element size of this data-type object.\n",
+      "     |      \n",
+      "     |      For 18 of the 21 types this number is fixed by the data-type.\n",
+      "     |      For the flexible data-types, this number can be anything.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> arr = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> arr.dtype\n",
+      "     |      dtype('int64')\n",
+      "     |      >>> arr.itemsize\n",
+      "     |      8\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n",
+      "     |      >>> dt.itemsize\n",
+      "     |      80\n",
+      "     |  \n",
+      "     |  kind\n",
+      "     |      A character code (one of 'biufcmMOSUV') identifying the general kind of data.\n",
+      "     |      \n",
+      "     |      =  ======================\n",
+      "     |      b  boolean\n",
+      "     |      i  signed integer\n",
+      "     |      u  unsigned integer\n",
+      "     |      f  floating-point\n",
+      "     |      c  complex floating-point\n",
+      "     |      m  timedelta\n",
+      "     |      M  datetime\n",
+      "     |      O  object\n",
+      "     |      S  (byte-)string\n",
+      "     |      U  Unicode\n",
+      "     |      V  void\n",
+      "     |      =  ======================\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype('i4')\n",
+      "     |      >>> dt.kind\n",
+      "     |      'i'\n",
+      "     |      >>> dt = np.dtype('f8')\n",
+      "     |      >>> dt.kind\n",
+      "     |      'f'\n",
+      "     |      >>> dt = np.dtype([('field1', 'f8')])\n",
+      "     |      >>> dt.kind\n",
+      "     |      'V'\n",
+      "     |  \n",
+      "     |  metadata\n",
+      "     |  \n",
+      "     |  name\n",
+      "     |      A bit-width name for this data-type.\n",
+      "     |      \n",
+      "     |      Un-sized flexible data-type objects do not have this attribute.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype(float)\n",
+      "     |      >>> x.name\n",
+      "     |      'float64'\n",
+      "     |      >>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)])\n",
+      "     |      >>> x.name\n",
+      "     |      'void640'\n",
+      "     |  \n",
+      "     |  names\n",
+      "     |      Ordered list of field names, or ``None`` if there are no fields.\n",
+      "     |      \n",
+      "     |      The names are ordered according to increasing byte offset. This can be\n",
+      "     |      used, for example, to walk through all of the named fields in offset order.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n",
+      "     |      >>> dt.names\n",
+      "     |      ('name', 'grades')\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      Number of dimensions of the sub-array if this data type describes a\n",
+      "     |      sub-array, and ``0`` otherwise.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.13.0\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.dtype(float)\n",
+      "     |      >>> x.ndim\n",
+      "     |      0\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype((float, 8))\n",
+      "     |      >>> x.ndim\n",
+      "     |      1\n",
+      "     |      \n",
+      "     |      >>> x = np.dtype(('i4', (3, 4)))\n",
+      "     |      >>> x.ndim\n",
+      "     |      2\n",
+      "     |  \n",
+      "     |  num\n",
+      "     |      A unique number for each of the 21 different built-in types.\n",
+      "     |      \n",
+      "     |      These are roughly ordered from least-to-most precision.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype(str)\n",
+      "     |      >>> dt.num\n",
+      "     |      19\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype(float)\n",
+      "     |      >>> dt.num\n",
+      "     |      12\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      Shape tuple of the sub-array if this data type describes a sub-array,\n",
+      "     |      and ``()`` otherwise.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype(('i4', 4))\n",
+      "     |      >>> dt.shape\n",
+      "     |      (4,)\n",
+      "     |      \n",
+      "     |      >>> dt = np.dtype(('i4', (2, 3)))\n",
+      "     |      >>> dt.shape\n",
+      "     |      (2, 3)\n",
+      "     |  \n",
+      "     |  str\n",
+      "     |      The array-protocol typestring of this data-type object.\n",
+      "     |  \n",
+      "     |  subdtype\n",
+      "     |      Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and\n",
+      "     |      None otherwise.\n",
+      "     |      \n",
+      "     |      The *shape* is the fixed shape of the sub-array described by this\n",
+      "     |      data type, and *item_dtype* the data type of the array.\n",
+      "     |      \n",
+      "     |      If a field whose dtype object has this attribute is retrieved,\n",
+      "     |      then the extra dimensions implied by *shape* are tacked on to\n",
+      "     |      the end of the retrieved array.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      dtype.base\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = numpy.dtype('8f')\n",
+      "     |      >>> x.subdtype\n",
+      "     |      (dtype('float32'), (8,))\n",
+      "     |      \n",
+      "     |      >>> x =  numpy.dtype('i2')\n",
+      "     |      >>> x.subdtype\n",
+      "     |      >>>\n",
+      "     |  \n",
+      "     |  type\n",
+      "     |      The type object used to instantiate a scalar of this data-type.\n",
+      "    \n",
+      "    class errstate(contextlib.ContextDecorator)\n",
+      "     |  errstate(*, call=<numpy.core._ufunc_config._unspecified object at 0x0000018C0DF6EC08>, **kwargs)\n",
+      "     |  \n",
+      "     |  errstate(**kwargs)\n",
+      "     |  \n",
+      "     |  Context manager for floating-point error handling.\n",
+      "     |  \n",
+      "     |  Using an instance of `errstate` as a context manager allows statements in\n",
+      "     |  that context to execute with a known error handling behavior. Upon entering\n",
+      "     |  the context the error handling is set with `seterr` and `seterrcall`, and\n",
+      "     |  upon exiting it is reset to what it was before.\n",
+      "     |  \n",
+      "     |  ..  versionchanged:: 1.17.0\n",
+      "     |      `errstate` is also usable as a function decorator, saving\n",
+      "     |      a level of indentation if an entire function is wrapped.\n",
+      "     |      See :py:class:`contextlib.ContextDecorator` for more information.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  kwargs : {divide, over, under, invalid}\n",
+      "     |      Keyword arguments. The valid keywords are the possible floating-point\n",
+      "     |      exceptions. Each keyword should have a string value that defines the\n",
+      "     |      treatment for the particular error. Possible values are\n",
+      "     |      {'ignore', 'warn', 'raise', 'call', 'print', 'log'}.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  seterr, geterr, seterrcall, geterrcall\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  For complete documentation of the types of floating-point exceptions and\n",
+      "     |  treatment options, see `seterr`.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> from collections import OrderedDict\n",
+      "     |  >>> olderr = np.seterr(all='ignore')  # Set error handling to known state.\n",
+      "     |  \n",
+      "     |  >>> np.arange(3) / 0.\n",
+      "     |  array([nan, inf, inf])\n",
+      "     |  >>> with np.errstate(divide='warn'):\n",
+      "     |  ...     np.arange(3) / 0.\n",
+      "     |  array([nan, inf, inf])\n",
+      "     |  \n",
+      "     |  >>> np.sqrt(-1)\n",
+      "     |  nan\n",
+      "     |  >>> with np.errstate(invalid='raise'):\n",
+      "     |  ...     np.sqrt(-1)\n",
+      "     |  Traceback (most recent call last):\n",
+      "     |    File \"<stdin>\", line 2, in <module>\n",
+      "     |  FloatingPointError: invalid value encountered in sqrt\n",
+      "     |  \n",
+      "     |  Outside the context the error handling behavior has not changed:\n",
+      "     |  \n",
+      "     |  >>> OrderedDict(sorted(np.geterr().items()))\n",
+      "     |  OrderedDict([('divide', 'ignore'), ('invalid', 'ignore'), ('over', 'ignore'), ('under', 'ignore')])\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      errstate\n",
+      "     |      contextlib.ContextDecorator\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __enter__(self)\n",
+      "     |  \n",
+      "     |  __exit__(self, *exc_info)\n",
+      "     |  \n",
+      "     |  __init__(self, *, call=<numpy.core._ufunc_config._unspecified object at 0x0000018C0DF6EC08>, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from contextlib.ContextDecorator:\n",
+      "     |  \n",
+      "     |  __call__(self, func)\n",
+      "     |      Call self as a function.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from contextlib.ContextDecorator:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class finfo(builtins.object)\n",
+      "     |  finfo(dtype)\n",
+      "     |  \n",
+      "     |  finfo(dtype)\n",
+      "     |  \n",
+      "     |  Machine limits for floating point types.\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  bits : int\n",
+      "     |      The number of bits occupied by the type.\n",
+      "     |  eps : float\n",
+      "     |      The difference between 1.0 and the next smallest representable float\n",
+      "     |      larger than 1.0. For example, for 64-bit binary floats in the IEEE-754\n",
+      "     |      standard, ``eps = 2**-52``, approximately 2.22e-16.\n",
+      "     |  epsneg : float\n",
+      "     |      The difference between 1.0 and the next smallest representable float\n",
+      "     |      less than 1.0. For example, for 64-bit binary floats in the IEEE-754\n",
+      "     |      standard, ``epsneg = 2**-53``, approximately 1.11e-16.\n",
+      "     |  iexp : int\n",
+      "     |      The number of bits in the exponent portion of the floating point\n",
+      "     |      representation.\n",
+      "     |  machar : MachAr\n",
+      "     |      The object which calculated these parameters and holds more\n",
+      "     |      detailed information.\n",
+      "     |  machep : int\n",
+      "     |      The exponent that yields `eps`.\n",
+      "     |  max : floating point number of the appropriate type\n",
+      "     |      The largest representable number.\n",
+      "     |  maxexp : int\n",
+      "     |      The smallest positive power of the base (2) that causes overflow.\n",
+      "     |  min : floating point number of the appropriate type\n",
+      "     |      The smallest representable number, typically ``-max``.\n",
+      "     |  minexp : int\n",
+      "     |      The most negative power of the base (2) consistent with there\n",
+      "     |      being no leading 0's in the mantissa.\n",
+      "     |  negep : int\n",
+      "     |      The exponent that yields `epsneg`.\n",
+      "     |  nexp : int\n",
+      "     |      The number of bits in the exponent including its sign and bias.\n",
+      "     |  nmant : int\n",
+      "     |      The number of bits in the mantissa.\n",
+      "     |  precision : int\n",
+      "     |      The approximate number of decimal digits to which this kind of\n",
+      "     |      float is precise.\n",
+      "     |  resolution : floating point number of the appropriate type\n",
+      "     |      The approximate decimal resolution of this type, i.e.,\n",
+      "     |      ``10**-precision``.\n",
+      "     |  tiny : float\n",
+      "     |      The smallest positive usable number.  Type of `tiny` is an\n",
+      "     |      appropriate floating point type.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  dtype : float, dtype, or instance\n",
+      "     |      Kind of floating point data-type about which to get information.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  MachAr : The implementation of the tests that produce this information.\n",
+      "     |  iinfo : The equivalent for integer data types.\n",
+      "     |  spacing : The distance between a value and the nearest adjacent number\n",
+      "     |  nextafter : The next floating point value after x1 towards x2\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  For developers of NumPy: do not instantiate this at the module level.\n",
+      "     |  The initial calculation of these parameters is expensive and negatively\n",
+      "     |  impacts import times.  These objects are cached, so calling ``finfo()``\n",
+      "     |  repeatedly inside your functions is not a problem.\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __repr__(self)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(cls, dtype)\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class flatiter(builtins.object)\n",
+      "     |  Flat iterator object to iterate over arrays.\n",
+      "     |  \n",
+      "     |  A `flatiter` iterator is returned by ``x.flat`` for any array `x`.\n",
+      "     |  It allows iterating over the array as if it were a 1-D array,\n",
+      "     |  either in a for-loop or by calling its `next` method.\n",
+      "     |  \n",
+      "     |  Iteration is done in row-major, C-style order (the last\n",
+      "     |  index varying the fastest). The iterator can also be indexed using\n",
+      "     |  basic slicing or advanced indexing.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  ndarray.flat : Return a flat iterator over an array.\n",
+      "     |  ndarray.flatten : Returns a flattened copy of an array.\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  A `flatiter` iterator can not be constructed directly from Python code\n",
+      "     |  by calling the `flatiter` constructor.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> x = np.arange(6).reshape(2, 3)\n",
+      "     |  >>> fl = x.flat\n",
+      "     |  >>> type(fl)\n",
+      "     |  <class 'numpy.flatiter'>\n",
+      "     |  >>> for item in fl:\n",
+      "     |  ...     print(item)\n",
+      "     |  ...\n",
+      "     |  0\n",
+      "     |  1\n",
+      "     |  2\n",
+      "     |  3\n",
+      "     |  4\n",
+      "     |  5\n",
+      "     |  \n",
+      "     |  >>> fl[2:4]\n",
+      "     |  array([2, 3])\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      __array__(type=None) Get array from iterator\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __next__(self, /)\n",
+      "     |      Implement next(self).\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      copy()\n",
+      "     |      \n",
+      "     |      Get a copy of the iterator as a 1-D array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(6).reshape(2, 3)\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1, 2],\n",
+      "     |             [3, 4, 5]])\n",
+      "     |      >>> fl = x.flat\n",
+      "     |      >>> fl.copy()\n",
+      "     |      array([0, 1, 2, 3, 4, 5])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      A reference to the array that is iterated over.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(5)\n",
+      "     |      >>> fl = x.flat\n",
+      "     |      >>> fl.base is x\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  coords\n",
+      "     |      An N-dimensional tuple of current coordinates.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(6).reshape(2, 3)\n",
+      "     |      >>> fl = x.flat\n",
+      "     |      >>> fl.coords\n",
+      "     |      (0, 0)\n",
+      "     |      >>> next(fl)\n",
+      "     |      0\n",
+      "     |      >>> fl.coords\n",
+      "     |      (0, 1)\n",
+      "     |  \n",
+      "     |  index\n",
+      "     |      Current flat index into the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(6).reshape(2, 3)\n",
+      "     |      >>> fl = x.flat\n",
+      "     |      >>> fl.index\n",
+      "     |      0\n",
+      "     |      >>> next(fl)\n",
+      "     |      0\n",
+      "     |      >>> fl.index\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class flexible(generic)\n",
+      "     |  Abstract base class of all scalar types without predefined length.\n",
+      "     |  The actual size of these types depends on the specific `np.dtype`\n",
+      "     |  instantiation.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class float16(floating)\n",
+      "     |  Half-precision floating-point number type.\n",
+      "     |  Character code: ``'e'``.\n",
+      "     |  Canonical name: ``np.half``.\n",
+      "     |  Alias *on this platform*: ``np.float16``: 16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      float16\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      half.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.half(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.half(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.half(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class float32(floating)\n",
+      "     |  Single-precision floating-point number type, compatible with C ``float``.\n",
+      "     |  Character code: ``'f'``.\n",
+      "     |  Canonical name: ``np.single``.\n",
+      "     |  Alias *on this platform*: ``np.float32``: 32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      float32\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      single.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.single(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.single(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.single(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class float64(floating, builtins.float)\n",
+      "     |  float64(x=0, /)\n",
+      "     |  \n",
+      "     |  Double-precision floating-point number type, compatible with Python `float`\n",
+      "     |  and C ``double``.\n",
+      "     |  Character code: ``'d'``.\n",
+      "     |  Canonical name: ``np.double``.\n",
+      "     |  Alias: ``np.float_``.\n",
+      "     |  Alias *on this platform*: ``np.float64``: 64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      float64\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.float\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      double.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.double(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.double(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.double(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.float:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getnewargs__(self, /)\n",
+      "     |  \n",
+      "     |  __trunc__(self, /)\n",
+      "     |      Return the Integral closest to x between 0 and x.\n",
+      "     |  \n",
+      "     |  hex(self, /)\n",
+      "     |      Return a hexadecimal representation of a floating-point number.\n",
+      "     |      \n",
+      "     |      >>> (-0.1).hex()\n",
+      "     |      '-0x1.999999999999ap-4'\n",
+      "     |      >>> 3.14159.hex()\n",
+      "     |      '0x1.921f9f01b866ep+1'\n",
+      "     |  \n",
+      "     |  is_integer(self, /)\n",
+      "     |      Return True if the float is an integer.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Class methods inherited from builtins.float:\n",
+      "     |  \n",
+      "     |  __getformat__(typestr, /) from builtins.type\n",
+      "     |      You probably don't want to use this function.\n",
+      "     |      \n",
+      "     |        typestr\n",
+      "     |          Must be 'double' or 'float'.\n",
+      "     |      \n",
+      "     |      It exists mainly to be used in Python's test suite.\n",
+      "     |      \n",
+      "     |      This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,\n",
+      "     |      little-endian' best describes the format of floating point numbers used by the\n",
+      "     |      C type named by typestr.\n",
+      "     |  \n",
+      "     |  __set_format__(typestr, fmt, /) from builtins.type\n",
+      "     |      You probably don't want to use this function.\n",
+      "     |      \n",
+      "     |        typestr\n",
+      "     |          Must be 'double' or 'float'.\n",
+      "     |        fmt\n",
+      "     |          Must be one of 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian',\n",
+      "     |          and in addition can only be one of the latter two if it appears to\n",
+      "     |          match the underlying C reality.\n",
+      "     |      \n",
+      "     |      It exists mainly to be used in Python's test suite.\n",
+      "     |      \n",
+      "     |      Override the automatic determination of C-level floating point type.\n",
+      "     |      This affects how floats are converted to and from binary strings.\n",
+      "     |  \n",
+      "     |  fromhex(string, /) from builtins.type\n",
+      "     |      Create a floating-point number from a hexadecimal string.\n",
+      "     |      \n",
+      "     |      >>> float.fromhex('0x1.ffffp10')\n",
+      "     |      2047.984375\n",
+      "     |      >>> float.fromhex('-0x1p-1074')\n",
+      "     |      -5e-324\n",
+      "    \n",
+      "    float_ = class float64(floating, builtins.float)\n",
+      "     |  float_(x=0, /)\n",
+      "     |  \n",
+      "     |  Double-precision floating-point number type, compatible with Python `float`\n",
+      "     |  and C ``double``.\n",
+      "     |  Character code: ``'d'``.\n",
+      "     |  Canonical name: ``np.double``.\n",
+      "     |  Alias: ``np.float_``.\n",
+      "     |  Alias *on this platform*: ``np.float64``: 64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      float64\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.float\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      double.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.double(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.double(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.double(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.float:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getnewargs__(self, /)\n",
+      "     |  \n",
+      "     |  __trunc__(self, /)\n",
+      "     |      Return the Integral closest to x between 0 and x.\n",
+      "     |  \n",
+      "     |  hex(self, /)\n",
+      "     |      Return a hexadecimal representation of a floating-point number.\n",
+      "     |      \n",
+      "     |      >>> (-0.1).hex()\n",
+      "     |      '-0x1.999999999999ap-4'\n",
+      "     |      >>> 3.14159.hex()\n",
+      "     |      '0x1.921f9f01b866ep+1'\n",
+      "     |  \n",
+      "     |  is_integer(self, /)\n",
+      "     |      Return True if the float is an integer.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Class methods inherited from builtins.float:\n",
+      "     |  \n",
+      "     |  __getformat__(typestr, /) from builtins.type\n",
+      "     |      You probably don't want to use this function.\n",
+      "     |      \n",
+      "     |        typestr\n",
+      "     |          Must be 'double' or 'float'.\n",
+      "     |      \n",
+      "     |      It exists mainly to be used in Python's test suite.\n",
+      "     |      \n",
+      "     |      This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,\n",
+      "     |      little-endian' best describes the format of floating point numbers used by the\n",
+      "     |      C type named by typestr.\n",
+      "     |  \n",
+      "     |  __set_format__(typestr, fmt, /) from builtins.type\n",
+      "     |      You probably don't want to use this function.\n",
+      "     |      \n",
+      "     |        typestr\n",
+      "     |          Must be 'double' or 'float'.\n",
+      "     |        fmt\n",
+      "     |          Must be one of 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian',\n",
+      "     |          and in addition can only be one of the latter two if it appears to\n",
+      "     |          match the underlying C reality.\n",
+      "     |      \n",
+      "     |      It exists mainly to be used in Python's test suite.\n",
+      "     |      \n",
+      "     |      Override the automatic determination of C-level floating point type.\n",
+      "     |      This affects how floats are converted to and from binary strings.\n",
+      "     |  \n",
+      "     |  fromhex(string, /) from builtins.type\n",
+      "     |      Create a floating-point number from a hexadecimal string.\n",
+      "     |      \n",
+      "     |      >>> float.fromhex('0x1.ffffp10')\n",
+      "     |      2047.984375\n",
+      "     |      >>> float.fromhex('-0x1p-1074')\n",
+      "     |      -5e-324\n",
+      "    \n",
+      "    class floating(inexact)\n",
+      "     |  Abstract base class of all floating-point scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class format_parser(builtins.object)\n",
+      "     |  format_parser(formats, names, titles, aligned=False, byteorder=None)\n",
+      "     |  \n",
+      "     |  Class to convert formats, names, titles description to a dtype.\n",
+      "     |  \n",
+      "     |  After constructing the format_parser object, the dtype attribute is\n",
+      "     |  the converted data-type:\n",
+      "     |  ``dtype = format_parser(formats, names, titles).dtype``\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  dtype : dtype\n",
+      "     |      The converted data-type.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  formats : str or list of str\n",
+      "     |      The format description, either specified as a string with\n",
+      "     |      comma-separated format descriptions in the form ``'f8, i4, a5'``, or\n",
+      "     |      a list of format description strings  in the form\n",
+      "     |      ``['f8', 'i4', 'a5']``.\n",
+      "     |  names : str or list/tuple of str\n",
+      "     |      The field names, either specified as a comma-separated string in the\n",
+      "     |      form ``'col1, col2, col3'``, or as a list or tuple of strings in the\n",
+      "     |      form ``['col1', 'col2', 'col3']``.\n",
+      "     |      An empty list can be used, in that case default field names\n",
+      "     |      ('f0', 'f1', ...) are used.\n",
+      "     |  titles : sequence\n",
+      "     |      Sequence of title strings. An empty list can be used to leave titles\n",
+      "     |      out.\n",
+      "     |  aligned : bool, optional\n",
+      "     |      If True, align the fields by padding as the C-compiler would.\n",
+      "     |      Default is False.\n",
+      "     |  byteorder : str, optional\n",
+      "     |      If specified, all the fields will be changed to the\n",
+      "     |      provided byte-order.  Otherwise, the default byte-order is\n",
+      "     |      used. For all available string specifiers, see `dtype.newbyteorder`.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  dtype, typename, sctype2char\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> np.format_parser(['<f8', '<i4', '<a5'], ['col1', 'col2', 'col3'],\n",
+      "     |  ...                  ['T1', 'T2', 'T3']).dtype\n",
+      "     |  dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'), (('T3', 'col3'), 'S5')])\n",
+      "     |  \n",
+      "     |  `names` and/or `titles` can be empty lists. If `titles` is an empty list,\n",
+      "     |  titles will simply not appear. If `names` is empty, default field names\n",
+      "     |  will be used.\n",
+      "     |  \n",
+      "     |  >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],\n",
+      "     |  ...                  []).dtype\n",
+      "     |  dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '<S5')])\n",
+      "     |  >>> np.format_parser(['<f8', '<i4', '<a5'], [], []).dtype\n",
+      "     |  dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', 'S5')])\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __init__(self, formats, names, titles, aligned=False, byteorder=None)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class generic(builtins.object)\n",
+      "     |  Base class for numpy scalar types.\n",
+      "     |  \n",
+      "     |  Class from which most (all?) numpy scalar types are derived.  For\n",
+      "     |  consistency, exposes the same API as `ndarray`, despite many\n",
+      "     |  consequent attributes being either \"get-only,\" or completely irrelevant.\n",
+      "     |  This is the class from which it is strongly suggested users should derive\n",
+      "     |  custom scalar types.\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    half = class float16(floating)\n",
+      "     |  Half-precision floating-point number type.\n",
+      "     |  Character code: ``'e'``.\n",
+      "     |  Canonical name: ``np.half``.\n",
+      "     |  Alias *on this platform*: ``np.float16``: 16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      float16\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      half.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.half(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.half(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.half(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class iinfo(builtins.object)\n",
+      "     |  iinfo(int_type)\n",
+      "     |  \n",
+      "     |  iinfo(type)\n",
+      "     |  \n",
+      "     |  Machine limits for integer types.\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  bits : int\n",
+      "     |      The number of bits occupied by the type.\n",
+      "     |  min : int\n",
+      "     |      The smallest integer expressible by the type.\n",
+      "     |  max : int\n",
+      "     |      The largest integer expressible by the type.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  int_type : integer type, dtype, or instance\n",
+      "     |      The kind of integer data type to get information about.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  finfo : The equivalent for floating point data types.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  With types:\n",
+      "     |  \n",
+      "     |  >>> ii16 = np.iinfo(np.int16)\n",
+      "     |  >>> ii16.min\n",
+      "     |  -32768\n",
+      "     |  >>> ii16.max\n",
+      "     |  32767\n",
+      "     |  >>> ii32 = np.iinfo(np.int32)\n",
+      "     |  >>> ii32.min\n",
+      "     |  -2147483648\n",
+      "     |  >>> ii32.max\n",
+      "     |  2147483647\n",
+      "     |  \n",
+      "     |  With instances:\n",
+      "     |  \n",
+      "     |  >>> ii32 = np.iinfo(np.int32(10))\n",
+      "     |  >>> ii32.min\n",
+      "     |  -2147483648\n",
+      "     |  >>> ii32.max\n",
+      "     |  2147483647\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __init__(self, int_type)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  __repr__(self)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self)\n",
+      "     |      String representation.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  max\n",
+      "     |      Maximum value of given dtype.\n",
+      "     |  \n",
+      "     |  min\n",
+      "     |      Minimum value of given dtype.\n",
+      "    \n",
+      "    class inexact(number)\n",
+      "     |  Abstract base class of all numeric scalar types with a (potentially)\n",
+      "     |  inexact representation of the values in its range, such as\n",
+      "     |  floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    int0 = class int64(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``long long``.\n",
+      "     |  Character code: ``'q'``.\n",
+      "     |  Canonical name: ``np.longlong``.\n",
+      "     |  Alias *on this platform*: ``np.int64``: 64-bit signed integer (-9223372036854775808 to 9223372036854775807).\n",
+      "     |  Alias *on this platform*: ``np.intp``: Signed integer large enough to fit pointer, compatible with C ``intptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int64\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class int16(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``short``.\n",
+      "     |  Character code: ``'h'``.\n",
+      "     |  Canonical name: ``np.short``.\n",
+      "     |  Alias *on this platform*: ``np.int16``: 16-bit signed integer (-32768 to 32767).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int16\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class int32(signedinteger)\n",
+      "     |  Signed integer type, compatible with Python `int` anc C ``long``.\n",
+      "     |  Character code: ``'l'``.\n",
+      "     |  Canonical name: ``np.int_``.\n",
+      "     |  Alias *on this platform*: ``np.int32``: 32-bit signed integer (-2147483648 to 2147483647).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int32\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class int64(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``long long``.\n",
+      "     |  Character code: ``'q'``.\n",
+      "     |  Canonical name: ``np.longlong``.\n",
+      "     |  Alias *on this platform*: ``np.int64``: 64-bit signed integer (-9223372036854775808 to 9223372036854775807).\n",
+      "     |  Alias *on this platform*: ``np.intp``: Signed integer large enough to fit pointer, compatible with C ``intptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int64\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class int8(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``char``.\n",
+      "     |  Character code: ``'b'``.\n",
+      "     |  Canonical name: ``np.byte``.\n",
+      "     |  Alias *on this platform*: ``np.int8``: 8-bit signed integer (-128 to 127).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int8\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    int_ = class int32(signedinteger)\n",
+      "     |  Signed integer type, compatible with Python `int` anc C ``long``.\n",
+      "     |  Character code: ``'l'``.\n",
+      "     |  Canonical name: ``np.int_``.\n",
+      "     |  Alias *on this platform*: ``np.int32``: 32-bit signed integer (-2147483648 to 2147483647).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int32\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class intc(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``int``.\n",
+      "     |  Character code: ``'i'``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      intc\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class integer(number)\n",
+      "     |  Abstract base class of all integer scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    intp = class int64(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``long long``.\n",
+      "     |  Character code: ``'q'``.\n",
+      "     |  Canonical name: ``np.longlong``.\n",
+      "     |  Alias *on this platform*: ``np.int64``: 64-bit signed integer (-9223372036854775808 to 9223372036854775807).\n",
+      "     |  Alias *on this platform*: ``np.intp``: Signed integer large enough to fit pointer, compatible with C ``intptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int64\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    longcomplex = class clongdouble(complexfloating)\n",
+      "     |  Complex number type composed of two extended-precision floating-point\n",
+      "     |  numbers.\n",
+      "     |  Character code: ``'G'``.\n",
+      "     |  Alias: ``np.clongfloat``.\n",
+      "     |  Alias: ``np.longcomplex``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      clongdouble\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class longdouble(floating)\n",
+      "     |  Extended-precision floating-point number type, compatible with C\n",
+      "     |  ``long double`` but not necessarily with IEEE 754 quadruple-precision.\n",
+      "     |  Character code: ``'g'``.\n",
+      "     |  Alias: ``np.longfloat``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      longdouble\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      longdouble.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.longdouble(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.longdouble(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.longdouble(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    longfloat = class longdouble(floating)\n",
+      "     |  Extended-precision floating-point number type, compatible with C\n",
+      "     |  ``long double`` but not necessarily with IEEE 754 quadruple-precision.\n",
+      "     |  Character code: ``'g'``.\n",
+      "     |  Alias: ``np.longfloat``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      longdouble\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      longdouble.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.longdouble(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.longdouble(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.longdouble(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    longlong = class int64(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``long long``.\n",
+      "     |  Character code: ``'q'``.\n",
+      "     |  Canonical name: ``np.longlong``.\n",
+      "     |  Alias *on this platform*: ``np.int64``: 64-bit signed integer (-9223372036854775808 to 9223372036854775807).\n",
+      "     |  Alias *on this platform*: ``np.intp``: Signed integer large enough to fit pointer, compatible with C ``intptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int64\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class matrix(ndarray)\n",
+      "     |  matrix(data, dtype=None, copy=True)\n",
+      "     |  \n",
+      "     |  matrix(data, dtype=None, copy=True)\n",
+      "     |  \n",
+      "     |  .. note:: It is no longer recommended to use this class, even for linear\n",
+      "     |            algebra. Instead use regular arrays. The class may be removed\n",
+      "     |            in the future.\n",
+      "     |  \n",
+      "     |  Returns a matrix from an array-like object, or from a string of data.\n",
+      "     |  A matrix is a specialized 2-D array that retains its 2-D nature\n",
+      "     |  through operations.  It has certain special operators, such as ``*``\n",
+      "     |  (matrix multiplication) and ``**`` (matrix power).\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  data : array_like or string\n",
+      "     |     If `data` is a string, it is interpreted as a matrix with commas\n",
+      "     |     or spaces separating columns, and semicolons separating rows.\n",
+      "     |  dtype : data-type\n",
+      "     |     Data-type of the output matrix.\n",
+      "     |  copy : bool\n",
+      "     |     If `data` is already an `ndarray`, then this flag determines\n",
+      "     |     whether the data is copied (the default), or whether a view is\n",
+      "     |     constructed.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  array\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> a = np.matrix('1 2; 3 4')\n",
+      "     |  >>> a\n",
+      "     |  matrix([[1, 2],\n",
+      "     |          [3, 4]])\n",
+      "     |  \n",
+      "     |  >>> np.matrix([[1, 2], [3, 4]])\n",
+      "     |  matrix([[1, 2],\n",
+      "     |          [3, 4]])\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      matrix\n",
+      "     |      ndarray\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __array_finalize__(self, obj)\n",
+      "     |      None.\n",
+      "     |  \n",
+      "     |  __getitem__(self, index)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __imul__(self, other)\n",
+      "     |      Return self*=value.\n",
+      "     |  \n",
+      "     |  __ipow__(self, other)\n",
+      "     |      Return self**=value.\n",
+      "     |  \n",
+      "     |  __mul__(self, other)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __pow__(self, other)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __rmul__(self, other)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, other)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  all(self, axis=None, out=None)\n",
+      "     |      Test whether all matrix elements along a given axis evaluate to True.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      See `numpy.all` for complete descriptions\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.all\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.all`, but it returns a `matrix` object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> y = x[0]; y\n",
+      "     |      matrix([[0, 1, 2, 3]])\n",
+      "     |      >>> (x == y)\n",
+      "     |      matrix([[ True,  True,  True,  True],\n",
+      "     |              [False, False, False, False],\n",
+      "     |              [False, False, False, False]])\n",
+      "     |      >>> (x == y).all()\n",
+      "     |      False\n",
+      "     |      >>> (x == y).all(0)\n",
+      "     |      matrix([[False, False, False, False]])\n",
+      "     |      >>> (x == y).all(1)\n",
+      "     |      matrix([[ True],\n",
+      "     |              [False],\n",
+      "     |              [False]])\n",
+      "     |  \n",
+      "     |  any(self, axis=None, out=None)\n",
+      "     |      Test whether any array element along a given axis evaluates to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.any` for full documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which logical OR is performed\n",
+      "     |      out : ndarray, optional\n",
+      "     |          Output to existing array instead of creating new one, must have\n",
+      "     |          same shape as expected output\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |          any : bool, ndarray\n",
+      "     |              Returns a single bool if `axis` is ``None``; otherwise,\n",
+      "     |              returns `ndarray`\n",
+      "     |  \n",
+      "     |  argmax(self, axis=None, out=None)\n",
+      "     |      Indexes of the maximum values along an axis.\n",
+      "     |      \n",
+      "     |      Return the indexes of the first occurrences of the maximum values\n",
+      "     |      along the specified axis.  If axis is None, the index is for the\n",
+      "     |      flattened matrix.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      See `numpy.argmax` for complete descriptions\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmax\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.argmax`, but returns a `matrix` object\n",
+      "     |      where `ndarray.argmax` would return an `ndarray`.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.argmax()\n",
+      "     |      11\n",
+      "     |      >>> x.argmax(0)\n",
+      "     |      matrix([[2, 2, 2, 2]])\n",
+      "     |      >>> x.argmax(1)\n",
+      "     |      matrix([[3],\n",
+      "     |              [3],\n",
+      "     |              [3]])\n",
+      "     |  \n",
+      "     |  argmin(self, axis=None, out=None)\n",
+      "     |      Indexes of the minimum values along an axis.\n",
+      "     |      \n",
+      "     |      Return the indexes of the first occurrences of the minimum values\n",
+      "     |      along the specified axis.  If axis is None, the index is for the\n",
+      "     |      flattened matrix.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      See `numpy.argmin` for complete descriptions.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmin\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.argmin`, but returns a `matrix` object\n",
+      "     |      where `ndarray.argmin` would return an `ndarray`.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = -np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[  0,  -1,  -2,  -3],\n",
+      "     |              [ -4,  -5,  -6,  -7],\n",
+      "     |              [ -8,  -9, -10, -11]])\n",
+      "     |      >>> x.argmin()\n",
+      "     |      11\n",
+      "     |      >>> x.argmin(0)\n",
+      "     |      matrix([[2, 2, 2, 2]])\n",
+      "     |      >>> x.argmin(1)\n",
+      "     |      matrix([[3],\n",
+      "     |              [3],\n",
+      "     |              [3]])\n",
+      "     |  \n",
+      "     |  flatten(self, order='C')\n",
+      "     |      Return a flattened copy of the matrix.\n",
+      "     |      \n",
+      "     |      All `N` elements of the matrix are placed into a single row.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          'C' means to flatten in row-major (C-style) order. 'F' means to\n",
+      "     |          flatten in column-major (Fortran-style) order. 'A' means to\n",
+      "     |          flatten in column-major order if `m` is Fortran *contiguous* in\n",
+      "     |          memory, row-major order otherwise. 'K' means to flatten `m` in\n",
+      "     |          the order the elements occur in memory. The default is 'C'.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : matrix\n",
+      "     |          A copy of the matrix, flattened to a `(1, N)` matrix where `N`\n",
+      "     |          is the number of elements in the original matrix.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ravel : Return a flattened array.\n",
+      "     |      flat : A 1-D flat iterator over the matrix.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> m = np.matrix([[1,2], [3,4]])\n",
+      "     |      >>> m.flatten()\n",
+      "     |      matrix([[1, 2, 3, 4]])\n",
+      "     |      >>> m.flatten('F')\n",
+      "     |      matrix([[1, 3, 2, 4]])\n",
+      "     |  \n",
+      "     |  getA = A(self)\n",
+      "     |      Return `self` as an `ndarray` object.\n",
+      "     |      \n",
+      "     |      Equivalent to ``np.asarray(self)``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : ndarray\n",
+      "     |          `self` as an `ndarray`\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.getA()\n",
+      "     |      array([[ 0,  1,  2,  3],\n",
+      "     |             [ 4,  5,  6,  7],\n",
+      "     |             [ 8,  9, 10, 11]])\n",
+      "     |  \n",
+      "     |  getA1 = A1(self)\n",
+      "     |      Return `self` as a flattened `ndarray`.\n",
+      "     |      \n",
+      "     |      Equivalent to ``np.asarray(x).ravel()``\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : ndarray\n",
+      "     |          `self`, 1-D, as an `ndarray`\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.getA1()\n",
+      "     |      array([ 0,  1,  2, ...,  9, 10, 11])\n",
+      "     |  \n",
+      "     |  getH = H(self)\n",
+      "     |      Returns the (complex) conjugate transpose of `self`.\n",
+      "     |      \n",
+      "     |      Equivalent to ``np.transpose(self)`` if `self` is real-valued.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : matrix object\n",
+      "     |          complex conjugate transpose of `self`\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4)))\n",
+      "     |      >>> z = x - 1j*x; z\n",
+      "     |      matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],\n",
+      "     |              [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],\n",
+      "     |              [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])\n",
+      "     |      >>> z.getH()\n",
+      "     |      matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],\n",
+      "     |              [ 1. +1.j,  5. +5.j,  9. +9.j],\n",
+      "     |              [ 2. +2.j,  6. +6.j, 10.+10.j],\n",
+      "     |              [ 3. +3.j,  7. +7.j, 11.+11.j]])\n",
+      "     |  \n",
+      "     |  getI = I(self)\n",
+      "     |      Returns the (multiplicative) inverse of invertible `self`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : matrix object\n",
+      "     |          If `self` is non-singular, `ret` is such that ``ret * self`` ==\n",
+      "     |          ``self * ret`` == ``np.matrix(np.eye(self[0,:].size))`` all return\n",
+      "     |          ``True``.\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      numpy.linalg.LinAlgError: Singular matrix\n",
+      "     |          If `self` is singular.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      linalg.inv\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> m = np.matrix('[1, 2; 3, 4]'); m\n",
+      "     |      matrix([[1, 2],\n",
+      "     |              [3, 4]])\n",
+      "     |      >>> m.getI()\n",
+      "     |      matrix([[-2. ,  1. ],\n",
+      "     |              [ 1.5, -0.5]])\n",
+      "     |      >>> m.getI() * m\n",
+      "     |      matrix([[ 1.,  0.], # may vary\n",
+      "     |              [ 0.,  1.]])\n",
+      "     |  \n",
+      "     |  getT = T(self)\n",
+      "     |      Returns the transpose of the matrix.\n",
+      "     |      \n",
+      "     |      Does *not* conjugate!  For the complex conjugate transpose, use ``.H``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : matrix object\n",
+      "     |          The (non-conjugated) transpose of the matrix.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      transpose, getH\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> m = np.matrix('[1, 2; 3, 4]')\n",
+      "     |      >>> m\n",
+      "     |      matrix([[1, 2],\n",
+      "     |              [3, 4]])\n",
+      "     |      >>> m.getT()\n",
+      "     |      matrix([[1, 3],\n",
+      "     |              [2, 4]])\n",
+      "     |  \n",
+      "     |  max(self, axis=None, out=None)\n",
+      "     |      Return the maximum value along an axis.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      See `amax` for complete descriptions\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      amax, ndarray.max\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.max`, but returns a `matrix` object\n",
+      "     |      where `ndarray.max` would return an ndarray.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.max()\n",
+      "     |      11\n",
+      "     |      >>> x.max(0)\n",
+      "     |      matrix([[ 8,  9, 10, 11]])\n",
+      "     |      >>> x.max(1)\n",
+      "     |      matrix([[ 3],\n",
+      "     |              [ 7],\n",
+      "     |              [11]])\n",
+      "     |  \n",
+      "     |  mean(self, axis=None, dtype=None, out=None)\n",
+      "     |      Returns the average of the matrix elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.mean` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.mean\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Same as `ndarray.mean` except that, where that returns an `ndarray`,\n",
+      "     |      this returns a `matrix` object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3, 4)))\n",
+      "     |      >>> x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.mean()\n",
+      "     |      5.5\n",
+      "     |      >>> x.mean(0)\n",
+      "     |      matrix([[4., 5., 6., 7.]])\n",
+      "     |      >>> x.mean(1)\n",
+      "     |      matrix([[ 1.5],\n",
+      "     |              [ 5.5],\n",
+      "     |              [ 9.5]])\n",
+      "     |  \n",
+      "     |  min(self, axis=None, out=None)\n",
+      "     |      Return the minimum value along an axis.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      See `amin` for complete descriptions.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      amin, ndarray.min\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.min`, but returns a `matrix` object\n",
+      "     |      where `ndarray.min` would return an ndarray.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = -np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[  0,  -1,  -2,  -3],\n",
+      "     |              [ -4,  -5,  -6,  -7],\n",
+      "     |              [ -8,  -9, -10, -11]])\n",
+      "     |      >>> x.min()\n",
+      "     |      -11\n",
+      "     |      >>> x.min(0)\n",
+      "     |      matrix([[ -8,  -9, -10, -11]])\n",
+      "     |      >>> x.min(1)\n",
+      "     |      matrix([[ -3],\n",
+      "     |              [ -7],\n",
+      "     |              [-11]])\n",
+      "     |  \n",
+      "     |  prod(self, axis=None, dtype=None, out=None)\n",
+      "     |      Return the product of the array elements over the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `prod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      prod, ndarray.prod\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Same as `ndarray.prod`, except, where that returns an `ndarray`, this\n",
+      "     |      returns a `matrix` object instead.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.prod()\n",
+      "     |      0\n",
+      "     |      >>> x.prod(0)\n",
+      "     |      matrix([[  0,  45, 120, 231]])\n",
+      "     |      >>> x.prod(1)\n",
+      "     |      matrix([[   0],\n",
+      "     |              [ 840],\n",
+      "     |              [7920]])\n",
+      "     |  \n",
+      "     |  ptp(self, axis=None, out=None)\n",
+      "     |      Peak-to-peak (maximum - minimum) value along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ptp` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ptp\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Same as `ndarray.ptp`, except, where that would return an `ndarray` object,\n",
+      "     |      this returns a `matrix` object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.ptp()\n",
+      "     |      11\n",
+      "     |      >>> x.ptp(0)\n",
+      "     |      matrix([[8, 8, 8, 8]])\n",
+      "     |      >>> x.ptp(1)\n",
+      "     |      matrix([[3],\n",
+      "     |              [3],\n",
+      "     |              [3]])\n",
+      "     |  \n",
+      "     |  ravel(self, order='C')\n",
+      "     |      Return a flattened matrix.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ravel` for more documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          The elements of `m` are read using this index order. 'C' means to\n",
+      "     |          index the elements in C-like order, with the last axis index\n",
+      "     |          changing fastest, back to the first axis index changing slowest.\n",
+      "     |          'F' means to index the elements in Fortran-like index order, with\n",
+      "     |          the first index changing fastest, and the last index changing\n",
+      "     |          slowest. Note that the 'C' and 'F' options take no account of the\n",
+      "     |          memory layout of the underlying array, and only refer to the order\n",
+      "     |          of axis indexing.  'A' means to read the elements in Fortran-like\n",
+      "     |          index order if `m` is Fortran *contiguous* in memory, C-like order\n",
+      "     |          otherwise.  'K' means to read the elements in the order they occur\n",
+      "     |          in memory, except for reversing the data when strides are negative.\n",
+      "     |          By default, 'C' index order is used.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : matrix\n",
+      "     |          Return the matrix flattened to shape `(1, N)` where `N`\n",
+      "     |          is the number of elements in the original matrix.\n",
+      "     |          A copy is made only if necessary.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      matrix.flatten : returns a similar output matrix but always a copy\n",
+      "     |      matrix.flat : a flat iterator on the array.\n",
+      "     |      numpy.ravel : related function which returns an ndarray\n",
+      "     |  \n",
+      "     |  squeeze(self, axis=None)\n",
+      "     |      Return a possibly reshaped matrix.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.squeeze` for more documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axis : None or int or tuple of ints, optional\n",
+      "     |          Selects a subset of the single-dimensional entries in the shape.\n",
+      "     |          If an axis is selected with shape entry greater than one,\n",
+      "     |          an error is raised.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      squeezed : matrix\n",
+      "     |          The matrix, but as a (1, N) matrix if it had shape (N, 1).\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.squeeze : related function\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      If `m` has a single column then that column is returned\n",
+      "     |      as the single row of a matrix.  Otherwise `m` is returned.\n",
+      "     |      The returned matrix is always either `m` itself or a view into `m`.\n",
+      "     |      Supplying an axis keyword argument will not affect the returned matrix\n",
+      "     |      but it may cause an error to be raised.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> c = np.matrix([[1], [2]])\n",
+      "     |      >>> c\n",
+      "     |      matrix([[1],\n",
+      "     |              [2]])\n",
+      "     |      >>> c.squeeze()\n",
+      "     |      matrix([[1, 2]])\n",
+      "     |      >>> r = c.T\n",
+      "     |      >>> r\n",
+      "     |      matrix([[1, 2]])\n",
+      "     |      >>> r.squeeze()\n",
+      "     |      matrix([[1, 2]])\n",
+      "     |      >>> m = np.matrix([[1, 2], [3, 4]])\n",
+      "     |      >>> m.squeeze()\n",
+      "     |      matrix([[1, 2],\n",
+      "     |              [3, 4]])\n",
+      "     |  \n",
+      "     |  std(self, axis=None, dtype=None, out=None, ddof=0)\n",
+      "     |      Return the standard deviation of the array elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.std` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.std\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.std`, except that where an `ndarray` would\n",
+      "     |      be returned, a `matrix` object is returned instead.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3, 4)))\n",
+      "     |      >>> x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.std()\n",
+      "     |      3.4520525295346629 # may vary\n",
+      "     |      >>> x.std(0)\n",
+      "     |      matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]]) # may vary\n",
+      "     |      >>> x.std(1)\n",
+      "     |      matrix([[ 1.11803399],\n",
+      "     |              [ 1.11803399],\n",
+      "     |              [ 1.11803399]])\n",
+      "     |  \n",
+      "     |  sum(self, axis=None, dtype=None, out=None)\n",
+      "     |      Returns the sum of the matrix elements, along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.sum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sum\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.sum`, except that where an `ndarray` would\n",
+      "     |      be returned, a `matrix` object is returned instead.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix([[1, 2], [4, 3]])\n",
+      "     |      >>> x.sum()\n",
+      "     |      10\n",
+      "     |      >>> x.sum(axis=1)\n",
+      "     |      matrix([[3],\n",
+      "     |              [7]])\n",
+      "     |      >>> x.sum(axis=1, dtype='float')\n",
+      "     |      matrix([[3.],\n",
+      "     |              [7.]])\n",
+      "     |      >>> out = np.zeros((2, 1), dtype='float')\n",
+      "     |      >>> x.sum(axis=1, dtype='float', out=np.asmatrix(out))\n",
+      "     |      matrix([[3.],\n",
+      "     |              [7.]])\n",
+      "     |  \n",
+      "     |  tolist(self)\n",
+      "     |      Return the matrix as a (possibly nested) list.\n",
+      "     |      \n",
+      "     |      See `ndarray.tolist` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ndarray.tolist\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.tolist()\n",
+      "     |      [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]\n",
+      "     |  \n",
+      "     |  var(self, axis=None, dtype=None, out=None, ddof=0)\n",
+      "     |      Returns the variance of the matrix elements, along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.var` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.var\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is the same as `ndarray.var`, except that where an `ndarray` would\n",
+      "     |      be returned, a `matrix` object is returned instead.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3, 4)))\n",
+      "     |      >>> x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.var()\n",
+      "     |      11.916666666666666\n",
+      "     |      >>> x.var(0)\n",
+      "     |      matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]]) # may vary\n",
+      "     |      >>> x.var(1)\n",
+      "     |      matrix([[1.25],\n",
+      "     |              [1.25],\n",
+      "     |              [1.25]])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(subtype, data, dtype=None, copy=True)\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  A\n",
+      "     |      Return `self` as an `ndarray` object.\n",
+      "     |      \n",
+      "     |      Equivalent to ``np.asarray(self)``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : ndarray\n",
+      "     |          `self` as an `ndarray`\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.getA()\n",
+      "     |      array([[ 0,  1,  2,  3],\n",
+      "     |             [ 4,  5,  6,  7],\n",
+      "     |             [ 8,  9, 10, 11]])\n",
+      "     |  \n",
+      "     |  A1\n",
+      "     |      Return `self` as a flattened `ndarray`.\n",
+      "     |      \n",
+      "     |      Equivalent to ``np.asarray(x).ravel()``\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : ndarray\n",
+      "     |          `self`, 1-D, as an `ndarray`\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n",
+      "     |      matrix([[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]])\n",
+      "     |      >>> x.getA1()\n",
+      "     |      array([ 0,  1,  2, ...,  9, 10, 11])\n",
+      "     |  \n",
+      "     |  H\n",
+      "     |      Returns the (complex) conjugate transpose of `self`.\n",
+      "     |      \n",
+      "     |      Equivalent to ``np.transpose(self)`` if `self` is real-valued.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : matrix object\n",
+      "     |          complex conjugate transpose of `self`\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.matrix(np.arange(12).reshape((3,4)))\n",
+      "     |      >>> z = x - 1j*x; z\n",
+      "     |      matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],\n",
+      "     |              [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],\n",
+      "     |              [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])\n",
+      "     |      >>> z.getH()\n",
+      "     |      matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],\n",
+      "     |              [ 1. +1.j,  5. +5.j,  9. +9.j],\n",
+      "     |              [ 2. +2.j,  6. +6.j, 10.+10.j],\n",
+      "     |              [ 3. +3.j,  7. +7.j, 11.+11.j]])\n",
+      "     |  \n",
+      "     |  I\n",
+      "     |      Returns the (multiplicative) inverse of invertible `self`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : matrix object\n",
+      "     |          If `self` is non-singular, `ret` is such that ``ret * self`` ==\n",
+      "     |          ``self * ret`` == ``np.matrix(np.eye(self[0,:].size))`` all return\n",
+      "     |          ``True``.\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      numpy.linalg.LinAlgError: Singular matrix\n",
+      "     |          If `self` is singular.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      linalg.inv\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> m = np.matrix('[1, 2; 3, 4]'); m\n",
+      "     |      matrix([[1, 2],\n",
+      "     |              [3, 4]])\n",
+      "     |      >>> m.getI()\n",
+      "     |      matrix([[-2. ,  1. ],\n",
+      "     |              [ 1.5, -0.5]])\n",
+      "     |      >>> m.getI() * m\n",
+      "     |      matrix([[ 1.,  0.], # may vary\n",
+      "     |              [ 0.,  1.]])\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      Returns the transpose of the matrix.\n",
+      "     |      \n",
+      "     |      Does *not* conjugate!  For the complex conjugate transpose, use ``.H``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      ret : matrix object\n",
+      "     |          The (non-conjugated) transpose of the matrix.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      transpose, getH\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> m = np.matrix('[1, 2; 3, 4]')\n",
+      "     |      >>> m\n",
+      "     |      matrix([[1, 2],\n",
+      "     |              [3, 4]])\n",
+      "     |      >>> m.getT()\n",
+      "     |      matrix([[1, 3],\n",
+      "     |              [2, 4]])\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __array_priority__ = 10.0\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      a.__array__([dtype], /) -> reference if type unchanged, copy otherwise.\n",
+      "     |      \n",
+      "     |      Returns either a new reference to self if dtype is not given or a new array\n",
+      "     |      of provided data type if dtype is different from the current dtype of the\n",
+      "     |      array.\n",
+      "     |  \n",
+      "     |  __array_function__(...)\n",
+      "     |  \n",
+      "     |  __array_prepare__(...)\n",
+      "     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n",
+      "     |  \n",
+      "     |  __array_ufunc__(...)\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |      a.__copy__()\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.copy` is called on an array. Returns a copy of the array.\n",
+      "     |      \n",
+      "     |      Equivalent to ``a.copy(order='K')``.\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |      a.__deepcopy__(memo, /) -> Deep copy of array.\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.deepcopy` is called on an array.\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      Default object formatter.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __iadd__(self, value, /)\n",
+      "     |      Return self+=value.\n",
+      "     |  \n",
+      "     |  __iand__(self, value, /)\n",
+      "     |      Return self&=value.\n",
+      "     |  \n",
+      "     |  __ifloordiv__(self, value, /)\n",
+      "     |      Return self//=value.\n",
+      "     |  \n",
+      "     |  __ilshift__(self, value, /)\n",
+      "     |      Return self<<=value.\n",
+      "     |  \n",
+      "     |  __imatmul__(self, value, /)\n",
+      "     |      Return self@=value.\n",
+      "     |  \n",
+      "     |  __imod__(self, value, /)\n",
+      "     |      Return self%=value.\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __ior__(self, value, /)\n",
+      "     |      Return self|=value.\n",
+      "     |  \n",
+      "     |  __irshift__(self, value, /)\n",
+      "     |      Return self>>=value.\n",
+      "     |  \n",
+      "     |  __isub__(self, value, /)\n",
+      "     |      Return self-=value.\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __itruediv__(self, value, /)\n",
+      "     |      Return self/=value.\n",
+      "     |  \n",
+      "     |  __ixor__(self, value, /)\n",
+      "     |      Return self^=value.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __matmul__(self, value, /)\n",
+      "     |      Return self@value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      a.__reduce__()\n",
+      "     |      \n",
+      "     |      For pickling.\n",
+      "     |  \n",
+      "     |  __reduce_ex__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmatmul__(self, value, /)\n",
+      "     |      Return value@self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |      a.__setstate__(state, /)\n",
+      "     |      \n",
+      "     |      For unpickling.\n",
+      "     |      \n",
+      "     |      The `state` argument must be a sequence that contains the following\n",
+      "     |      elements:\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      version : int\n",
+      "     |          optional pickle version. If omitted defaults to 0.\n",
+      "     |      shape : tuple\n",
+      "     |      dtype : data-type\n",
+      "     |      isFortran : bool\n",
+      "     |      rawdata : string or list\n",
+      "     |          a binary string with the data (or a list if 'a' is an object array)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  argpartition(...)\n",
+      "     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would partition this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argpartition` for full documentation.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argpartition : equivalent function\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      a.argsort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would sort this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argsort` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argsort : equivalent function\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n",
+      "     |      \n",
+      "     |      Copy of the array, cast to a specified type.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          Typecode or data-type to which the array is cast.\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout order of the result.\n",
+      "     |          'C' means C order, 'F' means Fortran order, 'A'\n",
+      "     |          means 'F' order if all the arrays are Fortran contiguous,\n",
+      "     |          'C' order otherwise, and 'K' means as close to the\n",
+      "     |          order the array elements appear in memory as possible.\n",
+      "     |          Default is 'K'.\n",
+      "     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n",
+      "     |          Controls what kind of data casting may occur. Defaults to 'unsafe'\n",
+      "     |          for backwards compatibility.\n",
+      "     |      \n",
+      "     |            * 'no' means the data types should not be cast at all.\n",
+      "     |            * 'equiv' means only byte-order changes are allowed.\n",
+      "     |            * 'safe' means only casts which can preserve values are allowed.\n",
+      "     |            * 'same_kind' means only safe casts or casts within a kind,\n",
+      "     |              like float64 to float32, are allowed.\n",
+      "     |            * 'unsafe' means any data conversions may be done.\n",
+      "     |      subok : bool, optional\n",
+      "     |          If True, then sub-classes will be passed-through (default), otherwise\n",
+      "     |          the returned array will be forced to be a base-class array.\n",
+      "     |      copy : bool, optional\n",
+      "     |          By default, astype always returns a newly allocated array. If this\n",
+      "     |          is set to false, and the `dtype`, `order`, and `subok`\n",
+      "     |          requirements are satisfied, the input array is returned instead\n",
+      "     |          of a copy.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      arr_t : ndarray\n",
+      "     |          Unless `copy` is False and the other conditions for returning the input\n",
+      "     |          array are satisfied (see description for `copy` input parameter), `arr_t`\n",
+      "     |          is a new array of the same shape as the input array, with dtype, order\n",
+      "     |          given by `dtype`, `order`.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      .. versionchanged:: 1.17.0\n",
+      "     |         Casting between a simple data type and a structured one is possible only\n",
+      "     |         for \"unsafe\" casting.  Casting to multiple fields is allowed, but\n",
+      "     |         casting from multiple fields is not.\n",
+      "     |      \n",
+      "     |      .. versionchanged:: 1.9.0\n",
+      "     |         Casting from numeric to string types in 'safe' casting mode requires\n",
+      "     |         that the string dtype length is long enough to store the max\n",
+      "     |         integer/float value converted.\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ComplexWarning\n",
+      "     |          When casting from complex to float or int. To avoid this,\n",
+      "     |          one should use ``a.real.astype(t)``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 2.5])\n",
+      "     |      >>> x\n",
+      "     |      array([1. ,  2. ,  2.5])\n",
+      "     |      \n",
+      "     |      >>> x.astype(int)\n",
+      "     |      array([1, 2, 2])\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      a.byteswap(inplace=False)\n",
+      "     |      \n",
+      "     |      Swap the bytes of the array elements\n",
+      "     |      \n",
+      "     |      Toggle between low-endian and big-endian data representation by\n",
+      "     |      returning a byteswapped array, optionally swapped in-place.\n",
+      "     |      Arrays of byte-strings are not swapped. The real and imaginary\n",
+      "     |      parts of a complex number are swapped individually.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      inplace : bool, optional\n",
+      "     |          If ``True``, swap bytes in-place, default is ``False``.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          The byteswapped array. If `inplace` is ``True``, this is\n",
+      "     |          a view to self.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x1', '0x100', '0x2233']\n",
+      "     |      >>> A.byteswap(inplace=True)\n",
+      "     |      array([  256,     1, 13090], dtype=int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x100', '0x1', '0x3322']\n",
+      "     |      \n",
+      "     |      Arrays of byte-strings are not swapped\n",
+      "     |      \n",
+      "     |      >>> A = np.array([b'ceg', b'fac'])\n",
+      "     |      >>> A.byteswap()\n",
+      "     |      array([b'ceg', b'fac'], dtype='|S3')\n",
+      "     |      \n",
+      "     |      ``A.newbyteorder().byteswap()`` produces an array with the same values\n",
+      "     |        but different representation in memory\n",
+      "     |      \n",
+      "     |      >>> A = np.array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,\n",
+      "     |             0, 0], dtype=uint8)\n",
+      "     |      >>> A.newbyteorder().byteswap(inplace=True)\n",
+      "     |      array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,\n",
+      "     |             0, 3], dtype=uint8)\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      a.choose(choices, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Use an index array to construct a new array from a set of choices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.choose` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.choose : equivalent function\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      a.clip(min=None, max=None, out=None, **kwargs)\n",
+      "     |      \n",
+      "     |      Return an array whose values are limited to ``[min, max]``.\n",
+      "     |      One of max or min must be given.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.clip` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.clip : equivalent function\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      a.compress(condition, axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return selected slices of this array along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.compress` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.compress : equivalent function\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |      a.conj()\n",
+      "     |      \n",
+      "     |      Complex-conjugate all elements.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      a.conjugate()\n",
+      "     |      \n",
+      "     |      Return the complex conjugate, element-wise.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      a.copy(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout of the copy. 'C' means C-order,\n",
+      "     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n",
+      "     |          'C' otherwise. 'K' means match the layout of `a` as closely\n",
+      "     |          as possible. (Note that this function and :func:`numpy.copy` are very\n",
+      "     |          similar, but have different default values for their order=\n",
+      "     |          arguments.)\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      numpy.copy\n",
+      "     |      numpy.copyto\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n",
+      "     |      \n",
+      "     |      >>> y = x.copy()\n",
+      "     |      \n",
+      "     |      >>> x.fill(0)\n",
+      "     |      \n",
+      "     |      >>> x\n",
+      "     |      array([[0, 0, 0],\n",
+      "     |             [0, 0, 0]])\n",
+      "     |      \n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      \n",
+      "     |      >>> y.flags['C_CONTIGUOUS']\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      a.cumprod(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative product of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumprod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumprod : equivalent function\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      a.cumsum(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative sum of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumsum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumsum : equivalent function\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      a.diagonal(offset=0, axis1=0, axis2=1)\n",
+      "     |      \n",
+      "     |      Return specified diagonals. In NumPy 1.9 the returned array is a\n",
+      "     |      read-only view instead of a copy as in previous NumPy versions.  In\n",
+      "     |      a future version the read-only restriction will be removed.\n",
+      "     |      \n",
+      "     |      Refer to :func:`numpy.diagonal` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.diagonal : equivalent function\n",
+      "     |  \n",
+      "     |  dot(...)\n",
+      "     |      a.dot(b, out=None)\n",
+      "     |      \n",
+      "     |      Dot product of two arrays.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.dot` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dot : equivalent function\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.eye(2)\n",
+      "     |      >>> b = np.ones((2, 2)) * 2\n",
+      "     |      >>> a.dot(b)\n",
+      "     |      array([[2.,  2.],\n",
+      "     |             [2.,  2.]])\n",
+      "     |      \n",
+      "     |      This array method can be conveniently chained:\n",
+      "     |      \n",
+      "     |      >>> a.dot(b).dot(b)\n",
+      "     |      array([[8.,  8.],\n",
+      "     |             [8.,  8.]])\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      a.dump(file)\n",
+      "     |      \n",
+      "     |      Dump a pickle of the array to the specified file.\n",
+      "     |      The array can be read back with pickle.load or numpy.load.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      file : str or Path\n",
+      "     |          A string naming the dump file.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      a.dumps()\n",
+      "     |      \n",
+      "     |      Returns the pickle of the array as a string.\n",
+      "     |      pickle.loads or numpy.loads will convert the string back to an array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      a.fill(value)\n",
+      "     |      \n",
+      "     |      Fill the array with a scalar value.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      value : scalar\n",
+      "     |          All elements of `a` will be assigned this value.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([1, 2])\n",
+      "     |      >>> a.fill(0)\n",
+      "     |      >>> a\n",
+      "     |      array([0, 0])\n",
+      "     |      >>> a = np.empty(2)\n",
+      "     |      >>> a.fill(1)\n",
+      "     |      >>> a\n",
+      "     |      array([1.,  1.])\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      a.getfield(dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Returns a field of the given array as a certain type.\n",
+      "     |      \n",
+      "     |      A field is a view of the array data with a given data-type. The values in\n",
+      "     |      the view are determined by the given type and the offset into the current\n",
+      "     |      array in bytes. The offset needs to be such that the view dtype fits in the\n",
+      "     |      array dtype; for example an array of dtype complex128 has 16-byte elements.\n",
+      "     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n",
+      "     |      between 0 and 12 bytes.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          The data type of the view. The dtype size of the view can not be larger\n",
+      "     |          than that of the array itself.\n",
+      "     |      offset : int\n",
+      "     |          Number of bytes to skip before beginning the element view.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.diag([1.+1.j]*2)\n",
+      "     |      >>> x[1, 1] = 2 + 4.j\n",
+      "     |      >>> x\n",
+      "     |      array([[1.+1.j,  0.+0.j],\n",
+      "     |             [0.+0.j,  2.+4.j]])\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  2.]])\n",
+      "     |      \n",
+      "     |      By choosing an offset of 8 bytes we can select the complex part of the\n",
+      "     |      array for our view:\n",
+      "     |      \n",
+      "     |      >>> x.getfield(np.float64, offset=8)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  4.]])\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      a.item(*args)\n",
+      "     |      \n",
+      "     |      Copy an element of an array to a standard Python scalar and return it.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments (variable number and type)\n",
+      "     |      \n",
+      "     |          * none: in this case, the method only works for arrays\n",
+      "     |            with one element (`a.size == 1`), which element is\n",
+      "     |            copied into a standard Python scalar object and returned.\n",
+      "     |      \n",
+      "     |          * int_type: this argument is interpreted as a flat index into\n",
+      "     |            the array, specifying which element to copy and return.\n",
+      "     |      \n",
+      "     |          * tuple of int_types: functions as does a single int_type argument,\n",
+      "     |            except that the argument is interpreted as an nd-index into the\n",
+      "     |            array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      z : Standard Python scalar object\n",
+      "     |          A copy of the specified element of the array as a suitable\n",
+      "     |          Python scalar\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      When the data type of `a` is longdouble or clongdouble, item() returns\n",
+      "     |      a scalar array object because there is no available Python scalar that\n",
+      "     |      would not lose information. Void arrays return a buffer object for item(),\n",
+      "     |      unless fields are defined, in which case a tuple is returned.\n",
+      "     |      \n",
+      "     |      `item` is very similar to a[args], except, instead of an array scalar,\n",
+      "     |      a standard Python scalar is returned. This can be useful for speeding up\n",
+      "     |      access to elements of the array and doing arithmetic on elements of the\n",
+      "     |      array using Python's optimized math.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.item(3)\n",
+      "     |      1\n",
+      "     |      >>> x.item(7)\n",
+      "     |      0\n",
+      "     |      >>> x.item((0, 1))\n",
+      "     |      2\n",
+      "     |      >>> x.item((2, 2))\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      a.itemset(*args)\n",
+      "     |      \n",
+      "     |      Insert scalar into an array (scalar is cast to array's dtype, if possible)\n",
+      "     |      \n",
+      "     |      There must be at least 1 argument, and define the last argument\n",
+      "     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster\n",
+      "     |      than ``a[args] = item``.  The item should be a scalar value and `args`\n",
+      "     |      must select a single item in the array `a`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments\n",
+      "     |          If one argument: a scalar, only used in case `a` is of size 1.\n",
+      "     |          If two arguments: the last argument is the value to be set\n",
+      "     |          and must be a scalar, the first argument specifies a single array\n",
+      "     |          element location. It is either an int or a tuple.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Compared to indexing syntax, `itemset` provides some speed increase\n",
+      "     |      for placing a scalar into a particular location in an `ndarray`,\n",
+      "     |      if you must do this.  However, generally this is discouraged:\n",
+      "     |      among other problems, it complicates the appearance of the code.\n",
+      "     |      Also, when using `itemset` (and `item`) inside a loop, be sure\n",
+      "     |      to assign the methods to a local variable to avoid the attribute\n",
+      "     |      look-up at each loop iteration.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.itemset(4, 0)\n",
+      "     |      >>> x.itemset((2, 2), 9)\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 0, 6],\n",
+      "     |             [1, 0, 9]])\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      arr.newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return the array with the same data viewed with a different byte order.\n",
+      "     |      \n",
+      "     |      Equivalent to::\n",
+      "     |      \n",
+      "     |          arr.view(arr.dtype.newbytorder(new_order))\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the array data\n",
+      "     |      type.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : string, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          below. `new_order` codes can be any of:\n",
+      "     |      \n",
+      "     |          * 'S' - swap dtype from current to opposite endian\n",
+      "     |          * {'<', 'L'} - little endian\n",
+      "     |          * {'>', 'B'} - big endian\n",
+      "     |          * {'=', 'N'} - native order\n",
+      "     |          * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |          The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_arr : array\n",
+      "     |          New array object with the dtype reflecting given change to the\n",
+      "     |          byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      a.nonzero()\n",
+      "     |      \n",
+      "     |      Return the indices of the elements that are non-zero.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.nonzero` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.nonzero : equivalent function\n",
+      "     |  \n",
+      "     |  partition(...)\n",
+      "     |      a.partition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Rearranges the elements in the array in such a way that the value of the\n",
+      "     |      element in kth position is in the position it would be in a sorted array.\n",
+      "     |      All elements smaller than the kth element are moved before this element and\n",
+      "     |      all equal or greater are moved behind it. The ordering of the elements in\n",
+      "     |      the two partitions is undefined.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      kth : int or sequence of ints\n",
+      "     |          Element index to partition by. The kth element value will be in its\n",
+      "     |          final sorted position and all smaller elements will be moved before it\n",
+      "     |          and all equal or greater elements behind it.\n",
+      "     |          The order of all elements in the partitions is undefined.\n",
+      "     |          If provided with a sequence of kth it will partition all elements\n",
+      "     |          indexed by kth of them into their sorted position at once.\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'introselect'}, optional\n",
+      "     |          Selection algorithm. Default is 'introselect'.\n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc. A single field can\n",
+      "     |          be specified as a string, and not all fields need to be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.partition : Return a parititioned copy of an array.\n",
+      "     |      argpartition : Indirect partition.\n",
+      "     |      sort : Full sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See ``np.partition`` for notes on the different algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([3, 4, 2, 1])\n",
+      "     |      >>> a.partition(3)\n",
+      "     |      >>> a\n",
+      "     |      array([2, 1, 3, 4])\n",
+      "     |      \n",
+      "     |      >>> a.partition((1, 3))\n",
+      "     |      >>> a\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      a.put(indices, values, mode='raise')\n",
+      "     |      \n",
+      "     |      Set ``a.flat[n] = values[n]`` for all `n` in indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.put` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.put : equivalent function\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      a.repeat(repeats, axis=None)\n",
+      "     |      \n",
+      "     |      Repeat elements of an array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.repeat` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.repeat : equivalent function\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      a.reshape(shape, order='C')\n",
+      "     |      \n",
+      "     |      Returns an array containing the same data with a new shape.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.reshape` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : equivalent function\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Unlike the free function `numpy.reshape`, this method on `ndarray` allows\n",
+      "     |      the elements of the shape parameter to be passed in as separate arguments.\n",
+      "     |      For example, ``a.reshape(10, 11)`` is equivalent to\n",
+      "     |      ``a.reshape((10, 11))``.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      a.resize(new_shape, refcheck=True)\n",
+      "     |      \n",
+      "     |      Change shape and size of array in-place.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_shape : tuple of ints, or `n` ints\n",
+      "     |          Shape of resized array.\n",
+      "     |      refcheck : bool, optional\n",
+      "     |          If False, reference count will not be checked. Default is True.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ValueError\n",
+      "     |          If `a` does not own its own data or references or views to it exist,\n",
+      "     |          and the data memory must be changed.\n",
+      "     |          PyPy only: will always raise if the data memory must be changed, since\n",
+      "     |          there is no reliable way to determine if references or views to it\n",
+      "     |          exist.\n",
+      "     |      \n",
+      "     |      SystemError\n",
+      "     |          If the `order` keyword argument is specified. This behaviour is a\n",
+      "     |          bug in NumPy.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      resize : Return a new array with the specified shape.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This reallocates space for the data area if necessary.\n",
+      "     |      \n",
+      "     |      Only contiguous arrays (data elements consecutive in memory) can be\n",
+      "     |      resized.\n",
+      "     |      \n",
+      "     |      The purpose of the reference count check is to make sure you\n",
+      "     |      do not use this array as a buffer for another Python object and then\n",
+      "     |      reallocate the memory. However, reference counts can increase in\n",
+      "     |      other ways so if you are sure that you have not shared the memory\n",
+      "     |      for this array with another Python object, then you may safely set\n",
+      "     |      `refcheck` to False.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      Shrinking an array: array is flattened (in the order that the data are\n",
+      "     |      stored in memory), resized, and reshaped:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='C')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [1]])\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='F')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [2]])\n",
+      "     |      \n",
+      "     |      Enlarging an array: as above, but missing entries are filled with zeros:\n",
+      "     |      \n",
+      "     |      >>> b = np.array([[0, 1], [2, 3]])\n",
+      "     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n",
+      "     |      >>> b\n",
+      "     |      array([[0, 1, 2],\n",
+      "     |             [3, 0, 0]])\n",
+      "     |      \n",
+      "     |      Referencing an array prevents resizing...\n",
+      "     |      \n",
+      "     |      >>> c = a\n",
+      "     |      >>> a.resize((1, 1))\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |      ...\n",
+      "     |      ValueError: cannot resize an array that references or is referenced ...\n",
+      "     |      \n",
+      "     |      Unless `refcheck` is False:\n",
+      "     |      \n",
+      "     |      >>> a.resize((1, 1), refcheck=False)\n",
+      "     |      >>> a\n",
+      "     |      array([[0]])\n",
+      "     |      >>> c\n",
+      "     |      array([[0]])\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      a.round(decimals=0, out=None)\n",
+      "     |      \n",
+      "     |      Return `a` with each element rounded to the given number of decimals.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.around` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.around : equivalent function\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      a.searchsorted(v, side='left', sorter=None)\n",
+      "     |      \n",
+      "     |      Find indices where elements of v should be inserted in a to maintain order.\n",
+      "     |      \n",
+      "     |      For full documentation, see `numpy.searchsorted`\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.searchsorted : equivalent function\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      a.setfield(val, dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Put a value into a specified place in a field defined by a data-type.\n",
+      "     |      \n",
+      "     |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n",
+      "     |      bytes into the field.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      val : object\n",
+      "     |          Value to be placed in field.\n",
+      "     |      dtype : dtype object\n",
+      "     |          Data-type of the field in which to place `val`.\n",
+      "     |      offset : int, optional\n",
+      "     |          The number of bytes into the field at which to place `val`.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      getfield\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.eye(3)\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |      >>> x.setfield(3, np.int32)\n",
+      "     |      >>> x.getfield(np.int32)\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3],\n",
+      "     |             [3, 3, 3]], dtype=int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.0e+000, 1.5e-323, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.0e+000, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.5e-323, 1.0e+000]])\n",
+      "     |      >>> x.setfield(np.eye(3), np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      a.setflags(write=None, align=None, uic=None)\n",
+      "     |      \n",
+      "     |      Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),\n",
+      "     |      respectively.\n",
+      "     |      \n",
+      "     |      These Boolean-valued flags affect how numpy interprets the memory\n",
+      "     |      area used by `a` (see Notes below). The ALIGNED flag can only\n",
+      "     |      be set to True if the data is actually aligned according to the type.\n",
+      "     |      The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set\n",
+      "     |      to True. The flag WRITEABLE can only be set to True if the array owns its\n",
+      "     |      own memory, or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |      interface, or is a string. (The exception for string is made so that\n",
+      "     |      unpickling can be done without copying memory.)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      write : bool, optional\n",
+      "     |          Describes whether or not `a` can be written to.\n",
+      "     |      align : bool, optional\n",
+      "     |          Describes whether or not `a` is aligned properly for its type.\n",
+      "     |      uic : bool, optional\n",
+      "     |          Describes whether or not `a` is a copy of another \"base\" array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Array flags provide information about how the memory area used\n",
+      "     |      for the array is to be interpreted. There are 7 Boolean flags\n",
+      "     |      in use, only four of which can be changed by the user:\n",
+      "     |      WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n",
+      "     |      \n",
+      "     |      WRITEABLE (W) the data area can be written to;\n",
+      "     |      \n",
+      "     |      ALIGNED (A) the data and strides are aligned appropriately for the hardware\n",
+      "     |      (as determined by the compiler);\n",
+      "     |      \n",
+      "     |      UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;\n",
+      "     |      \n",
+      "     |      WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced\n",
+      "     |      by .base). When the C-API function PyArray_ResolveWritebackIfCopy is\n",
+      "     |      called, the base array will be updated with the contents of this array.\n",
+      "     |      \n",
+      "     |      All flags can be accessed using the single (upper case) letter as well\n",
+      "     |      as the full name.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.array([[3, 1, 7],\n",
+      "     |      ...               [2, 0, 0],\n",
+      "     |      ...               [8, 5, 9]])\n",
+      "     |      >>> y\n",
+      "     |      array([[3, 1, 7],\n",
+      "     |             [2, 0, 0],\n",
+      "     |             [8, 5, 9]])\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : True\n",
+      "     |        ALIGNED : True\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(write=0, align=0)\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : False\n",
+      "     |        ALIGNED : False\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(uic=1)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: cannot set WRITEBACKIFCOPY flag to True\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      a.sort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Sort an array in-place. Refer to `numpy.sort` for full documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n",
+      "     |          Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n",
+      "     |          and 'mergesort' use timsort under the covers and, in general, the\n",
+      "     |          actual implementation will vary with datatype. The 'mergesort' option\n",
+      "     |          is retained for backwards compatibility.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.15.0.\n",
+      "     |             The 'stable' option was added.\n",
+      "     |      \n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc.  A single field can\n",
+      "     |          be specified as a string, and not all fields need be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sort : Return a sorted copy of an array.\n",
+      "     |      numpy.argsort : Indirect sort.\n",
+      "     |      numpy.lexsort : Indirect stable sort on multiple keys.\n",
+      "     |      numpy.searchsorted : Find elements in sorted array.\n",
+      "     |      numpy.partition: Partial sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See `numpy.sort` for notes on the different sorting algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,4], [3,1]])\n",
+      "     |      >>> a.sort(axis=1)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 4],\n",
+      "     |             [1, 3]])\n",
+      "     |      >>> a.sort(axis=0)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 3],\n",
+      "     |             [1, 4]])\n",
+      "     |      \n",
+      "     |      Use the `order` keyword to specify a field to use when sorting a\n",
+      "     |      structured array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n",
+      "     |      >>> a.sort(order='y')\n",
+      "     |      >>> a\n",
+      "     |      array([(b'c', 1), (b'a', 2)],\n",
+      "     |            dtype=[('x', 'S1'), ('y', '<i8')])\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      a.swapaxes(axis1, axis2)\n",
+      "     |      \n",
+      "     |      Return a view of the array with `axis1` and `axis2` interchanged.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.swapaxes` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.swapaxes : equivalent function\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      a.take(indices, axis=None, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Return an array formed from the elements of `a` at the given indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.take` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.take : equivalent function\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |      a.tobytes(order='C')\n",
+      "     |      \n",
+      "     |      Construct Python bytes containing the raw data bytes in the array.\n",
+      "     |      \n",
+      "     |      Constructs Python bytes showing a copy of the raw contents of\n",
+      "     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',\n",
+      "     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n",
+      "     |      unless the F_CONTIGUOUS flag in the array is set, in which case it\n",
+      "     |      means 'Fortran' order.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.9.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', None}, optional\n",
+      "     |          Order of the data for multidimensional arrays:\n",
+      "     |          C, Fortran, or the same as for the original array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      s : bytes\n",
+      "     |          Python bytes exhibiting a copy of `a`'s raw data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype='<u2')\n",
+      "     |      >>> x.tobytes()\n",
+      "     |      b'\\x00\\x00\\x01\\x00\\x02\\x00\\x03\\x00'\n",
+      "     |      >>> x.tobytes('C') == x.tobytes()\n",
+      "     |      True\n",
+      "     |      >>> x.tobytes('F')\n",
+      "     |      b'\\x00\\x00\\x02\\x00\\x01\\x00\\x03\\x00'\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      a.tofile(fid, sep=\"\", format=\"%s\")\n",
+      "     |      \n",
+      "     |      Write array to a file as text or binary (default).\n",
+      "     |      \n",
+      "     |      Data is always written in 'C' order, independent of the order of `a`.\n",
+      "     |      The data produced by this method can be recovered using the function\n",
+      "     |      fromfile().\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      fid : file or str or Path\n",
+      "     |          An open file object, or a string containing a filename.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |      \n",
+      "     |      sep : str\n",
+      "     |          Separator between array items for text output.\n",
+      "     |          If \"\" (empty), a binary file is written, equivalent to\n",
+      "     |          ``file.write(a.tobytes())``.\n",
+      "     |      format : str\n",
+      "     |          Format string for text file output.\n",
+      "     |          Each entry in the array is formatted to text by first converting\n",
+      "     |          it to the closest Python type, and then using \"format\" % item.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is a convenience function for quick storage of array data.\n",
+      "     |      Information on endianness and precision is lost, so this method is not a\n",
+      "     |      good choice for files intended to archive data or transport data between\n",
+      "     |      machines with different endianness. Some of these problems can be overcome\n",
+      "     |      by outputting the data as text files, at the expense of speed and file\n",
+      "     |      size.\n",
+      "     |      \n",
+      "     |      When fid is a file object, array contents are directly written to the\n",
+      "     |      file, bypassing the file object's ``write`` method. As a result, tofile\n",
+      "     |      cannot be used with files objects supporting compression (e.g., GzipFile)\n",
+      "     |      or file-like objects that do not support ``fileno()`` (e.g., BytesIO).\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      a.tostring(order='C')\n",
+      "     |      \n",
+      "     |      A compatibility alias for `tobytes`, with exactly the same behavior.\n",
+      "     |      \n",
+      "     |      Despite its name, it returns `bytes` not `str`\\ s.\n",
+      "     |      \n",
+      "     |      .. deprecated:: 1.19.0\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the sum along diagonals of the array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.trace` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.trace : equivalent function\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      a.transpose(*axes)\n",
+      "     |      \n",
+      "     |      Returns a view of the array with axes transposed.\n",
+      "     |      \n",
+      "     |      For a 1-D array this has no effect, as a transposed vector is simply the\n",
+      "     |      same vector. To convert a 1-D array into a 2D column vector, an additional\n",
+      "     |      dimension must be added. `np.atleast2d(a).T` achieves this, as does\n",
+      "     |      `a[:, np.newaxis]`.\n",
+      "     |      For a 2-D array, this is a standard matrix transpose.\n",
+      "     |      For an n-D array, if axes are given, their order indicates how the\n",
+      "     |      axes are permuted (see Examples). If axes are not provided and\n",
+      "     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n",
+      "     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axes : None, tuple of ints, or `n` ints\n",
+      "     |      \n",
+      "     |       * None or no argument: reverses the order of the axes.\n",
+      "     |      \n",
+      "     |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n",
+      "     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n",
+      "     |      \n",
+      "     |       * `n` ints: same as an n-tuple of the same ints (this form is\n",
+      "     |         intended simply as a \"convenience\" alternative to the tuple form)\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          View of `a`, with axes suitably permuted.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ndarray.T : Array property returning the array transposed.\n",
+      "     |      ndarray.reshape : Give a new shape to an array without changing its data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]])\n",
+      "     |      >>> a.transpose()\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose((1, 0))\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose(1, 0)\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      a.view([dtype][, type])\n",
+      "     |      \n",
+      "     |      New view of array with the same data.\n",
+      "     |      \n",
+      "     |      .. note::\n",
+      "     |          Passing None for ``dtype`` is different from omitting the parameter,\n",
+      "     |          since the former invokes ``dtype(None)`` which is an alias for\n",
+      "     |          ``dtype('float_')``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : data-type or ndarray sub-class, optional\n",
+      "     |          Data-type descriptor of the returned view, e.g., float32 or int16.\n",
+      "     |          Omitting it results in the view having the same data-type as `a`.\n",
+      "     |          This argument can also be specified as an ndarray sub-class, which\n",
+      "     |          then specifies the type of the returned object (this is equivalent to\n",
+      "     |          setting the ``type`` parameter).\n",
+      "     |      type : Python type, optional\n",
+      "     |          Type of the returned view, e.g., ndarray or matrix.  Again, omission\n",
+      "     |          of the parameter results in type preservation.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      ``a.view()`` is used two different ways:\n",
+      "     |      \n",
+      "     |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n",
+      "     |      of the array's memory with a different data-type.  This can cause a\n",
+      "     |      reinterpretation of the bytes of memory.\n",
+      "     |      \n",
+      "     |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n",
+      "     |      returns an instance of `ndarray_subclass` that looks at the same array\n",
+      "     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the\n",
+      "     |      memory.\n",
+      "     |      \n",
+      "     |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n",
+      "     |      bytes per entry than the previous dtype (for example, converting a\n",
+      "     |      regular array to a structured array), then the behavior of the view\n",
+      "     |      cannot be predicted just from the superficial appearance of ``a`` (shown\n",
+      "     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in\n",
+      "     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n",
+      "     |      defined as a slice or transpose, etc., the view may give different\n",
+      "     |      results.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      \n",
+      "     |      Viewing array data using a different type and dtype:\n",
+      "     |      \n",
+      "     |      >>> y = x.view(dtype=np.int16, type=np.matrix)\n",
+      "     |      >>> y\n",
+      "     |      matrix([[513]], dtype=int16)\n",
+      "     |      >>> print(type(y))\n",
+      "     |      <class 'numpy.matrix'>\n",
+      "     |      \n",
+      "     |      Creating a view on a structured array so it can be used in calculations\n",
+      "     |      \n",
+      "     |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n",
+      "     |      >>> xv\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]], dtype=int8)\n",
+      "     |      >>> xv.mean(0)\n",
+      "     |      array([2.,  3.])\n",
+      "     |      \n",
+      "     |      Making changes to the view changes the underlying array\n",
+      "     |      \n",
+      "     |      >>> xv[0,1] = 20\n",
+      "     |      >>> x\n",
+      "     |      array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])\n",
+      "     |      \n",
+      "     |      Using a view to convert an array to a recarray:\n",
+      "     |      \n",
+      "     |      >>> z = x.view(np.recarray)\n",
+      "     |      >>> z.a\n",
+      "     |      array([1, 3], dtype=int8)\n",
+      "     |      \n",
+      "     |      Views share data:\n",
+      "     |      \n",
+      "     |      >>> x[0] = (9, 10)\n",
+      "     |      >>> z[0]\n",
+      "     |      (9, 10)\n",
+      "     |      \n",
+      "     |      Views that change the dtype size (bytes per entry) should normally be\n",
+      "     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n",
+      "     |      >>> y = x[:, 0:2]\n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2],\n",
+      "     |             [4, 5]], dtype=int16)\n",
+      "     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |          ...\n",
+      "     |      ValueError: To change to a dtype of a different size, the array must be C-contiguous\n",
+      "     |      >>> z = y.copy()\n",
+      "     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      array([[(1, 2)],\n",
+      "     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: C-struct side.\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      Base object if memory is from some other object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      The base of an array that owns its memory is None:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([1,2,3,4])\n",
+      "     |      >>> x.base is None\n",
+      "     |      True\n",
+      "     |      \n",
+      "     |      Slicing creates a view, whose memory is shared with x:\n",
+      "     |      \n",
+      "     |      >>> y = x[2:]\n",
+      "     |      >>> y.base is x\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  ctypes\n",
+      "     |      An object to simplify the interaction of the array with the ctypes\n",
+      "     |      module.\n",
+      "     |      \n",
+      "     |      This attribute creates an object that makes it easier to use arrays\n",
+      "     |      when calling shared libraries with the ctypes module. The returned\n",
+      "     |      object has, among others, data, shape, and strides attributes (see\n",
+      "     |      Notes below) which themselves return ctypes objects that can be used\n",
+      "     |      as arguments to a shared library.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      c : Python object\n",
+      "     |          Possessing attributes data, shape, strides, etc.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ctypeslib\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Below are the public attributes of this object which were documented\n",
+      "     |      in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n",
+      "     |      as well as documented private attributes):\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.data\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.shape\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.strides\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.data_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.shape_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.strides_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      If the ctypes module is not available, then the ctypes attribute\n",
+      "     |      of array objects still returns something useful, but ctypes objects\n",
+      "     |      are not returned and errors may be raised instead. In particular,\n",
+      "     |      the object will still have the ``as_parameter`` attribute which will\n",
+      "     |      return an integer equal to the data attribute.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> import ctypes\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]], dtype=int32)\n",
+      "     |      >>> x.ctypes.data\n",
+      "     |      31962608 # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))\n",
+      "     |      <__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents\n",
+      "     |      c_uint(0)\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents\n",
+      "     |      c_ulong(4294967296)\n",
+      "     |      >>> x.ctypes.shape\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary\n",
+      "     |      >>> x.ctypes.strides\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      Python buffer object pointing to the start of the array's data.\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      Data-type of the array's elements.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      d : numpy dtype object\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dtype\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]])\n",
+      "     |      >>> x.dtype\n",
+      "     |      dtype('int32')\n",
+      "     |      >>> type(x.dtype)\n",
+      "     |      <type 'numpy.dtype'>\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      Information about the memory layout of the array.\n",
+      "     |      \n",
+      "     |      Attributes\n",
+      "     |      ----------\n",
+      "     |      C_CONTIGUOUS (C)\n",
+      "     |          The data is in a single, C-style contiguous segment.\n",
+      "     |      F_CONTIGUOUS (F)\n",
+      "     |          The data is in a single, Fortran-style contiguous segment.\n",
+      "     |      OWNDATA (O)\n",
+      "     |          The array owns the memory it uses or borrows it from another object.\n",
+      "     |      WRITEABLE (W)\n",
+      "     |          The data area can be written to.  Setting this to False locks\n",
+      "     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE\n",
+      "     |          from its base array at creation time, but a view of a writeable\n",
+      "     |          array may be subsequently locked while the base array remains writeable.\n",
+      "     |          (The opposite is not true, in that a view of a locked array may not\n",
+      "     |          be made writeable.  However, currently, locking a base object does not\n",
+      "     |          lock any views that already reference it, so under that circumstance it\n",
+      "     |          is possible to alter the contents of a locked array via a previously\n",
+      "     |          created writeable view onto it.)  Attempting to change a non-writeable\n",
+      "     |          array raises a RuntimeError exception.\n",
+      "     |      ALIGNED (A)\n",
+      "     |          The data and all elements are aligned appropriately for the hardware.\n",
+      "     |      WRITEBACKIFCOPY (X)\n",
+      "     |          This array is a copy of some other array. The C-API function\n",
+      "     |          PyArray_ResolveWritebackIfCopy must be called before deallocating\n",
+      "     |          to the base array will be updated with the contents of this array.\n",
+      "     |      UPDATEIFCOPY (U)\n",
+      "     |          (Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array.\n",
+      "     |          When this array is\n",
+      "     |          deallocated, the base array will be updated with the contents of\n",
+      "     |          this array.\n",
+      "     |      FNC\n",
+      "     |          F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      FORC\n",
+      "     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n",
+      "     |      BEHAVED (B)\n",
+      "     |          ALIGNED and WRITEABLE.\n",
+      "     |      CARRAY (CA)\n",
+      "     |          BEHAVED and C_CONTIGUOUS.\n",
+      "     |      FARRAY (FA)\n",
+      "     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n",
+      "     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n",
+      "     |      names are only supported in dictionary access.\n",
+      "     |      \n",
+      "     |      Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be\n",
+      "     |      changed by the user, via direct assignment to the attribute or dictionary\n",
+      "     |      entry, or by calling `ndarray.setflags`.\n",
+      "     |      \n",
+      "     |      The array flags cannot be set arbitrarily:\n",
+      "     |      \n",
+      "     |      - UPDATEIFCOPY can only be set ``False``.\n",
+      "     |      - WRITEBACKIFCOPY can only be set ``False``.\n",
+      "     |      - ALIGNED can only be set ``True`` if the data is truly aligned.\n",
+      "     |      - WRITEABLE can only be set ``True`` if the array owns its own memory\n",
+      "     |        or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |        interface or is a string.\n",
+      "     |      \n",
+      "     |      Arrays can be both C-style and Fortran-style contiguous simultaneously.\n",
+      "     |      This is clear for 1-dimensional arrays, but can also be true for higher\n",
+      "     |      dimensional arrays.\n",
+      "     |      \n",
+      "     |      Even for contiguous arrays a stride for a given dimension\n",
+      "     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n",
+      "     |      or the array has no elements.\n",
+      "     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n",
+      "     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n",
+      "     |      Fortran-style contiguous arrays is true.\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      A 1-D iterator over the array.\n",
+      "     |      \n",
+      "     |      This is a `numpy.flatiter` instance, which acts similarly to, but is not\n",
+      "     |      a subclass of, Python's built-in iterator object.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      flatten : Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      flatiter\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(1, 7).reshape(2, 3)\n",
+      "     |      >>> x\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      >>> x.flat[3]\n",
+      "     |      4\n",
+      "     |      >>> x.T\n",
+      "     |      array([[1, 4],\n",
+      "     |             [2, 5],\n",
+      "     |             [3, 6]])\n",
+      "     |      >>> x.T.flat[3]\n",
+      "     |      5\n",
+      "     |      >>> type(x.flat)\n",
+      "     |      <class 'numpy.flatiter'>\n",
+      "     |      \n",
+      "     |      An assignment example:\n",
+      "     |      \n",
+      "     |      >>> x.flat = 3; x\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3]])\n",
+      "     |      >>> x.flat[[1,4]] = 1; x\n",
+      "     |      array([[3, 1, 3],\n",
+      "     |             [3, 1, 3]])\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      The imaginary part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.imag\n",
+      "     |      array([ 0.        ,  0.70710678])\n",
+      "     |      >>> x.imag.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      Length of one array element in bytes.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.float64)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      8\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.complex128)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      16\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      Total bytes consumed by the elements of the array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Does not include memory consumed by non-element attributes of the\n",
+      "     |      array object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)\n",
+      "     |      >>> x.nbytes\n",
+      "     |      480\n",
+      "     |      >>> np.prod(x.shape) * x.itemsize\n",
+      "     |      480\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      Number of array dimensions.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> x.ndim\n",
+      "     |      1\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.ndim\n",
+      "     |      3\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      The real part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.real\n",
+      "     |      array([ 1.        ,  0.70710678])\n",
+      "     |      >>> x.real.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.real : equivalent function\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      Tuple of array dimensions.\n",
+      "     |      \n",
+      "     |      The shape property is usually used to get the current shape of an array,\n",
+      "     |      but may also be used to reshape the array in-place by assigning a tuple of\n",
+      "     |      array dimensions to it.  As with `numpy.reshape`, one of the new shape\n",
+      "     |      dimensions can be -1, in which case its value is inferred from the size of\n",
+      "     |      the array and the remaining dimensions. Reshaping an array in-place will\n",
+      "     |      fail if a copy is required.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3, 4])\n",
+      "     |      >>> x.shape\n",
+      "     |      (4,)\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.shape\n",
+      "     |      (2, 3, 4)\n",
+      "     |      >>> y.shape = (3, 8)\n",
+      "     |      >>> y\n",
+      "     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
+      "     |      >>> y.shape = (3, 6)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: total size of new array must be unchanged\n",
+      "     |      >>> np.zeros((4,2))[::2].shape = (-1,)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      AttributeError: Incompatible shape for in-place modification. Use\n",
+      "     |      `.reshape()` to make a copy with the desired shape.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : similar function\n",
+      "     |      ndarray.reshape : similar method\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      Number of elements in the array.\n",
+      "     |      \n",
+      "     |      Equal to ``np.prod(a.shape)``, i.e., the product of the array's\n",
+      "     |      dimensions.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      `a.size` returns a standard arbitrary precision Python integer. This\n",
+      "     |      may not be the case with other methods of obtaining the same value\n",
+      "     |      (like the suggested ``np.prod(a.shape)``, which returns an instance\n",
+      "     |      of ``np.int_``), and may be relevant if the value is used further in\n",
+      "     |      calculations that may overflow a fixed size integer type.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n",
+      "     |      >>> x.size\n",
+      "     |      30\n",
+      "     |      >>> np.prod(x.shape)\n",
+      "     |      30\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      Tuple of bytes to step in each dimension when traversing an array.\n",
+      "     |      \n",
+      "     |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n",
+      "     |      is::\n",
+      "     |      \n",
+      "     |          offset = sum(np.array(i) * a.strides)\n",
+      "     |      \n",
+      "     |      A more detailed explanation of strides can be found in the\n",
+      "     |      \"ndarray.rst\" file in the NumPy reference guide.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Imagine an array of 32-bit integers (each 4 bytes)::\n",
+      "     |      \n",
+      "     |        x = np.array([[0, 1, 2, 3, 4],\n",
+      "     |                      [5, 6, 7, 8, 9]], dtype=np.int32)\n",
+      "     |      \n",
+      "     |      This array is stored in memory as 40 bytes, one after the other\n",
+      "     |      (known as a contiguous block of memory).  The strides of an array tell\n",
+      "     |      us how many bytes we have to skip in memory to move to the next position\n",
+      "     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to\n",
+      "     |      move to the next column, but 20 bytes (5 values) to get to the same\n",
+      "     |      position in the next row.  As such, the strides for the array `x` will be\n",
+      "     |      ``(20, 4)``.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.lib.stride_tricks.as_strided\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n",
+      "     |      >>> y\n",
+      "     |      array([[[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]],\n",
+      "     |             [[12, 13, 14, 15],\n",
+      "     |              [16, 17, 18, 19],\n",
+      "     |              [20, 21, 22, 23]]])\n",
+      "     |      >>> y.strides\n",
+      "     |      (48, 16, 4)\n",
+      "     |      >>> y[1,1,1]\n",
+      "     |      17\n",
+      "     |      >>> offset=sum(y.strides * np.array((1,1,1)))\n",
+      "     |      >>> offset/y.itemsize\n",
+      "     |      17\n",
+      "     |      \n",
+      "     |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n",
+      "     |      >>> x.strides\n",
+      "     |      (32, 4, 224, 1344)\n",
+      "     |      >>> i = np.array([3,5,2,2])\n",
+      "     |      >>> offset = sum(i * x.strides)\n",
+      "     |      >>> x[3,5,2,2]\n",
+      "     |      813\n",
+      "     |      >>> offset / x.itemsize\n",
+      "     |      813\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class memmap(ndarray)\n",
+      "     |  memmap(filename, dtype=<class 'numpy.uint8'>, mode='r+', offset=0, shape=None, order='C')\n",
+      "     |  \n",
+      "     |  Create a memory-map to an array stored in a *binary* file on disk.\n",
+      "     |  \n",
+      "     |  Memory-mapped files are used for accessing small segments of large files\n",
+      "     |  on disk, without reading the entire file into memory.  NumPy's\n",
+      "     |  memmap's are array-like objects.  This differs from Python's ``mmap``\n",
+      "     |  module, which uses file-like objects.\n",
+      "     |  \n",
+      "     |  This subclass of ndarray has some unpleasant interactions with\n",
+      "     |  some operations, because it doesn't quite fit properly as a subclass.\n",
+      "     |  An alternative to using this subclass is to create the ``mmap``\n",
+      "     |  object yourself, then create an ndarray with ndarray.__new__ directly,\n",
+      "     |  passing the object created in its 'buffer=' parameter.\n",
+      "     |  \n",
+      "     |  This class may at some point be turned into a factory function\n",
+      "     |  which returns a view into an mmap buffer.\n",
+      "     |  \n",
+      "     |  Delete the memmap instance to close the memmap file.\n",
+      "     |  \n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  filename : str, file-like object, or pathlib.Path instance\n",
+      "     |      The file name or file object to be used as the array data buffer.\n",
+      "     |  dtype : data-type, optional\n",
+      "     |      The data-type used to interpret the file contents.\n",
+      "     |      Default is `uint8`.\n",
+      "     |  mode : {'r+', 'r', 'w+', 'c'}, optional\n",
+      "     |      The file is opened in this mode:\n",
+      "     |  \n",
+      "     |      +------+-------------------------------------------------------------+\n",
+      "     |      | 'r'  | Open existing file for reading only.                        |\n",
+      "     |      +------+-------------------------------------------------------------+\n",
+      "     |      | 'r+' | Open existing file for reading and writing.                 |\n",
+      "     |      +------+-------------------------------------------------------------+\n",
+      "     |      | 'w+' | Create or overwrite existing file for reading and writing.  |\n",
+      "     |      +------+-------------------------------------------------------------+\n",
+      "     |      | 'c'  | Copy-on-write: assignments affect data in memory, but       |\n",
+      "     |      |      | changes are not saved to disk.  The file on disk is         |\n",
+      "     |      |      | read-only.                                                  |\n",
+      "     |      +------+-------------------------------------------------------------+\n",
+      "     |  \n",
+      "     |      Default is 'r+'.\n",
+      "     |  offset : int, optional\n",
+      "     |      In the file, array data starts at this offset. Since `offset` is\n",
+      "     |      measured in bytes, it should normally be a multiple of the byte-size\n",
+      "     |      of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of\n",
+      "     |      file are valid; The file will be extended to accommodate the\n",
+      "     |      additional data. By default, ``memmap`` will start at the beginning of\n",
+      "     |      the file, even if ``filename`` is a file pointer ``fp`` and\n",
+      "     |      ``fp.tell() != 0``.\n",
+      "     |  shape : tuple, optional\n",
+      "     |      The desired shape of the array. If ``mode == 'r'`` and the number\n",
+      "     |      of remaining bytes after `offset` is not a multiple of the byte-size\n",
+      "     |      of `dtype`, you must specify `shape`. By default, the returned array\n",
+      "     |      will be 1-D with the number of elements determined by file size\n",
+      "     |      and data-type.\n",
+      "     |  order : {'C', 'F'}, optional\n",
+      "     |      Specify the order of the ndarray memory layout:\n",
+      "     |      :term:`row-major`, C-style or :term:`column-major`,\n",
+      "     |      Fortran-style.  This only has an effect if the shape is\n",
+      "     |      greater than 1-D.  The default order is 'C'.\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  filename : str or pathlib.Path instance\n",
+      "     |      Path to the mapped file.\n",
+      "     |  offset : int\n",
+      "     |      Offset position in the file.\n",
+      "     |  mode : str\n",
+      "     |      File mode.\n",
+      "     |  \n",
+      "     |  Methods\n",
+      "     |  -------\n",
+      "     |  flush\n",
+      "     |      Flush any changes in memory to file on disk.\n",
+      "     |      When you delete a memmap object, flush is called first to write\n",
+      "     |      changes to disk before removing the object.\n",
+      "     |  \n",
+      "     |  \n",
+      "     |  See also\n",
+      "     |  --------\n",
+      "     |  lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  The memmap object can be used anywhere an ndarray is accepted.\n",
+      "     |  Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns\n",
+      "     |  ``True``.\n",
+      "     |  \n",
+      "     |  Memory-mapped files cannot be larger than 2GB on 32-bit systems.\n",
+      "     |  \n",
+      "     |  When a memmap causes a file to be created or extended beyond its\n",
+      "     |  current size in the filesystem, the contents of the new part are\n",
+      "     |  unspecified. On systems with POSIX filesystem semantics, the extended\n",
+      "     |  part will be filled with zero bytes.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> data = np.arange(12, dtype='float32')\n",
+      "     |  >>> data.resize((3,4))\n",
+      "     |  \n",
+      "     |  This example uses a temporary file so that doctest doesn't write\n",
+      "     |  files to your directory. You would use a 'normal' filename.\n",
+      "     |  \n",
+      "     |  >>> from tempfile import mkdtemp\n",
+      "     |  >>> import os.path as path\n",
+      "     |  >>> filename = path.join(mkdtemp(), 'newfile.dat')\n",
+      "     |  \n",
+      "     |  Create a memmap with dtype and shape that matches our data:\n",
+      "     |  \n",
+      "     |  >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))\n",
+      "     |  >>> fp\n",
+      "     |  memmap([[0., 0., 0., 0.],\n",
+      "     |          [0., 0., 0., 0.],\n",
+      "     |          [0., 0., 0., 0.]], dtype=float32)\n",
+      "     |  \n",
+      "     |  Write data to memmap array:\n",
+      "     |  \n",
+      "     |  >>> fp[:] = data[:]\n",
+      "     |  >>> fp\n",
+      "     |  memmap([[  0.,   1.,   2.,   3.],\n",
+      "     |          [  4.,   5.,   6.,   7.],\n",
+      "     |          [  8.,   9.,  10.,  11.]], dtype=float32)\n",
+      "     |  \n",
+      "     |  >>> fp.filename == path.abspath(filename)\n",
+      "     |  True\n",
+      "     |  \n",
+      "     |  Deletion flushes memory changes to disk before removing the object:\n",
+      "     |  \n",
+      "     |  >>> del fp\n",
+      "     |  \n",
+      "     |  Load the memmap and verify data was stored:\n",
+      "     |  \n",
+      "     |  >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))\n",
+      "     |  >>> newfp\n",
+      "     |  memmap([[  0.,   1.,   2.,   3.],\n",
+      "     |          [  4.,   5.,   6.,   7.],\n",
+      "     |          [  8.,   9.,  10.,  11.]], dtype=float32)\n",
+      "     |  \n",
+      "     |  Read-only memmap:\n",
+      "     |  \n",
+      "     |  >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))\n",
+      "     |  >>> fpr.flags.writeable\n",
+      "     |  False\n",
+      "     |  \n",
+      "     |  Copy-on-write memmap:\n",
+      "     |  \n",
+      "     |  >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))\n",
+      "     |  >>> fpc.flags.writeable\n",
+      "     |  True\n",
+      "     |  \n",
+      "     |  It's possible to assign to copy-on-write array, but values are only\n",
+      "     |  written into the memory copy of the array, and not written to disk:\n",
+      "     |  \n",
+      "     |  >>> fpc\n",
+      "     |  memmap([[  0.,   1.,   2.,   3.],\n",
+      "     |          [  4.,   5.,   6.,   7.],\n",
+      "     |          [  8.,   9.,  10.,  11.]], dtype=float32)\n",
+      "     |  >>> fpc[0,:] = 0\n",
+      "     |  >>> fpc\n",
+      "     |  memmap([[  0.,   0.,   0.,   0.],\n",
+      "     |          [  4.,   5.,   6.,   7.],\n",
+      "     |          [  8.,   9.,  10.,  11.]], dtype=float32)\n",
+      "     |  \n",
+      "     |  File on disk is unchanged:\n",
+      "     |  \n",
+      "     |  >>> fpr\n",
+      "     |  memmap([[  0.,   1.,   2.,   3.],\n",
+      "     |          [  4.,   5.,   6.,   7.],\n",
+      "     |          [  8.,   9.,  10.,  11.]], dtype=float32)\n",
+      "     |  \n",
+      "     |  Offset into a memmap:\n",
+      "     |  \n",
+      "     |  >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)\n",
+      "     |  >>> fpo\n",
+      "     |  memmap([  4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.], dtype=float32)\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      memmap\n",
+      "     |      ndarray\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __array_finalize__(self, obj)\n",
+      "     |      None.\n",
+      "     |  \n",
+      "     |  __array_wrap__(self, arr, context=None)\n",
+      "     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n",
+      "     |  \n",
+      "     |  __getitem__(self, index)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  flush(self)\n",
+      "     |      Write any changes in the array to the file on disk.\n",
+      "     |      \n",
+      "     |      For further information, see `memmap`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      memmap\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(subtype, filename, dtype=<class 'numpy.uint8'>, mode='r+', offset=0, shape=None, order='C')\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __array_priority__ = -100.0\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      a.__array__([dtype], /) -> reference if type unchanged, copy otherwise.\n",
+      "     |      \n",
+      "     |      Returns either a new reference to self if dtype is not given or a new array\n",
+      "     |      of provided data type if dtype is different from the current dtype of the\n",
+      "     |      array.\n",
+      "     |  \n",
+      "     |  __array_function__(...)\n",
+      "     |  \n",
+      "     |  __array_prepare__(...)\n",
+      "     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n",
+      "     |  \n",
+      "     |  __array_ufunc__(...)\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |      a.__copy__()\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.copy` is called on an array. Returns a copy of the array.\n",
+      "     |      \n",
+      "     |      Equivalent to ``a.copy(order='K')``.\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |      a.__deepcopy__(memo, /) -> Deep copy of array.\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.deepcopy` is called on an array.\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      Default object formatter.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __iadd__(self, value, /)\n",
+      "     |      Return self+=value.\n",
+      "     |  \n",
+      "     |  __iand__(self, value, /)\n",
+      "     |      Return self&=value.\n",
+      "     |  \n",
+      "     |  __ifloordiv__(self, value, /)\n",
+      "     |      Return self//=value.\n",
+      "     |  \n",
+      "     |  __ilshift__(self, value, /)\n",
+      "     |      Return self<<=value.\n",
+      "     |  \n",
+      "     |  __imatmul__(self, value, /)\n",
+      "     |      Return self@=value.\n",
+      "     |  \n",
+      "     |  __imod__(self, value, /)\n",
+      "     |      Return self%=value.\n",
+      "     |  \n",
+      "     |  __imul__(self, value, /)\n",
+      "     |      Return self*=value.\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __ior__(self, value, /)\n",
+      "     |      Return self|=value.\n",
+      "     |  \n",
+      "     |  __ipow__(self, value, /)\n",
+      "     |      Return self**=value.\n",
+      "     |  \n",
+      "     |  __irshift__(self, value, /)\n",
+      "     |      Return self>>=value.\n",
+      "     |  \n",
+      "     |  __isub__(self, value, /)\n",
+      "     |      Return self-=value.\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __itruediv__(self, value, /)\n",
+      "     |      Return self/=value.\n",
+      "     |  \n",
+      "     |  __ixor__(self, value, /)\n",
+      "     |      Return self^=value.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __matmul__(self, value, /)\n",
+      "     |      Return self@value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      a.__reduce__()\n",
+      "     |      \n",
+      "     |      For pickling.\n",
+      "     |  \n",
+      "     |  __reduce_ex__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmatmul__(self, value, /)\n",
+      "     |      Return value@self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |      a.__setstate__(state, /)\n",
+      "     |      \n",
+      "     |      For unpickling.\n",
+      "     |      \n",
+      "     |      The `state` argument must be a sequence that contains the following\n",
+      "     |      elements:\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      version : int\n",
+      "     |          optional pickle version. If omitted defaults to 0.\n",
+      "     |      shape : tuple\n",
+      "     |      dtype : data-type\n",
+      "     |      isFortran : bool\n",
+      "     |      rawdata : string or list\n",
+      "     |          a binary string with the data (or a list if 'a' is an object array)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      a.all(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if all elements evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.all` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.all : equivalent function\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      a.any(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if any of the elements of `a` evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.any` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.any : equivalent function\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      a.argmax(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the maximum values along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmax : equivalent function\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      a.argmin(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the minimum values along the given axis of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmin` for detailed documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmin : equivalent function\n",
+      "     |  \n",
+      "     |  argpartition(...)\n",
+      "     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would partition this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argpartition` for full documentation.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argpartition : equivalent function\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      a.argsort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would sort this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argsort` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argsort : equivalent function\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n",
+      "     |      \n",
+      "     |      Copy of the array, cast to a specified type.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          Typecode or data-type to which the array is cast.\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout order of the result.\n",
+      "     |          'C' means C order, 'F' means Fortran order, 'A'\n",
+      "     |          means 'F' order if all the arrays are Fortran contiguous,\n",
+      "     |          'C' order otherwise, and 'K' means as close to the\n",
+      "     |          order the array elements appear in memory as possible.\n",
+      "     |          Default is 'K'.\n",
+      "     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n",
+      "     |          Controls what kind of data casting may occur. Defaults to 'unsafe'\n",
+      "     |          for backwards compatibility.\n",
+      "     |      \n",
+      "     |            * 'no' means the data types should not be cast at all.\n",
+      "     |            * 'equiv' means only byte-order changes are allowed.\n",
+      "     |            * 'safe' means only casts which can preserve values are allowed.\n",
+      "     |            * 'same_kind' means only safe casts or casts within a kind,\n",
+      "     |              like float64 to float32, are allowed.\n",
+      "     |            * 'unsafe' means any data conversions may be done.\n",
+      "     |      subok : bool, optional\n",
+      "     |          If True, then sub-classes will be passed-through (default), otherwise\n",
+      "     |          the returned array will be forced to be a base-class array.\n",
+      "     |      copy : bool, optional\n",
+      "     |          By default, astype always returns a newly allocated array. If this\n",
+      "     |          is set to false, and the `dtype`, `order`, and `subok`\n",
+      "     |          requirements are satisfied, the input array is returned instead\n",
+      "     |          of a copy.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      arr_t : ndarray\n",
+      "     |          Unless `copy` is False and the other conditions for returning the input\n",
+      "     |          array are satisfied (see description for `copy` input parameter), `arr_t`\n",
+      "     |          is a new array of the same shape as the input array, with dtype, order\n",
+      "     |          given by `dtype`, `order`.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      .. versionchanged:: 1.17.0\n",
+      "     |         Casting between a simple data type and a structured one is possible only\n",
+      "     |         for \"unsafe\" casting.  Casting to multiple fields is allowed, but\n",
+      "     |         casting from multiple fields is not.\n",
+      "     |      \n",
+      "     |      .. versionchanged:: 1.9.0\n",
+      "     |         Casting from numeric to string types in 'safe' casting mode requires\n",
+      "     |         that the string dtype length is long enough to store the max\n",
+      "     |         integer/float value converted.\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ComplexWarning\n",
+      "     |          When casting from complex to float or int. To avoid this,\n",
+      "     |          one should use ``a.real.astype(t)``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 2.5])\n",
+      "     |      >>> x\n",
+      "     |      array([1. ,  2. ,  2.5])\n",
+      "     |      \n",
+      "     |      >>> x.astype(int)\n",
+      "     |      array([1, 2, 2])\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      a.byteswap(inplace=False)\n",
+      "     |      \n",
+      "     |      Swap the bytes of the array elements\n",
+      "     |      \n",
+      "     |      Toggle between low-endian and big-endian data representation by\n",
+      "     |      returning a byteswapped array, optionally swapped in-place.\n",
+      "     |      Arrays of byte-strings are not swapped. The real and imaginary\n",
+      "     |      parts of a complex number are swapped individually.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      inplace : bool, optional\n",
+      "     |          If ``True``, swap bytes in-place, default is ``False``.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          The byteswapped array. If `inplace` is ``True``, this is\n",
+      "     |          a view to self.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x1', '0x100', '0x2233']\n",
+      "     |      >>> A.byteswap(inplace=True)\n",
+      "     |      array([  256,     1, 13090], dtype=int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x100', '0x1', '0x3322']\n",
+      "     |      \n",
+      "     |      Arrays of byte-strings are not swapped\n",
+      "     |      \n",
+      "     |      >>> A = np.array([b'ceg', b'fac'])\n",
+      "     |      >>> A.byteswap()\n",
+      "     |      array([b'ceg', b'fac'], dtype='|S3')\n",
+      "     |      \n",
+      "     |      ``A.newbyteorder().byteswap()`` produces an array with the same values\n",
+      "     |        but different representation in memory\n",
+      "     |      \n",
+      "     |      >>> A = np.array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,\n",
+      "     |             0, 0], dtype=uint8)\n",
+      "     |      >>> A.newbyteorder().byteswap(inplace=True)\n",
+      "     |      array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,\n",
+      "     |             0, 3], dtype=uint8)\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      a.choose(choices, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Use an index array to construct a new array from a set of choices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.choose` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.choose : equivalent function\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      a.clip(min=None, max=None, out=None, **kwargs)\n",
+      "     |      \n",
+      "     |      Return an array whose values are limited to ``[min, max]``.\n",
+      "     |      One of max or min must be given.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.clip` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.clip : equivalent function\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      a.compress(condition, axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return selected slices of this array along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.compress` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.compress : equivalent function\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |      a.conj()\n",
+      "     |      \n",
+      "     |      Complex-conjugate all elements.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      a.conjugate()\n",
+      "     |      \n",
+      "     |      Return the complex conjugate, element-wise.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      a.copy(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout of the copy. 'C' means C-order,\n",
+      "     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n",
+      "     |          'C' otherwise. 'K' means match the layout of `a` as closely\n",
+      "     |          as possible. (Note that this function and :func:`numpy.copy` are very\n",
+      "     |          similar, but have different default values for their order=\n",
+      "     |          arguments.)\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      numpy.copy\n",
+      "     |      numpy.copyto\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n",
+      "     |      \n",
+      "     |      >>> y = x.copy()\n",
+      "     |      \n",
+      "     |      >>> x.fill(0)\n",
+      "     |      \n",
+      "     |      >>> x\n",
+      "     |      array([[0, 0, 0],\n",
+      "     |             [0, 0, 0]])\n",
+      "     |      \n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      \n",
+      "     |      >>> y.flags['C_CONTIGUOUS']\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      a.cumprod(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative product of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumprod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumprod : equivalent function\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      a.cumsum(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative sum of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumsum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumsum : equivalent function\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      a.diagonal(offset=0, axis1=0, axis2=1)\n",
+      "     |      \n",
+      "     |      Return specified diagonals. In NumPy 1.9 the returned array is a\n",
+      "     |      read-only view instead of a copy as in previous NumPy versions.  In\n",
+      "     |      a future version the read-only restriction will be removed.\n",
+      "     |      \n",
+      "     |      Refer to :func:`numpy.diagonal` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.diagonal : equivalent function\n",
+      "     |  \n",
+      "     |  dot(...)\n",
+      "     |      a.dot(b, out=None)\n",
+      "     |      \n",
+      "     |      Dot product of two arrays.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.dot` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dot : equivalent function\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.eye(2)\n",
+      "     |      >>> b = np.ones((2, 2)) * 2\n",
+      "     |      >>> a.dot(b)\n",
+      "     |      array([[2.,  2.],\n",
+      "     |             [2.,  2.]])\n",
+      "     |      \n",
+      "     |      This array method can be conveniently chained:\n",
+      "     |      \n",
+      "     |      >>> a.dot(b).dot(b)\n",
+      "     |      array([[8.,  8.],\n",
+      "     |             [8.,  8.]])\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      a.dump(file)\n",
+      "     |      \n",
+      "     |      Dump a pickle of the array to the specified file.\n",
+      "     |      The array can be read back with pickle.load or numpy.load.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      file : str or Path\n",
+      "     |          A string naming the dump file.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      a.dumps()\n",
+      "     |      \n",
+      "     |      Returns the pickle of the array as a string.\n",
+      "     |      pickle.loads or numpy.loads will convert the string back to an array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      a.fill(value)\n",
+      "     |      \n",
+      "     |      Fill the array with a scalar value.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      value : scalar\n",
+      "     |          All elements of `a` will be assigned this value.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([1, 2])\n",
+      "     |      >>> a.fill(0)\n",
+      "     |      >>> a\n",
+      "     |      array([0, 0])\n",
+      "     |      >>> a = np.empty(2)\n",
+      "     |      >>> a.fill(1)\n",
+      "     |      >>> a\n",
+      "     |      array([1.,  1.])\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      a.flatten(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          'C' means to flatten in row-major (C-style) order.\n",
+      "     |          'F' means to flatten in column-major (Fortran-\n",
+      "     |          style) order. 'A' means to flatten in column-major\n",
+      "     |          order if `a` is Fortran *contiguous* in memory,\n",
+      "     |          row-major order otherwise. 'K' means to flatten\n",
+      "     |          `a` in the order the elements occur in memory.\n",
+      "     |          The default is 'C'.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : ndarray\n",
+      "     |          A copy of the input array, flattened to one dimension.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ravel : Return a flattened array.\n",
+      "     |      flat : A 1-D flat iterator over the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,2], [3,4]])\n",
+      "     |      >>> a.flatten()\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |      >>> a.flatten('F')\n",
+      "     |      array([1, 3, 2, 4])\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      a.getfield(dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Returns a field of the given array as a certain type.\n",
+      "     |      \n",
+      "     |      A field is a view of the array data with a given data-type. The values in\n",
+      "     |      the view are determined by the given type and the offset into the current\n",
+      "     |      array in bytes. The offset needs to be such that the view dtype fits in the\n",
+      "     |      array dtype; for example an array of dtype complex128 has 16-byte elements.\n",
+      "     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n",
+      "     |      between 0 and 12 bytes.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          The data type of the view. The dtype size of the view can not be larger\n",
+      "     |          than that of the array itself.\n",
+      "     |      offset : int\n",
+      "     |          Number of bytes to skip before beginning the element view.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.diag([1.+1.j]*2)\n",
+      "     |      >>> x[1, 1] = 2 + 4.j\n",
+      "     |      >>> x\n",
+      "     |      array([[1.+1.j,  0.+0.j],\n",
+      "     |             [0.+0.j,  2.+4.j]])\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  2.]])\n",
+      "     |      \n",
+      "     |      By choosing an offset of 8 bytes we can select the complex part of the\n",
+      "     |      array for our view:\n",
+      "     |      \n",
+      "     |      >>> x.getfield(np.float64, offset=8)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  4.]])\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      a.item(*args)\n",
+      "     |      \n",
+      "     |      Copy an element of an array to a standard Python scalar and return it.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments (variable number and type)\n",
+      "     |      \n",
+      "     |          * none: in this case, the method only works for arrays\n",
+      "     |            with one element (`a.size == 1`), which element is\n",
+      "     |            copied into a standard Python scalar object and returned.\n",
+      "     |      \n",
+      "     |          * int_type: this argument is interpreted as a flat index into\n",
+      "     |            the array, specifying which element to copy and return.\n",
+      "     |      \n",
+      "     |          * tuple of int_types: functions as does a single int_type argument,\n",
+      "     |            except that the argument is interpreted as an nd-index into the\n",
+      "     |            array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      z : Standard Python scalar object\n",
+      "     |          A copy of the specified element of the array as a suitable\n",
+      "     |          Python scalar\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      When the data type of `a` is longdouble or clongdouble, item() returns\n",
+      "     |      a scalar array object because there is no available Python scalar that\n",
+      "     |      would not lose information. Void arrays return a buffer object for item(),\n",
+      "     |      unless fields are defined, in which case a tuple is returned.\n",
+      "     |      \n",
+      "     |      `item` is very similar to a[args], except, instead of an array scalar,\n",
+      "     |      a standard Python scalar is returned. This can be useful for speeding up\n",
+      "     |      access to elements of the array and doing arithmetic on elements of the\n",
+      "     |      array using Python's optimized math.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.item(3)\n",
+      "     |      1\n",
+      "     |      >>> x.item(7)\n",
+      "     |      0\n",
+      "     |      >>> x.item((0, 1))\n",
+      "     |      2\n",
+      "     |      >>> x.item((2, 2))\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      a.itemset(*args)\n",
+      "     |      \n",
+      "     |      Insert scalar into an array (scalar is cast to array's dtype, if possible)\n",
+      "     |      \n",
+      "     |      There must be at least 1 argument, and define the last argument\n",
+      "     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster\n",
+      "     |      than ``a[args] = item``.  The item should be a scalar value and `args`\n",
+      "     |      must select a single item in the array `a`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments\n",
+      "     |          If one argument: a scalar, only used in case `a` is of size 1.\n",
+      "     |          If two arguments: the last argument is the value to be set\n",
+      "     |          and must be a scalar, the first argument specifies a single array\n",
+      "     |          element location. It is either an int or a tuple.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Compared to indexing syntax, `itemset` provides some speed increase\n",
+      "     |      for placing a scalar into a particular location in an `ndarray`,\n",
+      "     |      if you must do this.  However, generally this is discouraged:\n",
+      "     |      among other problems, it complicates the appearance of the code.\n",
+      "     |      Also, when using `itemset` (and `item`) inside a loop, be sure\n",
+      "     |      to assign the methods to a local variable to avoid the attribute\n",
+      "     |      look-up at each loop iteration.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.itemset(4, 0)\n",
+      "     |      >>> x.itemset((2, 2), 9)\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 0, 6],\n",
+      "     |             [1, 0, 9]])\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      a.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the maximum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amax : equivalent function\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      a.mean(axis=None, dtype=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the average of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.mean` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.mean : equivalent function\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      a.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the minimum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amin` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amin : equivalent function\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      arr.newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return the array with the same data viewed with a different byte order.\n",
+      "     |      \n",
+      "     |      Equivalent to::\n",
+      "     |      \n",
+      "     |          arr.view(arr.dtype.newbytorder(new_order))\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the array data\n",
+      "     |      type.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : string, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          below. `new_order` codes can be any of:\n",
+      "     |      \n",
+      "     |          * 'S' - swap dtype from current to opposite endian\n",
+      "     |          * {'<', 'L'} - little endian\n",
+      "     |          * {'>', 'B'} - big endian\n",
+      "     |          * {'=', 'N'} - native order\n",
+      "     |          * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |          The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_arr : array\n",
+      "     |          New array object with the dtype reflecting given change to the\n",
+      "     |          byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      a.nonzero()\n",
+      "     |      \n",
+      "     |      Return the indices of the elements that are non-zero.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.nonzero` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.nonzero : equivalent function\n",
+      "     |  \n",
+      "     |  partition(...)\n",
+      "     |      a.partition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Rearranges the elements in the array in such a way that the value of the\n",
+      "     |      element in kth position is in the position it would be in a sorted array.\n",
+      "     |      All elements smaller than the kth element are moved before this element and\n",
+      "     |      all equal or greater are moved behind it. The ordering of the elements in\n",
+      "     |      the two partitions is undefined.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      kth : int or sequence of ints\n",
+      "     |          Element index to partition by. The kth element value will be in its\n",
+      "     |          final sorted position and all smaller elements will be moved before it\n",
+      "     |          and all equal or greater elements behind it.\n",
+      "     |          The order of all elements in the partitions is undefined.\n",
+      "     |          If provided with a sequence of kth it will partition all elements\n",
+      "     |          indexed by kth of them into their sorted position at once.\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'introselect'}, optional\n",
+      "     |          Selection algorithm. Default is 'introselect'.\n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc. A single field can\n",
+      "     |          be specified as a string, and not all fields need to be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.partition : Return a parititioned copy of an array.\n",
+      "     |      argpartition : Indirect partition.\n",
+      "     |      sort : Full sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See ``np.partition`` for notes on the different algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([3, 4, 2, 1])\n",
+      "     |      >>> a.partition(3)\n",
+      "     |      >>> a\n",
+      "     |      array([2, 1, 3, 4])\n",
+      "     |      \n",
+      "     |      >>> a.partition((1, 3))\n",
+      "     |      >>> a\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)\n",
+      "     |      \n",
+      "     |      Return the product of the array elements over the given axis\n",
+      "     |      \n",
+      "     |      Refer to `numpy.prod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.prod : equivalent function\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      a.ptp(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Peak to peak (maximum - minimum) value along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ptp` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ptp : equivalent function\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      a.put(indices, values, mode='raise')\n",
+      "     |      \n",
+      "     |      Set ``a.flat[n] = values[n]`` for all `n` in indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.put` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.put : equivalent function\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      a.ravel([order])\n",
+      "     |      \n",
+      "     |      Return a flattened array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ravel` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ravel : equivalent function\n",
+      "     |      \n",
+      "     |      ndarray.flat : a flat iterator on the array.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      a.repeat(repeats, axis=None)\n",
+      "     |      \n",
+      "     |      Repeat elements of an array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.repeat` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.repeat : equivalent function\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      a.reshape(shape, order='C')\n",
+      "     |      \n",
+      "     |      Returns an array containing the same data with a new shape.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.reshape` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : equivalent function\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Unlike the free function `numpy.reshape`, this method on `ndarray` allows\n",
+      "     |      the elements of the shape parameter to be passed in as separate arguments.\n",
+      "     |      For example, ``a.reshape(10, 11)`` is equivalent to\n",
+      "     |      ``a.reshape((10, 11))``.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      a.resize(new_shape, refcheck=True)\n",
+      "     |      \n",
+      "     |      Change shape and size of array in-place.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_shape : tuple of ints, or `n` ints\n",
+      "     |          Shape of resized array.\n",
+      "     |      refcheck : bool, optional\n",
+      "     |          If False, reference count will not be checked. Default is True.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ValueError\n",
+      "     |          If `a` does not own its own data or references or views to it exist,\n",
+      "     |          and the data memory must be changed.\n",
+      "     |          PyPy only: will always raise if the data memory must be changed, since\n",
+      "     |          there is no reliable way to determine if references or views to it\n",
+      "     |          exist.\n",
+      "     |      \n",
+      "     |      SystemError\n",
+      "     |          If the `order` keyword argument is specified. This behaviour is a\n",
+      "     |          bug in NumPy.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      resize : Return a new array with the specified shape.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This reallocates space for the data area if necessary.\n",
+      "     |      \n",
+      "     |      Only contiguous arrays (data elements consecutive in memory) can be\n",
+      "     |      resized.\n",
+      "     |      \n",
+      "     |      The purpose of the reference count check is to make sure you\n",
+      "     |      do not use this array as a buffer for another Python object and then\n",
+      "     |      reallocate the memory. However, reference counts can increase in\n",
+      "     |      other ways so if you are sure that you have not shared the memory\n",
+      "     |      for this array with another Python object, then you may safely set\n",
+      "     |      `refcheck` to False.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      Shrinking an array: array is flattened (in the order that the data are\n",
+      "     |      stored in memory), resized, and reshaped:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='C')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [1]])\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='F')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [2]])\n",
+      "     |      \n",
+      "     |      Enlarging an array: as above, but missing entries are filled with zeros:\n",
+      "     |      \n",
+      "     |      >>> b = np.array([[0, 1], [2, 3]])\n",
+      "     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n",
+      "     |      >>> b\n",
+      "     |      array([[0, 1, 2],\n",
+      "     |             [3, 0, 0]])\n",
+      "     |      \n",
+      "     |      Referencing an array prevents resizing...\n",
+      "     |      \n",
+      "     |      >>> c = a\n",
+      "     |      >>> a.resize((1, 1))\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |      ...\n",
+      "     |      ValueError: cannot resize an array that references or is referenced ...\n",
+      "     |      \n",
+      "     |      Unless `refcheck` is False:\n",
+      "     |      \n",
+      "     |      >>> a.resize((1, 1), refcheck=False)\n",
+      "     |      >>> a\n",
+      "     |      array([[0]])\n",
+      "     |      >>> c\n",
+      "     |      array([[0]])\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      a.round(decimals=0, out=None)\n",
+      "     |      \n",
+      "     |      Return `a` with each element rounded to the given number of decimals.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.around` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.around : equivalent function\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      a.searchsorted(v, side='left', sorter=None)\n",
+      "     |      \n",
+      "     |      Find indices where elements of v should be inserted in a to maintain order.\n",
+      "     |      \n",
+      "     |      For full documentation, see `numpy.searchsorted`\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.searchsorted : equivalent function\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      a.setfield(val, dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Put a value into a specified place in a field defined by a data-type.\n",
+      "     |      \n",
+      "     |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n",
+      "     |      bytes into the field.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      val : object\n",
+      "     |          Value to be placed in field.\n",
+      "     |      dtype : dtype object\n",
+      "     |          Data-type of the field in which to place `val`.\n",
+      "     |      offset : int, optional\n",
+      "     |          The number of bytes into the field at which to place `val`.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      getfield\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.eye(3)\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |      >>> x.setfield(3, np.int32)\n",
+      "     |      >>> x.getfield(np.int32)\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3],\n",
+      "     |             [3, 3, 3]], dtype=int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.0e+000, 1.5e-323, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.0e+000, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.5e-323, 1.0e+000]])\n",
+      "     |      >>> x.setfield(np.eye(3), np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      a.setflags(write=None, align=None, uic=None)\n",
+      "     |      \n",
+      "     |      Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),\n",
+      "     |      respectively.\n",
+      "     |      \n",
+      "     |      These Boolean-valued flags affect how numpy interprets the memory\n",
+      "     |      area used by `a` (see Notes below). The ALIGNED flag can only\n",
+      "     |      be set to True if the data is actually aligned according to the type.\n",
+      "     |      The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set\n",
+      "     |      to True. The flag WRITEABLE can only be set to True if the array owns its\n",
+      "     |      own memory, or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |      interface, or is a string. (The exception for string is made so that\n",
+      "     |      unpickling can be done without copying memory.)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      write : bool, optional\n",
+      "     |          Describes whether or not `a` can be written to.\n",
+      "     |      align : bool, optional\n",
+      "     |          Describes whether or not `a` is aligned properly for its type.\n",
+      "     |      uic : bool, optional\n",
+      "     |          Describes whether or not `a` is a copy of another \"base\" array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Array flags provide information about how the memory area used\n",
+      "     |      for the array is to be interpreted. There are 7 Boolean flags\n",
+      "     |      in use, only four of which can be changed by the user:\n",
+      "     |      WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n",
+      "     |      \n",
+      "     |      WRITEABLE (W) the data area can be written to;\n",
+      "     |      \n",
+      "     |      ALIGNED (A) the data and strides are aligned appropriately for the hardware\n",
+      "     |      (as determined by the compiler);\n",
+      "     |      \n",
+      "     |      UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;\n",
+      "     |      \n",
+      "     |      WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced\n",
+      "     |      by .base). When the C-API function PyArray_ResolveWritebackIfCopy is\n",
+      "     |      called, the base array will be updated with the contents of this array.\n",
+      "     |      \n",
+      "     |      All flags can be accessed using the single (upper case) letter as well\n",
+      "     |      as the full name.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.array([[3, 1, 7],\n",
+      "     |      ...               [2, 0, 0],\n",
+      "     |      ...               [8, 5, 9]])\n",
+      "     |      >>> y\n",
+      "     |      array([[3, 1, 7],\n",
+      "     |             [2, 0, 0],\n",
+      "     |             [8, 5, 9]])\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : True\n",
+      "     |        ALIGNED : True\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(write=0, align=0)\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : False\n",
+      "     |        ALIGNED : False\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(uic=1)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: cannot set WRITEBACKIFCOPY flag to True\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      a.sort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Sort an array in-place. Refer to `numpy.sort` for full documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n",
+      "     |          Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n",
+      "     |          and 'mergesort' use timsort under the covers and, in general, the\n",
+      "     |          actual implementation will vary with datatype. The 'mergesort' option\n",
+      "     |          is retained for backwards compatibility.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.15.0.\n",
+      "     |             The 'stable' option was added.\n",
+      "     |      \n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc.  A single field can\n",
+      "     |          be specified as a string, and not all fields need be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sort : Return a sorted copy of an array.\n",
+      "     |      numpy.argsort : Indirect sort.\n",
+      "     |      numpy.lexsort : Indirect stable sort on multiple keys.\n",
+      "     |      numpy.searchsorted : Find elements in sorted array.\n",
+      "     |      numpy.partition: Partial sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See `numpy.sort` for notes on the different sorting algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,4], [3,1]])\n",
+      "     |      >>> a.sort(axis=1)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 4],\n",
+      "     |             [1, 3]])\n",
+      "     |      >>> a.sort(axis=0)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 3],\n",
+      "     |             [1, 4]])\n",
+      "     |      \n",
+      "     |      Use the `order` keyword to specify a field to use when sorting a\n",
+      "     |      structured array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n",
+      "     |      >>> a.sort(order='y')\n",
+      "     |      >>> a\n",
+      "     |      array([(b'c', 1), (b'a', 2)],\n",
+      "     |            dtype=[('x', 'S1'), ('y', '<i8')])\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      a.squeeze(axis=None)\n",
+      "     |      \n",
+      "     |      Remove single-dimensional entries from the shape of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.squeeze` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.squeeze : equivalent function\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the standard deviation of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.std` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.std : equivalent function\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)\n",
+      "     |      \n",
+      "     |      Return the sum of the array elements over the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.sum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sum : equivalent function\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      a.swapaxes(axis1, axis2)\n",
+      "     |      \n",
+      "     |      Return a view of the array with `axis1` and `axis2` interchanged.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.swapaxes` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.swapaxes : equivalent function\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      a.take(indices, axis=None, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Return an array formed from the elements of `a` at the given indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.take` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.take : equivalent function\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |      a.tobytes(order='C')\n",
+      "     |      \n",
+      "     |      Construct Python bytes containing the raw data bytes in the array.\n",
+      "     |      \n",
+      "     |      Constructs Python bytes showing a copy of the raw contents of\n",
+      "     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',\n",
+      "     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n",
+      "     |      unless the F_CONTIGUOUS flag in the array is set, in which case it\n",
+      "     |      means 'Fortran' order.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.9.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', None}, optional\n",
+      "     |          Order of the data for multidimensional arrays:\n",
+      "     |          C, Fortran, or the same as for the original array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      s : bytes\n",
+      "     |          Python bytes exhibiting a copy of `a`'s raw data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype='<u2')\n",
+      "     |      >>> x.tobytes()\n",
+      "     |      b'\\x00\\x00\\x01\\x00\\x02\\x00\\x03\\x00'\n",
+      "     |      >>> x.tobytes('C') == x.tobytes()\n",
+      "     |      True\n",
+      "     |      >>> x.tobytes('F')\n",
+      "     |      b'\\x00\\x00\\x02\\x00\\x01\\x00\\x03\\x00'\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      a.tofile(fid, sep=\"\", format=\"%s\")\n",
+      "     |      \n",
+      "     |      Write array to a file as text or binary (default).\n",
+      "     |      \n",
+      "     |      Data is always written in 'C' order, independent of the order of `a`.\n",
+      "     |      The data produced by this method can be recovered using the function\n",
+      "     |      fromfile().\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      fid : file or str or Path\n",
+      "     |          An open file object, or a string containing a filename.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |      \n",
+      "     |      sep : str\n",
+      "     |          Separator between array items for text output.\n",
+      "     |          If \"\" (empty), a binary file is written, equivalent to\n",
+      "     |          ``file.write(a.tobytes())``.\n",
+      "     |      format : str\n",
+      "     |          Format string for text file output.\n",
+      "     |          Each entry in the array is formatted to text by first converting\n",
+      "     |          it to the closest Python type, and then using \"format\" % item.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is a convenience function for quick storage of array data.\n",
+      "     |      Information on endianness and precision is lost, so this method is not a\n",
+      "     |      good choice for files intended to archive data or transport data between\n",
+      "     |      machines with different endianness. Some of these problems can be overcome\n",
+      "     |      by outputting the data as text files, at the expense of speed and file\n",
+      "     |      size.\n",
+      "     |      \n",
+      "     |      When fid is a file object, array contents are directly written to the\n",
+      "     |      file, bypassing the file object's ``write`` method. As a result, tofile\n",
+      "     |      cannot be used with files objects supporting compression (e.g., GzipFile)\n",
+      "     |      or file-like objects that do not support ``fileno()`` (e.g., BytesIO).\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      a.tolist()\n",
+      "     |      \n",
+      "     |      Return the array as an ``a.ndim``-levels deep nested list of Python scalars.\n",
+      "     |      \n",
+      "     |      Return a copy of the array data as a (nested) Python list.\n",
+      "     |      Data items are converted to the nearest compatible builtin Python type, via\n",
+      "     |      the `~numpy.ndarray.item` function.\n",
+      "     |      \n",
+      "     |      If ``a.ndim`` is 0, then since the depth of the nested list is 0, it will\n",
+      "     |      not be a list at all, but a simple Python scalar.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      none\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : object, or list of object, or list of list of object, or ...\n",
+      "     |          The possibly nested list of array elements.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The array may be recreated via ``a = np.array(a.tolist())``, although this\n",
+      "     |      may sometimes lose precision.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      For a 1D array, ``a.tolist()`` is almost the same as ``list(a)``,\n",
+      "     |      except that ``tolist`` changes numpy scalars to Python scalars:\n",
+      "     |      \n",
+      "     |      >>> a = np.uint32([1, 2])\n",
+      "     |      >>> a_list = list(a)\n",
+      "     |      >>> a_list\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_list[0])\n",
+      "     |      <class 'numpy.uint32'>\n",
+      "     |      >>> a_tolist = a.tolist()\n",
+      "     |      >>> a_tolist\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_tolist[0])\n",
+      "     |      <class 'int'>\n",
+      "     |      \n",
+      "     |      Additionally, for a 2D array, ``tolist`` applies recursively:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> list(a)\n",
+      "     |      [array([1, 2]), array([3, 4])]\n",
+      "     |      >>> a.tolist()\n",
+      "     |      [[1, 2], [3, 4]]\n",
+      "     |      \n",
+      "     |      The base case for this recursion is a 0D array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array(1)\n",
+      "     |      >>> list(a)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        ...\n",
+      "     |      TypeError: iteration over a 0-d array\n",
+      "     |      >>> a.tolist()\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      a.tostring(order='C')\n",
+      "     |      \n",
+      "     |      A compatibility alias for `tobytes`, with exactly the same behavior.\n",
+      "     |      \n",
+      "     |      Despite its name, it returns `bytes` not `str`\\ s.\n",
+      "     |      \n",
+      "     |      .. deprecated:: 1.19.0\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the sum along diagonals of the array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.trace` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.trace : equivalent function\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      a.transpose(*axes)\n",
+      "     |      \n",
+      "     |      Returns a view of the array with axes transposed.\n",
+      "     |      \n",
+      "     |      For a 1-D array this has no effect, as a transposed vector is simply the\n",
+      "     |      same vector. To convert a 1-D array into a 2D column vector, an additional\n",
+      "     |      dimension must be added. `np.atleast2d(a).T` achieves this, as does\n",
+      "     |      `a[:, np.newaxis]`.\n",
+      "     |      For a 2-D array, this is a standard matrix transpose.\n",
+      "     |      For an n-D array, if axes are given, their order indicates how the\n",
+      "     |      axes are permuted (see Examples). If axes are not provided and\n",
+      "     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n",
+      "     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axes : None, tuple of ints, or `n` ints\n",
+      "     |      \n",
+      "     |       * None or no argument: reverses the order of the axes.\n",
+      "     |      \n",
+      "     |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n",
+      "     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n",
+      "     |      \n",
+      "     |       * `n` ints: same as an n-tuple of the same ints (this form is\n",
+      "     |         intended simply as a \"convenience\" alternative to the tuple form)\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          View of `a`, with axes suitably permuted.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ndarray.T : Array property returning the array transposed.\n",
+      "     |      ndarray.reshape : Give a new shape to an array without changing its data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]])\n",
+      "     |      >>> a.transpose()\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose((1, 0))\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose(1, 0)\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the variance of the array elements, along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.var` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.var : equivalent function\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      a.view([dtype][, type])\n",
+      "     |      \n",
+      "     |      New view of array with the same data.\n",
+      "     |      \n",
+      "     |      .. note::\n",
+      "     |          Passing None for ``dtype`` is different from omitting the parameter,\n",
+      "     |          since the former invokes ``dtype(None)`` which is an alias for\n",
+      "     |          ``dtype('float_')``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : data-type or ndarray sub-class, optional\n",
+      "     |          Data-type descriptor of the returned view, e.g., float32 or int16.\n",
+      "     |          Omitting it results in the view having the same data-type as `a`.\n",
+      "     |          This argument can also be specified as an ndarray sub-class, which\n",
+      "     |          then specifies the type of the returned object (this is equivalent to\n",
+      "     |          setting the ``type`` parameter).\n",
+      "     |      type : Python type, optional\n",
+      "     |          Type of the returned view, e.g., ndarray or matrix.  Again, omission\n",
+      "     |          of the parameter results in type preservation.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      ``a.view()`` is used two different ways:\n",
+      "     |      \n",
+      "     |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n",
+      "     |      of the array's memory with a different data-type.  This can cause a\n",
+      "     |      reinterpretation of the bytes of memory.\n",
+      "     |      \n",
+      "     |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n",
+      "     |      returns an instance of `ndarray_subclass` that looks at the same array\n",
+      "     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the\n",
+      "     |      memory.\n",
+      "     |      \n",
+      "     |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n",
+      "     |      bytes per entry than the previous dtype (for example, converting a\n",
+      "     |      regular array to a structured array), then the behavior of the view\n",
+      "     |      cannot be predicted just from the superficial appearance of ``a`` (shown\n",
+      "     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in\n",
+      "     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n",
+      "     |      defined as a slice or transpose, etc., the view may give different\n",
+      "     |      results.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      \n",
+      "     |      Viewing array data using a different type and dtype:\n",
+      "     |      \n",
+      "     |      >>> y = x.view(dtype=np.int16, type=np.matrix)\n",
+      "     |      >>> y\n",
+      "     |      matrix([[513]], dtype=int16)\n",
+      "     |      >>> print(type(y))\n",
+      "     |      <class 'numpy.matrix'>\n",
+      "     |      \n",
+      "     |      Creating a view on a structured array so it can be used in calculations\n",
+      "     |      \n",
+      "     |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n",
+      "     |      >>> xv\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]], dtype=int8)\n",
+      "     |      >>> xv.mean(0)\n",
+      "     |      array([2.,  3.])\n",
+      "     |      \n",
+      "     |      Making changes to the view changes the underlying array\n",
+      "     |      \n",
+      "     |      >>> xv[0,1] = 20\n",
+      "     |      >>> x\n",
+      "     |      array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])\n",
+      "     |      \n",
+      "     |      Using a view to convert an array to a recarray:\n",
+      "     |      \n",
+      "     |      >>> z = x.view(np.recarray)\n",
+      "     |      >>> z.a\n",
+      "     |      array([1, 3], dtype=int8)\n",
+      "     |      \n",
+      "     |      Views share data:\n",
+      "     |      \n",
+      "     |      >>> x[0] = (9, 10)\n",
+      "     |      >>> z[0]\n",
+      "     |      (9, 10)\n",
+      "     |      \n",
+      "     |      Views that change the dtype size (bytes per entry) should normally be\n",
+      "     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n",
+      "     |      >>> y = x[:, 0:2]\n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2],\n",
+      "     |             [4, 5]], dtype=int16)\n",
+      "     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |          ...\n",
+      "     |      ValueError: To change to a dtype of a different size, the array must be C-contiguous\n",
+      "     |      >>> z = y.copy()\n",
+      "     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      array([[(1, 2)],\n",
+      "     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from ndarray:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      The transposed array.\n",
+      "     |      \n",
+      "     |      Same as ``self.transpose()``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1.,2.],[3.,4.]])\n",
+      "     |      >>> x\n",
+      "     |      array([[ 1.,  2.],\n",
+      "     |             [ 3.,  4.]])\n",
+      "     |      >>> x.T\n",
+      "     |      array([[ 1.,  3.],\n",
+      "     |             [ 2.,  4.]])\n",
+      "     |      >>> x = np.array([1.,2.,3.,4.])\n",
+      "     |      >>> x\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      >>> x.T\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: C-struct side.\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      Base object if memory is from some other object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      The base of an array that owns its memory is None:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([1,2,3,4])\n",
+      "     |      >>> x.base is None\n",
+      "     |      True\n",
+      "     |      \n",
+      "     |      Slicing creates a view, whose memory is shared with x:\n",
+      "     |      \n",
+      "     |      >>> y = x[2:]\n",
+      "     |      >>> y.base is x\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  ctypes\n",
+      "     |      An object to simplify the interaction of the array with the ctypes\n",
+      "     |      module.\n",
+      "     |      \n",
+      "     |      This attribute creates an object that makes it easier to use arrays\n",
+      "     |      when calling shared libraries with the ctypes module. The returned\n",
+      "     |      object has, among others, data, shape, and strides attributes (see\n",
+      "     |      Notes below) which themselves return ctypes objects that can be used\n",
+      "     |      as arguments to a shared library.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      c : Python object\n",
+      "     |          Possessing attributes data, shape, strides, etc.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ctypeslib\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Below are the public attributes of this object which were documented\n",
+      "     |      in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n",
+      "     |      as well as documented private attributes):\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.data\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.shape\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.strides\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.data_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.shape_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.strides_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      If the ctypes module is not available, then the ctypes attribute\n",
+      "     |      of array objects still returns something useful, but ctypes objects\n",
+      "     |      are not returned and errors may be raised instead. In particular,\n",
+      "     |      the object will still have the ``as_parameter`` attribute which will\n",
+      "     |      return an integer equal to the data attribute.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> import ctypes\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]], dtype=int32)\n",
+      "     |      >>> x.ctypes.data\n",
+      "     |      31962608 # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))\n",
+      "     |      <__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents\n",
+      "     |      c_uint(0)\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents\n",
+      "     |      c_ulong(4294967296)\n",
+      "     |      >>> x.ctypes.shape\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary\n",
+      "     |      >>> x.ctypes.strides\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      Python buffer object pointing to the start of the array's data.\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      Data-type of the array's elements.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      d : numpy dtype object\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dtype\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]])\n",
+      "     |      >>> x.dtype\n",
+      "     |      dtype('int32')\n",
+      "     |      >>> type(x.dtype)\n",
+      "     |      <type 'numpy.dtype'>\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      Information about the memory layout of the array.\n",
+      "     |      \n",
+      "     |      Attributes\n",
+      "     |      ----------\n",
+      "     |      C_CONTIGUOUS (C)\n",
+      "     |          The data is in a single, C-style contiguous segment.\n",
+      "     |      F_CONTIGUOUS (F)\n",
+      "     |          The data is in a single, Fortran-style contiguous segment.\n",
+      "     |      OWNDATA (O)\n",
+      "     |          The array owns the memory it uses or borrows it from another object.\n",
+      "     |      WRITEABLE (W)\n",
+      "     |          The data area can be written to.  Setting this to False locks\n",
+      "     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE\n",
+      "     |          from its base array at creation time, but a view of a writeable\n",
+      "     |          array may be subsequently locked while the base array remains writeable.\n",
+      "     |          (The opposite is not true, in that a view of a locked array may not\n",
+      "     |          be made writeable.  However, currently, locking a base object does not\n",
+      "     |          lock any views that already reference it, so under that circumstance it\n",
+      "     |          is possible to alter the contents of a locked array via a previously\n",
+      "     |          created writeable view onto it.)  Attempting to change a non-writeable\n",
+      "     |          array raises a RuntimeError exception.\n",
+      "     |      ALIGNED (A)\n",
+      "     |          The data and all elements are aligned appropriately for the hardware.\n",
+      "     |      WRITEBACKIFCOPY (X)\n",
+      "     |          This array is a copy of some other array. The C-API function\n",
+      "     |          PyArray_ResolveWritebackIfCopy must be called before deallocating\n",
+      "     |          to the base array will be updated with the contents of this array.\n",
+      "     |      UPDATEIFCOPY (U)\n",
+      "     |          (Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array.\n",
+      "     |          When this array is\n",
+      "     |          deallocated, the base array will be updated with the contents of\n",
+      "     |          this array.\n",
+      "     |      FNC\n",
+      "     |          F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      FORC\n",
+      "     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n",
+      "     |      BEHAVED (B)\n",
+      "     |          ALIGNED and WRITEABLE.\n",
+      "     |      CARRAY (CA)\n",
+      "     |          BEHAVED and C_CONTIGUOUS.\n",
+      "     |      FARRAY (FA)\n",
+      "     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n",
+      "     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n",
+      "     |      names are only supported in dictionary access.\n",
+      "     |      \n",
+      "     |      Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be\n",
+      "     |      changed by the user, via direct assignment to the attribute or dictionary\n",
+      "     |      entry, or by calling `ndarray.setflags`.\n",
+      "     |      \n",
+      "     |      The array flags cannot be set arbitrarily:\n",
+      "     |      \n",
+      "     |      - UPDATEIFCOPY can only be set ``False``.\n",
+      "     |      - WRITEBACKIFCOPY can only be set ``False``.\n",
+      "     |      - ALIGNED can only be set ``True`` if the data is truly aligned.\n",
+      "     |      - WRITEABLE can only be set ``True`` if the array owns its own memory\n",
+      "     |        or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |        interface or is a string.\n",
+      "     |      \n",
+      "     |      Arrays can be both C-style and Fortran-style contiguous simultaneously.\n",
+      "     |      This is clear for 1-dimensional arrays, but can also be true for higher\n",
+      "     |      dimensional arrays.\n",
+      "     |      \n",
+      "     |      Even for contiguous arrays a stride for a given dimension\n",
+      "     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n",
+      "     |      or the array has no elements.\n",
+      "     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n",
+      "     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n",
+      "     |      Fortran-style contiguous arrays is true.\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      A 1-D iterator over the array.\n",
+      "     |      \n",
+      "     |      This is a `numpy.flatiter` instance, which acts similarly to, but is not\n",
+      "     |      a subclass of, Python's built-in iterator object.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      flatten : Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      flatiter\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(1, 7).reshape(2, 3)\n",
+      "     |      >>> x\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      >>> x.flat[3]\n",
+      "     |      4\n",
+      "     |      >>> x.T\n",
+      "     |      array([[1, 4],\n",
+      "     |             [2, 5],\n",
+      "     |             [3, 6]])\n",
+      "     |      >>> x.T.flat[3]\n",
+      "     |      5\n",
+      "     |      >>> type(x.flat)\n",
+      "     |      <class 'numpy.flatiter'>\n",
+      "     |      \n",
+      "     |      An assignment example:\n",
+      "     |      \n",
+      "     |      >>> x.flat = 3; x\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3]])\n",
+      "     |      >>> x.flat[[1,4]] = 1; x\n",
+      "     |      array([[3, 1, 3],\n",
+      "     |             [3, 1, 3]])\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      The imaginary part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.imag\n",
+      "     |      array([ 0.        ,  0.70710678])\n",
+      "     |      >>> x.imag.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      Length of one array element in bytes.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.float64)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      8\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.complex128)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      16\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      Total bytes consumed by the elements of the array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Does not include memory consumed by non-element attributes of the\n",
+      "     |      array object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)\n",
+      "     |      >>> x.nbytes\n",
+      "     |      480\n",
+      "     |      >>> np.prod(x.shape) * x.itemsize\n",
+      "     |      480\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      Number of array dimensions.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> x.ndim\n",
+      "     |      1\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.ndim\n",
+      "     |      3\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      The real part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.real\n",
+      "     |      array([ 1.        ,  0.70710678])\n",
+      "     |      >>> x.real.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.real : equivalent function\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      Tuple of array dimensions.\n",
+      "     |      \n",
+      "     |      The shape property is usually used to get the current shape of an array,\n",
+      "     |      but may also be used to reshape the array in-place by assigning a tuple of\n",
+      "     |      array dimensions to it.  As with `numpy.reshape`, one of the new shape\n",
+      "     |      dimensions can be -1, in which case its value is inferred from the size of\n",
+      "     |      the array and the remaining dimensions. Reshaping an array in-place will\n",
+      "     |      fail if a copy is required.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3, 4])\n",
+      "     |      >>> x.shape\n",
+      "     |      (4,)\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.shape\n",
+      "     |      (2, 3, 4)\n",
+      "     |      >>> y.shape = (3, 8)\n",
+      "     |      >>> y\n",
+      "     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
+      "     |      >>> y.shape = (3, 6)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: total size of new array must be unchanged\n",
+      "     |      >>> np.zeros((4,2))[::2].shape = (-1,)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      AttributeError: Incompatible shape for in-place modification. Use\n",
+      "     |      `.reshape()` to make a copy with the desired shape.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : similar function\n",
+      "     |      ndarray.reshape : similar method\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      Number of elements in the array.\n",
+      "     |      \n",
+      "     |      Equal to ``np.prod(a.shape)``, i.e., the product of the array's\n",
+      "     |      dimensions.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      `a.size` returns a standard arbitrary precision Python integer. This\n",
+      "     |      may not be the case with other methods of obtaining the same value\n",
+      "     |      (like the suggested ``np.prod(a.shape)``, which returns an instance\n",
+      "     |      of ``np.int_``), and may be relevant if the value is used further in\n",
+      "     |      calculations that may overflow a fixed size integer type.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n",
+      "     |      >>> x.size\n",
+      "     |      30\n",
+      "     |      >>> np.prod(x.shape)\n",
+      "     |      30\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      Tuple of bytes to step in each dimension when traversing an array.\n",
+      "     |      \n",
+      "     |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n",
+      "     |      is::\n",
+      "     |      \n",
+      "     |          offset = sum(np.array(i) * a.strides)\n",
+      "     |      \n",
+      "     |      A more detailed explanation of strides can be found in the\n",
+      "     |      \"ndarray.rst\" file in the NumPy reference guide.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Imagine an array of 32-bit integers (each 4 bytes)::\n",
+      "     |      \n",
+      "     |        x = np.array([[0, 1, 2, 3, 4],\n",
+      "     |                      [5, 6, 7, 8, 9]], dtype=np.int32)\n",
+      "     |      \n",
+      "     |      This array is stored in memory as 40 bytes, one after the other\n",
+      "     |      (known as a contiguous block of memory).  The strides of an array tell\n",
+      "     |      us how many bytes we have to skip in memory to move to the next position\n",
+      "     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to\n",
+      "     |      move to the next column, but 20 bytes (5 values) to get to the same\n",
+      "     |      position in the next row.  As such, the strides for the array `x` will be\n",
+      "     |      ``(20, 4)``.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.lib.stride_tricks.as_strided\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n",
+      "     |      >>> y\n",
+      "     |      array([[[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]],\n",
+      "     |             [[12, 13, 14, 15],\n",
+      "     |              [16, 17, 18, 19],\n",
+      "     |              [20, 21, 22, 23]]])\n",
+      "     |      >>> y.strides\n",
+      "     |      (48, 16, 4)\n",
+      "     |      >>> y[1,1,1]\n",
+      "     |      17\n",
+      "     |      >>> offset=sum(y.strides * np.array((1,1,1)))\n",
+      "     |      >>> offset/y.itemsize\n",
+      "     |      17\n",
+      "     |      \n",
+      "     |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n",
+      "     |      >>> x.strides\n",
+      "     |      (32, 4, 224, 1344)\n",
+      "     |      >>> i = np.array([3,5,2,2])\n",
+      "     |      >>> offset = sum(i * x.strides)\n",
+      "     |      >>> x[3,5,2,2]\n",
+      "     |      813\n",
+      "     |      >>> offset / x.itemsize\n",
+      "     |      813\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class ndarray(builtins.object)\n",
+      "     |  ndarray(shape, dtype=float, buffer=None, offset=0,\n",
+      "     |          strides=None, order=None)\n",
+      "     |  \n",
+      "     |  An array object represents a multidimensional, homogeneous array\n",
+      "     |  of fixed-size items.  An associated data-type object describes the\n",
+      "     |  format of each element in the array (its byte-order, how many bytes it\n",
+      "     |  occupies in memory, whether it is an integer, a floating point number,\n",
+      "     |  or something else, etc.)\n",
+      "     |  \n",
+      "     |  Arrays should be constructed using `array`, `zeros` or `empty` (refer\n",
+      "     |  to the See Also section below).  The parameters given here refer to\n",
+      "     |  a low-level method (`ndarray(...)`) for instantiating an array.\n",
+      "     |  \n",
+      "     |  For more information, refer to the `numpy` module and examine the\n",
+      "     |  methods and attributes of an array.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  (for the __new__ method; see Notes below)\n",
+      "     |  \n",
+      "     |  shape : tuple of ints\n",
+      "     |      Shape of created array.\n",
+      "     |  dtype : data-type, optional\n",
+      "     |      Any object that can be interpreted as a numpy data type.\n",
+      "     |  buffer : object exposing buffer interface, optional\n",
+      "     |      Used to fill the array with data.\n",
+      "     |  offset : int, optional\n",
+      "     |      Offset of array data in buffer.\n",
+      "     |  strides : tuple of ints, optional\n",
+      "     |      Strides of data in memory.\n",
+      "     |  order : {'C', 'F'}, optional\n",
+      "     |      Row-major (C-style) or column-major (Fortran-style) order.\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  T : ndarray\n",
+      "     |      Transpose of the array.\n",
+      "     |  data : buffer\n",
+      "     |      The array's elements, in memory.\n",
+      "     |  dtype : dtype object\n",
+      "     |      Describes the format of the elements in the array.\n",
+      "     |  flags : dict\n",
+      "     |      Dictionary containing information related to memory use, e.g.,\n",
+      "     |      'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.\n",
+      "     |  flat : numpy.flatiter object\n",
+      "     |      Flattened version of the array as an iterator.  The iterator\n",
+      "     |      allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for\n",
+      "     |      assignment examples; TODO).\n",
+      "     |  imag : ndarray\n",
+      "     |      Imaginary part of the array.\n",
+      "     |  real : ndarray\n",
+      "     |      Real part of the array.\n",
+      "     |  size : int\n",
+      "     |      Number of elements in the array.\n",
+      "     |  itemsize : int\n",
+      "     |      The memory use of each array element in bytes.\n",
+      "     |  nbytes : int\n",
+      "     |      The total number of bytes required to store the array data,\n",
+      "     |      i.e., ``itemsize * size``.\n",
+      "     |  ndim : int\n",
+      "     |      The array's number of dimensions.\n",
+      "     |  shape : tuple of ints\n",
+      "     |      Shape of the array.\n",
+      "     |  strides : tuple of ints\n",
+      "     |      The step-size required to move from one element to the next in\n",
+      "     |      memory. For example, a contiguous ``(3, 4)`` array of type\n",
+      "     |      ``int16`` in C-order has strides ``(8, 2)``.  This implies that\n",
+      "     |      to move from element to element in memory requires jumps of 2 bytes.\n",
+      "     |      To move from row-to-row, one needs to jump 8 bytes at a time\n",
+      "     |      (``2 * 4``).\n",
+      "     |  ctypes : ctypes object\n",
+      "     |      Class containing properties of the array needed for interaction\n",
+      "     |      with ctypes.\n",
+      "     |  base : ndarray\n",
+      "     |      If the array is a view into another array, that array is its `base`\n",
+      "     |      (unless that array is also a view).  The `base` array is where the\n",
+      "     |      array data is actually stored.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  array : Construct an array.\n",
+      "     |  zeros : Create an array, each element of which is zero.\n",
+      "     |  empty : Create an array, but leave its allocated memory unchanged (i.e.,\n",
+      "     |          it contains \"garbage\").\n",
+      "     |  dtype : Create a data-type.\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  There are two modes of creating an array using ``__new__``:\n",
+      "     |  \n",
+      "     |  1. If `buffer` is None, then only `shape`, `dtype`, and `order`\n",
+      "     |     are used.\n",
+      "     |  2. If `buffer` is an object exposing the buffer interface, then\n",
+      "     |     all keywords are interpreted.\n",
+      "     |  \n",
+      "     |  No ``__init__`` method is needed because the array is fully initialized\n",
+      "     |  after the ``__new__`` method.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  These examples illustrate the low-level `ndarray` constructor.  Refer\n",
+      "     |  to the `See Also` section above for easier ways of constructing an\n",
+      "     |  ndarray.\n",
+      "     |  \n",
+      "     |  First mode, `buffer` is None:\n",
+      "     |  \n",
+      "     |  >>> np.ndarray(shape=(2,2), dtype=float, order='F')\n",
+      "     |  array([[0.0e+000, 0.0e+000], # random\n",
+      "     |         [     nan, 2.5e-323]])\n",
+      "     |  \n",
+      "     |  Second mode:\n",
+      "     |  \n",
+      "     |  >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n",
+      "     |  ...            offset=np.int_().itemsize,\n",
+      "     |  ...            dtype=int) # offset = 1*itemsize, i.e. skip first element\n",
+      "     |  array([2, 3])\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      a.__array__([dtype], /) -> reference if type unchanged, copy otherwise.\n",
+      "     |      \n",
+      "     |      Returns either a new reference to self if dtype is not given or a new array\n",
+      "     |      of provided data type if dtype is different from the current dtype of the\n",
+      "     |      array.\n",
+      "     |  \n",
+      "     |  __array_function__(...)\n",
+      "     |  \n",
+      "     |  __array_prepare__(...)\n",
+      "     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n",
+      "     |  \n",
+      "     |  __array_ufunc__(...)\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |      a.__copy__()\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.copy` is called on an array. Returns a copy of the array.\n",
+      "     |      \n",
+      "     |      Equivalent to ``a.copy(order='K')``.\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |      a.__deepcopy__(memo, /) -> Deep copy of array.\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.deepcopy` is called on an array.\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      Default object formatter.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __iadd__(self, value, /)\n",
+      "     |      Return self+=value.\n",
+      "     |  \n",
+      "     |  __iand__(self, value, /)\n",
+      "     |      Return self&=value.\n",
+      "     |  \n",
+      "     |  __ifloordiv__(self, value, /)\n",
+      "     |      Return self//=value.\n",
+      "     |  \n",
+      "     |  __ilshift__(self, value, /)\n",
+      "     |      Return self<<=value.\n",
+      "     |  \n",
+      "     |  __imatmul__(self, value, /)\n",
+      "     |      Return self@=value.\n",
+      "     |  \n",
+      "     |  __imod__(self, value, /)\n",
+      "     |      Return self%=value.\n",
+      "     |  \n",
+      "     |  __imul__(self, value, /)\n",
+      "     |      Return self*=value.\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __ior__(self, value, /)\n",
+      "     |      Return self|=value.\n",
+      "     |  \n",
+      "     |  __ipow__(self, value, /)\n",
+      "     |      Return self**=value.\n",
+      "     |  \n",
+      "     |  __irshift__(self, value, /)\n",
+      "     |      Return self>>=value.\n",
+      "     |  \n",
+      "     |  __isub__(self, value, /)\n",
+      "     |      Return self-=value.\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __itruediv__(self, value, /)\n",
+      "     |      Return self/=value.\n",
+      "     |  \n",
+      "     |  __ixor__(self, value, /)\n",
+      "     |      Return self^=value.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __matmul__(self, value, /)\n",
+      "     |      Return self@value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      a.__reduce__()\n",
+      "     |      \n",
+      "     |      For pickling.\n",
+      "     |  \n",
+      "     |  __reduce_ex__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmatmul__(self, value, /)\n",
+      "     |      Return value@self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |      a.__setstate__(state, /)\n",
+      "     |      \n",
+      "     |      For unpickling.\n",
+      "     |      \n",
+      "     |      The `state` argument must be a sequence that contains the following\n",
+      "     |      elements:\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      version : int\n",
+      "     |          optional pickle version. If omitted defaults to 0.\n",
+      "     |      shape : tuple\n",
+      "     |      dtype : data-type\n",
+      "     |      isFortran : bool\n",
+      "     |      rawdata : string or list\n",
+      "     |          a binary string with the data (or a list if 'a' is an object array)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      a.all(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if all elements evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.all` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.all : equivalent function\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      a.any(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if any of the elements of `a` evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.any` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.any : equivalent function\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      a.argmax(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the maximum values along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmax : equivalent function\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      a.argmin(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the minimum values along the given axis of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmin` for detailed documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmin : equivalent function\n",
+      "     |  \n",
+      "     |  argpartition(...)\n",
+      "     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would partition this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argpartition` for full documentation.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argpartition : equivalent function\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      a.argsort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would sort this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argsort` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argsort : equivalent function\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n",
+      "     |      \n",
+      "     |      Copy of the array, cast to a specified type.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          Typecode or data-type to which the array is cast.\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout order of the result.\n",
+      "     |          'C' means C order, 'F' means Fortran order, 'A'\n",
+      "     |          means 'F' order if all the arrays are Fortran contiguous,\n",
+      "     |          'C' order otherwise, and 'K' means as close to the\n",
+      "     |          order the array elements appear in memory as possible.\n",
+      "     |          Default is 'K'.\n",
+      "     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n",
+      "     |          Controls what kind of data casting may occur. Defaults to 'unsafe'\n",
+      "     |          for backwards compatibility.\n",
+      "     |      \n",
+      "     |            * 'no' means the data types should not be cast at all.\n",
+      "     |            * 'equiv' means only byte-order changes are allowed.\n",
+      "     |            * 'safe' means only casts which can preserve values are allowed.\n",
+      "     |            * 'same_kind' means only safe casts or casts within a kind,\n",
+      "     |              like float64 to float32, are allowed.\n",
+      "     |            * 'unsafe' means any data conversions may be done.\n",
+      "     |      subok : bool, optional\n",
+      "     |          If True, then sub-classes will be passed-through (default), otherwise\n",
+      "     |          the returned array will be forced to be a base-class array.\n",
+      "     |      copy : bool, optional\n",
+      "     |          By default, astype always returns a newly allocated array. If this\n",
+      "     |          is set to false, and the `dtype`, `order`, and `subok`\n",
+      "     |          requirements are satisfied, the input array is returned instead\n",
+      "     |          of a copy.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      arr_t : ndarray\n",
+      "     |          Unless `copy` is False and the other conditions for returning the input\n",
+      "     |          array are satisfied (see description for `copy` input parameter), `arr_t`\n",
+      "     |          is a new array of the same shape as the input array, with dtype, order\n",
+      "     |          given by `dtype`, `order`.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      .. versionchanged:: 1.17.0\n",
+      "     |         Casting between a simple data type and a structured one is possible only\n",
+      "     |         for \"unsafe\" casting.  Casting to multiple fields is allowed, but\n",
+      "     |         casting from multiple fields is not.\n",
+      "     |      \n",
+      "     |      .. versionchanged:: 1.9.0\n",
+      "     |         Casting from numeric to string types in 'safe' casting mode requires\n",
+      "     |         that the string dtype length is long enough to store the max\n",
+      "     |         integer/float value converted.\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ComplexWarning\n",
+      "     |          When casting from complex to float or int. To avoid this,\n",
+      "     |          one should use ``a.real.astype(t)``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 2.5])\n",
+      "     |      >>> x\n",
+      "     |      array([1. ,  2. ,  2.5])\n",
+      "     |      \n",
+      "     |      >>> x.astype(int)\n",
+      "     |      array([1, 2, 2])\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      a.byteswap(inplace=False)\n",
+      "     |      \n",
+      "     |      Swap the bytes of the array elements\n",
+      "     |      \n",
+      "     |      Toggle between low-endian and big-endian data representation by\n",
+      "     |      returning a byteswapped array, optionally swapped in-place.\n",
+      "     |      Arrays of byte-strings are not swapped. The real and imaginary\n",
+      "     |      parts of a complex number are swapped individually.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      inplace : bool, optional\n",
+      "     |          If ``True``, swap bytes in-place, default is ``False``.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          The byteswapped array. If `inplace` is ``True``, this is\n",
+      "     |          a view to self.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x1', '0x100', '0x2233']\n",
+      "     |      >>> A.byteswap(inplace=True)\n",
+      "     |      array([  256,     1, 13090], dtype=int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x100', '0x1', '0x3322']\n",
+      "     |      \n",
+      "     |      Arrays of byte-strings are not swapped\n",
+      "     |      \n",
+      "     |      >>> A = np.array([b'ceg', b'fac'])\n",
+      "     |      >>> A.byteswap()\n",
+      "     |      array([b'ceg', b'fac'], dtype='|S3')\n",
+      "     |      \n",
+      "     |      ``A.newbyteorder().byteswap()`` produces an array with the same values\n",
+      "     |        but different representation in memory\n",
+      "     |      \n",
+      "     |      >>> A = np.array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,\n",
+      "     |             0, 0], dtype=uint8)\n",
+      "     |      >>> A.newbyteorder().byteswap(inplace=True)\n",
+      "     |      array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,\n",
+      "     |             0, 3], dtype=uint8)\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      a.choose(choices, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Use an index array to construct a new array from a set of choices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.choose` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.choose : equivalent function\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      a.clip(min=None, max=None, out=None, **kwargs)\n",
+      "     |      \n",
+      "     |      Return an array whose values are limited to ``[min, max]``.\n",
+      "     |      One of max or min must be given.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.clip` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.clip : equivalent function\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      a.compress(condition, axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return selected slices of this array along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.compress` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.compress : equivalent function\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |      a.conj()\n",
+      "     |      \n",
+      "     |      Complex-conjugate all elements.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      a.conjugate()\n",
+      "     |      \n",
+      "     |      Return the complex conjugate, element-wise.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      a.copy(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout of the copy. 'C' means C-order,\n",
+      "     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n",
+      "     |          'C' otherwise. 'K' means match the layout of `a` as closely\n",
+      "     |          as possible. (Note that this function and :func:`numpy.copy` are very\n",
+      "     |          similar, but have different default values for their order=\n",
+      "     |          arguments.)\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      numpy.copy\n",
+      "     |      numpy.copyto\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n",
+      "     |      \n",
+      "     |      >>> y = x.copy()\n",
+      "     |      \n",
+      "     |      >>> x.fill(0)\n",
+      "     |      \n",
+      "     |      >>> x\n",
+      "     |      array([[0, 0, 0],\n",
+      "     |             [0, 0, 0]])\n",
+      "     |      \n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      \n",
+      "     |      >>> y.flags['C_CONTIGUOUS']\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      a.cumprod(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative product of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumprod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumprod : equivalent function\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      a.cumsum(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative sum of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumsum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumsum : equivalent function\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      a.diagonal(offset=0, axis1=0, axis2=1)\n",
+      "     |      \n",
+      "     |      Return specified diagonals. In NumPy 1.9 the returned array is a\n",
+      "     |      read-only view instead of a copy as in previous NumPy versions.  In\n",
+      "     |      a future version the read-only restriction will be removed.\n",
+      "     |      \n",
+      "     |      Refer to :func:`numpy.diagonal` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.diagonal : equivalent function\n",
+      "     |  \n",
+      "     |  dot(...)\n",
+      "     |      a.dot(b, out=None)\n",
+      "     |      \n",
+      "     |      Dot product of two arrays.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.dot` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dot : equivalent function\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.eye(2)\n",
+      "     |      >>> b = np.ones((2, 2)) * 2\n",
+      "     |      >>> a.dot(b)\n",
+      "     |      array([[2.,  2.],\n",
+      "     |             [2.,  2.]])\n",
+      "     |      \n",
+      "     |      This array method can be conveniently chained:\n",
+      "     |      \n",
+      "     |      >>> a.dot(b).dot(b)\n",
+      "     |      array([[8.,  8.],\n",
+      "     |             [8.,  8.]])\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      a.dump(file)\n",
+      "     |      \n",
+      "     |      Dump a pickle of the array to the specified file.\n",
+      "     |      The array can be read back with pickle.load or numpy.load.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      file : str or Path\n",
+      "     |          A string naming the dump file.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      a.dumps()\n",
+      "     |      \n",
+      "     |      Returns the pickle of the array as a string.\n",
+      "     |      pickle.loads or numpy.loads will convert the string back to an array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      a.fill(value)\n",
+      "     |      \n",
+      "     |      Fill the array with a scalar value.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      value : scalar\n",
+      "     |          All elements of `a` will be assigned this value.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([1, 2])\n",
+      "     |      >>> a.fill(0)\n",
+      "     |      >>> a\n",
+      "     |      array([0, 0])\n",
+      "     |      >>> a = np.empty(2)\n",
+      "     |      >>> a.fill(1)\n",
+      "     |      >>> a\n",
+      "     |      array([1.,  1.])\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      a.flatten(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          'C' means to flatten in row-major (C-style) order.\n",
+      "     |          'F' means to flatten in column-major (Fortran-\n",
+      "     |          style) order. 'A' means to flatten in column-major\n",
+      "     |          order if `a` is Fortran *contiguous* in memory,\n",
+      "     |          row-major order otherwise. 'K' means to flatten\n",
+      "     |          `a` in the order the elements occur in memory.\n",
+      "     |          The default is 'C'.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : ndarray\n",
+      "     |          A copy of the input array, flattened to one dimension.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ravel : Return a flattened array.\n",
+      "     |      flat : A 1-D flat iterator over the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,2], [3,4]])\n",
+      "     |      >>> a.flatten()\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |      >>> a.flatten('F')\n",
+      "     |      array([1, 3, 2, 4])\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      a.getfield(dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Returns a field of the given array as a certain type.\n",
+      "     |      \n",
+      "     |      A field is a view of the array data with a given data-type. The values in\n",
+      "     |      the view are determined by the given type and the offset into the current\n",
+      "     |      array in bytes. The offset needs to be such that the view dtype fits in the\n",
+      "     |      array dtype; for example an array of dtype complex128 has 16-byte elements.\n",
+      "     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n",
+      "     |      between 0 and 12 bytes.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          The data type of the view. The dtype size of the view can not be larger\n",
+      "     |          than that of the array itself.\n",
+      "     |      offset : int\n",
+      "     |          Number of bytes to skip before beginning the element view.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.diag([1.+1.j]*2)\n",
+      "     |      >>> x[1, 1] = 2 + 4.j\n",
+      "     |      >>> x\n",
+      "     |      array([[1.+1.j,  0.+0.j],\n",
+      "     |             [0.+0.j,  2.+4.j]])\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  2.]])\n",
+      "     |      \n",
+      "     |      By choosing an offset of 8 bytes we can select the complex part of the\n",
+      "     |      array for our view:\n",
+      "     |      \n",
+      "     |      >>> x.getfield(np.float64, offset=8)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  4.]])\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      a.item(*args)\n",
+      "     |      \n",
+      "     |      Copy an element of an array to a standard Python scalar and return it.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments (variable number and type)\n",
+      "     |      \n",
+      "     |          * none: in this case, the method only works for arrays\n",
+      "     |            with one element (`a.size == 1`), which element is\n",
+      "     |            copied into a standard Python scalar object and returned.\n",
+      "     |      \n",
+      "     |          * int_type: this argument is interpreted as a flat index into\n",
+      "     |            the array, specifying which element to copy and return.\n",
+      "     |      \n",
+      "     |          * tuple of int_types: functions as does a single int_type argument,\n",
+      "     |            except that the argument is interpreted as an nd-index into the\n",
+      "     |            array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      z : Standard Python scalar object\n",
+      "     |          A copy of the specified element of the array as a suitable\n",
+      "     |          Python scalar\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      When the data type of `a` is longdouble or clongdouble, item() returns\n",
+      "     |      a scalar array object because there is no available Python scalar that\n",
+      "     |      would not lose information. Void arrays return a buffer object for item(),\n",
+      "     |      unless fields are defined, in which case a tuple is returned.\n",
+      "     |      \n",
+      "     |      `item` is very similar to a[args], except, instead of an array scalar,\n",
+      "     |      a standard Python scalar is returned. This can be useful for speeding up\n",
+      "     |      access to elements of the array and doing arithmetic on elements of the\n",
+      "     |      array using Python's optimized math.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.item(3)\n",
+      "     |      1\n",
+      "     |      >>> x.item(7)\n",
+      "     |      0\n",
+      "     |      >>> x.item((0, 1))\n",
+      "     |      2\n",
+      "     |      >>> x.item((2, 2))\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      a.itemset(*args)\n",
+      "     |      \n",
+      "     |      Insert scalar into an array (scalar is cast to array's dtype, if possible)\n",
+      "     |      \n",
+      "     |      There must be at least 1 argument, and define the last argument\n",
+      "     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster\n",
+      "     |      than ``a[args] = item``.  The item should be a scalar value and `args`\n",
+      "     |      must select a single item in the array `a`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments\n",
+      "     |          If one argument: a scalar, only used in case `a` is of size 1.\n",
+      "     |          If two arguments: the last argument is the value to be set\n",
+      "     |          and must be a scalar, the first argument specifies a single array\n",
+      "     |          element location. It is either an int or a tuple.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Compared to indexing syntax, `itemset` provides some speed increase\n",
+      "     |      for placing a scalar into a particular location in an `ndarray`,\n",
+      "     |      if you must do this.  However, generally this is discouraged:\n",
+      "     |      among other problems, it complicates the appearance of the code.\n",
+      "     |      Also, when using `itemset` (and `item`) inside a loop, be sure\n",
+      "     |      to assign the methods to a local variable to avoid the attribute\n",
+      "     |      look-up at each loop iteration.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.itemset(4, 0)\n",
+      "     |      >>> x.itemset((2, 2), 9)\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 0, 6],\n",
+      "     |             [1, 0, 9]])\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      a.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the maximum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amax : equivalent function\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      a.mean(axis=None, dtype=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the average of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.mean` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.mean : equivalent function\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      a.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the minimum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amin` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amin : equivalent function\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      arr.newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return the array with the same data viewed with a different byte order.\n",
+      "     |      \n",
+      "     |      Equivalent to::\n",
+      "     |      \n",
+      "     |          arr.view(arr.dtype.newbytorder(new_order))\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the array data\n",
+      "     |      type.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : string, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          below. `new_order` codes can be any of:\n",
+      "     |      \n",
+      "     |          * 'S' - swap dtype from current to opposite endian\n",
+      "     |          * {'<', 'L'} - little endian\n",
+      "     |          * {'>', 'B'} - big endian\n",
+      "     |          * {'=', 'N'} - native order\n",
+      "     |          * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |          The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_arr : array\n",
+      "     |          New array object with the dtype reflecting given change to the\n",
+      "     |          byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      a.nonzero()\n",
+      "     |      \n",
+      "     |      Return the indices of the elements that are non-zero.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.nonzero` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.nonzero : equivalent function\n",
+      "     |  \n",
+      "     |  partition(...)\n",
+      "     |      a.partition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Rearranges the elements in the array in such a way that the value of the\n",
+      "     |      element in kth position is in the position it would be in a sorted array.\n",
+      "     |      All elements smaller than the kth element are moved before this element and\n",
+      "     |      all equal or greater are moved behind it. The ordering of the elements in\n",
+      "     |      the two partitions is undefined.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      kth : int or sequence of ints\n",
+      "     |          Element index to partition by. The kth element value will be in its\n",
+      "     |          final sorted position and all smaller elements will be moved before it\n",
+      "     |          and all equal or greater elements behind it.\n",
+      "     |          The order of all elements in the partitions is undefined.\n",
+      "     |          If provided with a sequence of kth it will partition all elements\n",
+      "     |          indexed by kth of them into their sorted position at once.\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'introselect'}, optional\n",
+      "     |          Selection algorithm. Default is 'introselect'.\n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc. A single field can\n",
+      "     |          be specified as a string, and not all fields need to be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.partition : Return a parititioned copy of an array.\n",
+      "     |      argpartition : Indirect partition.\n",
+      "     |      sort : Full sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See ``np.partition`` for notes on the different algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([3, 4, 2, 1])\n",
+      "     |      >>> a.partition(3)\n",
+      "     |      >>> a\n",
+      "     |      array([2, 1, 3, 4])\n",
+      "     |      \n",
+      "     |      >>> a.partition((1, 3))\n",
+      "     |      >>> a\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)\n",
+      "     |      \n",
+      "     |      Return the product of the array elements over the given axis\n",
+      "     |      \n",
+      "     |      Refer to `numpy.prod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.prod : equivalent function\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      a.ptp(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Peak to peak (maximum - minimum) value along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ptp` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ptp : equivalent function\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      a.put(indices, values, mode='raise')\n",
+      "     |      \n",
+      "     |      Set ``a.flat[n] = values[n]`` for all `n` in indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.put` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.put : equivalent function\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      a.ravel([order])\n",
+      "     |      \n",
+      "     |      Return a flattened array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ravel` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ravel : equivalent function\n",
+      "     |      \n",
+      "     |      ndarray.flat : a flat iterator on the array.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      a.repeat(repeats, axis=None)\n",
+      "     |      \n",
+      "     |      Repeat elements of an array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.repeat` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.repeat : equivalent function\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      a.reshape(shape, order='C')\n",
+      "     |      \n",
+      "     |      Returns an array containing the same data with a new shape.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.reshape` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : equivalent function\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Unlike the free function `numpy.reshape`, this method on `ndarray` allows\n",
+      "     |      the elements of the shape parameter to be passed in as separate arguments.\n",
+      "     |      For example, ``a.reshape(10, 11)`` is equivalent to\n",
+      "     |      ``a.reshape((10, 11))``.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      a.resize(new_shape, refcheck=True)\n",
+      "     |      \n",
+      "     |      Change shape and size of array in-place.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_shape : tuple of ints, or `n` ints\n",
+      "     |          Shape of resized array.\n",
+      "     |      refcheck : bool, optional\n",
+      "     |          If False, reference count will not be checked. Default is True.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ValueError\n",
+      "     |          If `a` does not own its own data or references or views to it exist,\n",
+      "     |          and the data memory must be changed.\n",
+      "     |          PyPy only: will always raise if the data memory must be changed, since\n",
+      "     |          there is no reliable way to determine if references or views to it\n",
+      "     |          exist.\n",
+      "     |      \n",
+      "     |      SystemError\n",
+      "     |          If the `order` keyword argument is specified. This behaviour is a\n",
+      "     |          bug in NumPy.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      resize : Return a new array with the specified shape.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This reallocates space for the data area if necessary.\n",
+      "     |      \n",
+      "     |      Only contiguous arrays (data elements consecutive in memory) can be\n",
+      "     |      resized.\n",
+      "     |      \n",
+      "     |      The purpose of the reference count check is to make sure you\n",
+      "     |      do not use this array as a buffer for another Python object and then\n",
+      "     |      reallocate the memory. However, reference counts can increase in\n",
+      "     |      other ways so if you are sure that you have not shared the memory\n",
+      "     |      for this array with another Python object, then you may safely set\n",
+      "     |      `refcheck` to False.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      Shrinking an array: array is flattened (in the order that the data are\n",
+      "     |      stored in memory), resized, and reshaped:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='C')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [1]])\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='F')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [2]])\n",
+      "     |      \n",
+      "     |      Enlarging an array: as above, but missing entries are filled with zeros:\n",
+      "     |      \n",
+      "     |      >>> b = np.array([[0, 1], [2, 3]])\n",
+      "     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n",
+      "     |      >>> b\n",
+      "     |      array([[0, 1, 2],\n",
+      "     |             [3, 0, 0]])\n",
+      "     |      \n",
+      "     |      Referencing an array prevents resizing...\n",
+      "     |      \n",
+      "     |      >>> c = a\n",
+      "     |      >>> a.resize((1, 1))\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |      ...\n",
+      "     |      ValueError: cannot resize an array that references or is referenced ...\n",
+      "     |      \n",
+      "     |      Unless `refcheck` is False:\n",
+      "     |      \n",
+      "     |      >>> a.resize((1, 1), refcheck=False)\n",
+      "     |      >>> a\n",
+      "     |      array([[0]])\n",
+      "     |      >>> c\n",
+      "     |      array([[0]])\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      a.round(decimals=0, out=None)\n",
+      "     |      \n",
+      "     |      Return `a` with each element rounded to the given number of decimals.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.around` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.around : equivalent function\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      a.searchsorted(v, side='left', sorter=None)\n",
+      "     |      \n",
+      "     |      Find indices where elements of v should be inserted in a to maintain order.\n",
+      "     |      \n",
+      "     |      For full documentation, see `numpy.searchsorted`\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.searchsorted : equivalent function\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      a.setfield(val, dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Put a value into a specified place in a field defined by a data-type.\n",
+      "     |      \n",
+      "     |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n",
+      "     |      bytes into the field.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      val : object\n",
+      "     |          Value to be placed in field.\n",
+      "     |      dtype : dtype object\n",
+      "     |          Data-type of the field in which to place `val`.\n",
+      "     |      offset : int, optional\n",
+      "     |          The number of bytes into the field at which to place `val`.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      getfield\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.eye(3)\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |      >>> x.setfield(3, np.int32)\n",
+      "     |      >>> x.getfield(np.int32)\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3],\n",
+      "     |             [3, 3, 3]], dtype=int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.0e+000, 1.5e-323, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.0e+000, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.5e-323, 1.0e+000]])\n",
+      "     |      >>> x.setfield(np.eye(3), np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      a.setflags(write=None, align=None, uic=None)\n",
+      "     |      \n",
+      "     |      Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),\n",
+      "     |      respectively.\n",
+      "     |      \n",
+      "     |      These Boolean-valued flags affect how numpy interprets the memory\n",
+      "     |      area used by `a` (see Notes below). The ALIGNED flag can only\n",
+      "     |      be set to True if the data is actually aligned according to the type.\n",
+      "     |      The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set\n",
+      "     |      to True. The flag WRITEABLE can only be set to True if the array owns its\n",
+      "     |      own memory, or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |      interface, or is a string. (The exception for string is made so that\n",
+      "     |      unpickling can be done without copying memory.)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      write : bool, optional\n",
+      "     |          Describes whether or not `a` can be written to.\n",
+      "     |      align : bool, optional\n",
+      "     |          Describes whether or not `a` is aligned properly for its type.\n",
+      "     |      uic : bool, optional\n",
+      "     |          Describes whether or not `a` is a copy of another \"base\" array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Array flags provide information about how the memory area used\n",
+      "     |      for the array is to be interpreted. There are 7 Boolean flags\n",
+      "     |      in use, only four of which can be changed by the user:\n",
+      "     |      WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n",
+      "     |      \n",
+      "     |      WRITEABLE (W) the data area can be written to;\n",
+      "     |      \n",
+      "     |      ALIGNED (A) the data and strides are aligned appropriately for the hardware\n",
+      "     |      (as determined by the compiler);\n",
+      "     |      \n",
+      "     |      UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;\n",
+      "     |      \n",
+      "     |      WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced\n",
+      "     |      by .base). When the C-API function PyArray_ResolveWritebackIfCopy is\n",
+      "     |      called, the base array will be updated with the contents of this array.\n",
+      "     |      \n",
+      "     |      All flags can be accessed using the single (upper case) letter as well\n",
+      "     |      as the full name.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.array([[3, 1, 7],\n",
+      "     |      ...               [2, 0, 0],\n",
+      "     |      ...               [8, 5, 9]])\n",
+      "     |      >>> y\n",
+      "     |      array([[3, 1, 7],\n",
+      "     |             [2, 0, 0],\n",
+      "     |             [8, 5, 9]])\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : True\n",
+      "     |        ALIGNED : True\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(write=0, align=0)\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : False\n",
+      "     |        ALIGNED : False\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(uic=1)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: cannot set WRITEBACKIFCOPY flag to True\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      a.sort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Sort an array in-place. Refer to `numpy.sort` for full documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n",
+      "     |          Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n",
+      "     |          and 'mergesort' use timsort under the covers and, in general, the\n",
+      "     |          actual implementation will vary with datatype. The 'mergesort' option\n",
+      "     |          is retained for backwards compatibility.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.15.0.\n",
+      "     |             The 'stable' option was added.\n",
+      "     |      \n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc.  A single field can\n",
+      "     |          be specified as a string, and not all fields need be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sort : Return a sorted copy of an array.\n",
+      "     |      numpy.argsort : Indirect sort.\n",
+      "     |      numpy.lexsort : Indirect stable sort on multiple keys.\n",
+      "     |      numpy.searchsorted : Find elements in sorted array.\n",
+      "     |      numpy.partition: Partial sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See `numpy.sort` for notes on the different sorting algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,4], [3,1]])\n",
+      "     |      >>> a.sort(axis=1)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 4],\n",
+      "     |             [1, 3]])\n",
+      "     |      >>> a.sort(axis=0)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 3],\n",
+      "     |             [1, 4]])\n",
+      "     |      \n",
+      "     |      Use the `order` keyword to specify a field to use when sorting a\n",
+      "     |      structured array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n",
+      "     |      >>> a.sort(order='y')\n",
+      "     |      >>> a\n",
+      "     |      array([(b'c', 1), (b'a', 2)],\n",
+      "     |            dtype=[('x', 'S1'), ('y', '<i8')])\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      a.squeeze(axis=None)\n",
+      "     |      \n",
+      "     |      Remove single-dimensional entries from the shape of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.squeeze` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.squeeze : equivalent function\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the standard deviation of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.std` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.std : equivalent function\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)\n",
+      "     |      \n",
+      "     |      Return the sum of the array elements over the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.sum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sum : equivalent function\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      a.swapaxes(axis1, axis2)\n",
+      "     |      \n",
+      "     |      Return a view of the array with `axis1` and `axis2` interchanged.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.swapaxes` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.swapaxes : equivalent function\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      a.take(indices, axis=None, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Return an array formed from the elements of `a` at the given indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.take` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.take : equivalent function\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |      a.tobytes(order='C')\n",
+      "     |      \n",
+      "     |      Construct Python bytes containing the raw data bytes in the array.\n",
+      "     |      \n",
+      "     |      Constructs Python bytes showing a copy of the raw contents of\n",
+      "     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',\n",
+      "     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n",
+      "     |      unless the F_CONTIGUOUS flag in the array is set, in which case it\n",
+      "     |      means 'Fortran' order.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.9.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', None}, optional\n",
+      "     |          Order of the data for multidimensional arrays:\n",
+      "     |          C, Fortran, or the same as for the original array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      s : bytes\n",
+      "     |          Python bytes exhibiting a copy of `a`'s raw data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype='<u2')\n",
+      "     |      >>> x.tobytes()\n",
+      "     |      b'\\x00\\x00\\x01\\x00\\x02\\x00\\x03\\x00'\n",
+      "     |      >>> x.tobytes('C') == x.tobytes()\n",
+      "     |      True\n",
+      "     |      >>> x.tobytes('F')\n",
+      "     |      b'\\x00\\x00\\x02\\x00\\x01\\x00\\x03\\x00'\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      a.tofile(fid, sep=\"\", format=\"%s\")\n",
+      "     |      \n",
+      "     |      Write array to a file as text or binary (default).\n",
+      "     |      \n",
+      "     |      Data is always written in 'C' order, independent of the order of `a`.\n",
+      "     |      The data produced by this method can be recovered using the function\n",
+      "     |      fromfile().\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      fid : file or str or Path\n",
+      "     |          An open file object, or a string containing a filename.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |      \n",
+      "     |      sep : str\n",
+      "     |          Separator between array items for text output.\n",
+      "     |          If \"\" (empty), a binary file is written, equivalent to\n",
+      "     |          ``file.write(a.tobytes())``.\n",
+      "     |      format : str\n",
+      "     |          Format string for text file output.\n",
+      "     |          Each entry in the array is formatted to text by first converting\n",
+      "     |          it to the closest Python type, and then using \"format\" % item.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is a convenience function for quick storage of array data.\n",
+      "     |      Information on endianness and precision is lost, so this method is not a\n",
+      "     |      good choice for files intended to archive data or transport data between\n",
+      "     |      machines with different endianness. Some of these problems can be overcome\n",
+      "     |      by outputting the data as text files, at the expense of speed and file\n",
+      "     |      size.\n",
+      "     |      \n",
+      "     |      When fid is a file object, array contents are directly written to the\n",
+      "     |      file, bypassing the file object's ``write`` method. As a result, tofile\n",
+      "     |      cannot be used with files objects supporting compression (e.g., GzipFile)\n",
+      "     |      or file-like objects that do not support ``fileno()`` (e.g., BytesIO).\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      a.tolist()\n",
+      "     |      \n",
+      "     |      Return the array as an ``a.ndim``-levels deep nested list of Python scalars.\n",
+      "     |      \n",
+      "     |      Return a copy of the array data as a (nested) Python list.\n",
+      "     |      Data items are converted to the nearest compatible builtin Python type, via\n",
+      "     |      the `~numpy.ndarray.item` function.\n",
+      "     |      \n",
+      "     |      If ``a.ndim`` is 0, then since the depth of the nested list is 0, it will\n",
+      "     |      not be a list at all, but a simple Python scalar.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      none\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : object, or list of object, or list of list of object, or ...\n",
+      "     |          The possibly nested list of array elements.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The array may be recreated via ``a = np.array(a.tolist())``, although this\n",
+      "     |      may sometimes lose precision.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      For a 1D array, ``a.tolist()`` is almost the same as ``list(a)``,\n",
+      "     |      except that ``tolist`` changes numpy scalars to Python scalars:\n",
+      "     |      \n",
+      "     |      >>> a = np.uint32([1, 2])\n",
+      "     |      >>> a_list = list(a)\n",
+      "     |      >>> a_list\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_list[0])\n",
+      "     |      <class 'numpy.uint32'>\n",
+      "     |      >>> a_tolist = a.tolist()\n",
+      "     |      >>> a_tolist\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_tolist[0])\n",
+      "     |      <class 'int'>\n",
+      "     |      \n",
+      "     |      Additionally, for a 2D array, ``tolist`` applies recursively:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> list(a)\n",
+      "     |      [array([1, 2]), array([3, 4])]\n",
+      "     |      >>> a.tolist()\n",
+      "     |      [[1, 2], [3, 4]]\n",
+      "     |      \n",
+      "     |      The base case for this recursion is a 0D array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array(1)\n",
+      "     |      >>> list(a)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        ...\n",
+      "     |      TypeError: iteration over a 0-d array\n",
+      "     |      >>> a.tolist()\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      a.tostring(order='C')\n",
+      "     |      \n",
+      "     |      A compatibility alias for `tobytes`, with exactly the same behavior.\n",
+      "     |      \n",
+      "     |      Despite its name, it returns `bytes` not `str`\\ s.\n",
+      "     |      \n",
+      "     |      .. deprecated:: 1.19.0\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the sum along diagonals of the array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.trace` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.trace : equivalent function\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      a.transpose(*axes)\n",
+      "     |      \n",
+      "     |      Returns a view of the array with axes transposed.\n",
+      "     |      \n",
+      "     |      For a 1-D array this has no effect, as a transposed vector is simply the\n",
+      "     |      same vector. To convert a 1-D array into a 2D column vector, an additional\n",
+      "     |      dimension must be added. `np.atleast2d(a).T` achieves this, as does\n",
+      "     |      `a[:, np.newaxis]`.\n",
+      "     |      For a 2-D array, this is a standard matrix transpose.\n",
+      "     |      For an n-D array, if axes are given, their order indicates how the\n",
+      "     |      axes are permuted (see Examples). If axes are not provided and\n",
+      "     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n",
+      "     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axes : None, tuple of ints, or `n` ints\n",
+      "     |      \n",
+      "     |       * None or no argument: reverses the order of the axes.\n",
+      "     |      \n",
+      "     |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n",
+      "     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n",
+      "     |      \n",
+      "     |       * `n` ints: same as an n-tuple of the same ints (this form is\n",
+      "     |         intended simply as a \"convenience\" alternative to the tuple form)\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          View of `a`, with axes suitably permuted.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ndarray.T : Array property returning the array transposed.\n",
+      "     |      ndarray.reshape : Give a new shape to an array without changing its data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]])\n",
+      "     |      >>> a.transpose()\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose((1, 0))\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose(1, 0)\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the variance of the array elements, along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.var` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.var : equivalent function\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      a.view([dtype][, type])\n",
+      "     |      \n",
+      "     |      New view of array with the same data.\n",
+      "     |      \n",
+      "     |      .. note::\n",
+      "     |          Passing None for ``dtype`` is different from omitting the parameter,\n",
+      "     |          since the former invokes ``dtype(None)`` which is an alias for\n",
+      "     |          ``dtype('float_')``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : data-type or ndarray sub-class, optional\n",
+      "     |          Data-type descriptor of the returned view, e.g., float32 or int16.\n",
+      "     |          Omitting it results in the view having the same data-type as `a`.\n",
+      "     |          This argument can also be specified as an ndarray sub-class, which\n",
+      "     |          then specifies the type of the returned object (this is equivalent to\n",
+      "     |          setting the ``type`` parameter).\n",
+      "     |      type : Python type, optional\n",
+      "     |          Type of the returned view, e.g., ndarray or matrix.  Again, omission\n",
+      "     |          of the parameter results in type preservation.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      ``a.view()`` is used two different ways:\n",
+      "     |      \n",
+      "     |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n",
+      "     |      of the array's memory with a different data-type.  This can cause a\n",
+      "     |      reinterpretation of the bytes of memory.\n",
+      "     |      \n",
+      "     |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n",
+      "     |      returns an instance of `ndarray_subclass` that looks at the same array\n",
+      "     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the\n",
+      "     |      memory.\n",
+      "     |      \n",
+      "     |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n",
+      "     |      bytes per entry than the previous dtype (for example, converting a\n",
+      "     |      regular array to a structured array), then the behavior of the view\n",
+      "     |      cannot be predicted just from the superficial appearance of ``a`` (shown\n",
+      "     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in\n",
+      "     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n",
+      "     |      defined as a slice or transpose, etc., the view may give different\n",
+      "     |      results.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      \n",
+      "     |      Viewing array data using a different type and dtype:\n",
+      "     |      \n",
+      "     |      >>> y = x.view(dtype=np.int16, type=np.matrix)\n",
+      "     |      >>> y\n",
+      "     |      matrix([[513]], dtype=int16)\n",
+      "     |      >>> print(type(y))\n",
+      "     |      <class 'numpy.matrix'>\n",
+      "     |      \n",
+      "     |      Creating a view on a structured array so it can be used in calculations\n",
+      "     |      \n",
+      "     |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n",
+      "     |      >>> xv\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]], dtype=int8)\n",
+      "     |      >>> xv.mean(0)\n",
+      "     |      array([2.,  3.])\n",
+      "     |      \n",
+      "     |      Making changes to the view changes the underlying array\n",
+      "     |      \n",
+      "     |      >>> xv[0,1] = 20\n",
+      "     |      >>> x\n",
+      "     |      array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])\n",
+      "     |      \n",
+      "     |      Using a view to convert an array to a recarray:\n",
+      "     |      \n",
+      "     |      >>> z = x.view(np.recarray)\n",
+      "     |      >>> z.a\n",
+      "     |      array([1, 3], dtype=int8)\n",
+      "     |      \n",
+      "     |      Views share data:\n",
+      "     |      \n",
+      "     |      >>> x[0] = (9, 10)\n",
+      "     |      >>> z[0]\n",
+      "     |      (9, 10)\n",
+      "     |      \n",
+      "     |      Views that change the dtype size (bytes per entry) should normally be\n",
+      "     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n",
+      "     |      >>> y = x[:, 0:2]\n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2],\n",
+      "     |             [4, 5]], dtype=int16)\n",
+      "     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |          ...\n",
+      "     |      ValueError: To change to a dtype of a different size, the array must be C-contiguous\n",
+      "     |      >>> z = y.copy()\n",
+      "     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      array([[(1, 2)],\n",
+      "     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      The transposed array.\n",
+      "     |      \n",
+      "     |      Same as ``self.transpose()``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1.,2.],[3.,4.]])\n",
+      "     |      >>> x\n",
+      "     |      array([[ 1.,  2.],\n",
+      "     |             [ 3.,  4.]])\n",
+      "     |      >>> x.T\n",
+      "     |      array([[ 1.,  3.],\n",
+      "     |             [ 2.,  4.]])\n",
+      "     |      >>> x = np.array([1.,2.,3.,4.])\n",
+      "     |      >>> x\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      >>> x.T\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_finalize__\n",
+      "     |      None.\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side.\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: C-struct side.\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      Base object if memory is from some other object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      The base of an array that owns its memory is None:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([1,2,3,4])\n",
+      "     |      >>> x.base is None\n",
+      "     |      True\n",
+      "     |      \n",
+      "     |      Slicing creates a view, whose memory is shared with x:\n",
+      "     |      \n",
+      "     |      >>> y = x[2:]\n",
+      "     |      >>> y.base is x\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  ctypes\n",
+      "     |      An object to simplify the interaction of the array with the ctypes\n",
+      "     |      module.\n",
+      "     |      \n",
+      "     |      This attribute creates an object that makes it easier to use arrays\n",
+      "     |      when calling shared libraries with the ctypes module. The returned\n",
+      "     |      object has, among others, data, shape, and strides attributes (see\n",
+      "     |      Notes below) which themselves return ctypes objects that can be used\n",
+      "     |      as arguments to a shared library.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      c : Python object\n",
+      "     |          Possessing attributes data, shape, strides, etc.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ctypeslib\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Below are the public attributes of this object which were documented\n",
+      "     |      in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n",
+      "     |      as well as documented private attributes):\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.data\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.shape\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.strides\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.data_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.shape_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.strides_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      If the ctypes module is not available, then the ctypes attribute\n",
+      "     |      of array objects still returns something useful, but ctypes objects\n",
+      "     |      are not returned and errors may be raised instead. In particular,\n",
+      "     |      the object will still have the ``as_parameter`` attribute which will\n",
+      "     |      return an integer equal to the data attribute.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> import ctypes\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]], dtype=int32)\n",
+      "     |      >>> x.ctypes.data\n",
+      "     |      31962608 # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))\n",
+      "     |      <__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents\n",
+      "     |      c_uint(0)\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents\n",
+      "     |      c_ulong(4294967296)\n",
+      "     |      >>> x.ctypes.shape\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary\n",
+      "     |      >>> x.ctypes.strides\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      Python buffer object pointing to the start of the array's data.\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      Data-type of the array's elements.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      d : numpy dtype object\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dtype\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]])\n",
+      "     |      >>> x.dtype\n",
+      "     |      dtype('int32')\n",
+      "     |      >>> type(x.dtype)\n",
+      "     |      <type 'numpy.dtype'>\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      Information about the memory layout of the array.\n",
+      "     |      \n",
+      "     |      Attributes\n",
+      "     |      ----------\n",
+      "     |      C_CONTIGUOUS (C)\n",
+      "     |          The data is in a single, C-style contiguous segment.\n",
+      "     |      F_CONTIGUOUS (F)\n",
+      "     |          The data is in a single, Fortran-style contiguous segment.\n",
+      "     |      OWNDATA (O)\n",
+      "     |          The array owns the memory it uses or borrows it from another object.\n",
+      "     |      WRITEABLE (W)\n",
+      "     |          The data area can be written to.  Setting this to False locks\n",
+      "     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE\n",
+      "     |          from its base array at creation time, but a view of a writeable\n",
+      "     |          array may be subsequently locked while the base array remains writeable.\n",
+      "     |          (The opposite is not true, in that a view of a locked array may not\n",
+      "     |          be made writeable.  However, currently, locking a base object does not\n",
+      "     |          lock any views that already reference it, so under that circumstance it\n",
+      "     |          is possible to alter the contents of a locked array via a previously\n",
+      "     |          created writeable view onto it.)  Attempting to change a non-writeable\n",
+      "     |          array raises a RuntimeError exception.\n",
+      "     |      ALIGNED (A)\n",
+      "     |          The data and all elements are aligned appropriately for the hardware.\n",
+      "     |      WRITEBACKIFCOPY (X)\n",
+      "     |          This array is a copy of some other array. The C-API function\n",
+      "     |          PyArray_ResolveWritebackIfCopy must be called before deallocating\n",
+      "     |          to the base array will be updated with the contents of this array.\n",
+      "     |      UPDATEIFCOPY (U)\n",
+      "     |          (Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array.\n",
+      "     |          When this array is\n",
+      "     |          deallocated, the base array will be updated with the contents of\n",
+      "     |          this array.\n",
+      "     |      FNC\n",
+      "     |          F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      FORC\n",
+      "     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n",
+      "     |      BEHAVED (B)\n",
+      "     |          ALIGNED and WRITEABLE.\n",
+      "     |      CARRAY (CA)\n",
+      "     |          BEHAVED and C_CONTIGUOUS.\n",
+      "     |      FARRAY (FA)\n",
+      "     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n",
+      "     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n",
+      "     |      names are only supported in dictionary access.\n",
+      "     |      \n",
+      "     |      Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be\n",
+      "     |      changed by the user, via direct assignment to the attribute or dictionary\n",
+      "     |      entry, or by calling `ndarray.setflags`.\n",
+      "     |      \n",
+      "     |      The array flags cannot be set arbitrarily:\n",
+      "     |      \n",
+      "     |      - UPDATEIFCOPY can only be set ``False``.\n",
+      "     |      - WRITEBACKIFCOPY can only be set ``False``.\n",
+      "     |      - ALIGNED can only be set ``True`` if the data is truly aligned.\n",
+      "     |      - WRITEABLE can only be set ``True`` if the array owns its own memory\n",
+      "     |        or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |        interface or is a string.\n",
+      "     |      \n",
+      "     |      Arrays can be both C-style and Fortran-style contiguous simultaneously.\n",
+      "     |      This is clear for 1-dimensional arrays, but can also be true for higher\n",
+      "     |      dimensional arrays.\n",
+      "     |      \n",
+      "     |      Even for contiguous arrays a stride for a given dimension\n",
+      "     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n",
+      "     |      or the array has no elements.\n",
+      "     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n",
+      "     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n",
+      "     |      Fortran-style contiguous arrays is true.\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      A 1-D iterator over the array.\n",
+      "     |      \n",
+      "     |      This is a `numpy.flatiter` instance, which acts similarly to, but is not\n",
+      "     |      a subclass of, Python's built-in iterator object.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      flatten : Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      flatiter\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(1, 7).reshape(2, 3)\n",
+      "     |      >>> x\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      >>> x.flat[3]\n",
+      "     |      4\n",
+      "     |      >>> x.T\n",
+      "     |      array([[1, 4],\n",
+      "     |             [2, 5],\n",
+      "     |             [3, 6]])\n",
+      "     |      >>> x.T.flat[3]\n",
+      "     |      5\n",
+      "     |      >>> type(x.flat)\n",
+      "     |      <class 'numpy.flatiter'>\n",
+      "     |      \n",
+      "     |      An assignment example:\n",
+      "     |      \n",
+      "     |      >>> x.flat = 3; x\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3]])\n",
+      "     |      >>> x.flat[[1,4]] = 1; x\n",
+      "     |      array([[3, 1, 3],\n",
+      "     |             [3, 1, 3]])\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      The imaginary part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.imag\n",
+      "     |      array([ 0.        ,  0.70710678])\n",
+      "     |      >>> x.imag.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      Length of one array element in bytes.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.float64)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      8\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.complex128)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      16\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      Total bytes consumed by the elements of the array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Does not include memory consumed by non-element attributes of the\n",
+      "     |      array object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)\n",
+      "     |      >>> x.nbytes\n",
+      "     |      480\n",
+      "     |      >>> np.prod(x.shape) * x.itemsize\n",
+      "     |      480\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      Number of array dimensions.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> x.ndim\n",
+      "     |      1\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.ndim\n",
+      "     |      3\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      The real part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.real\n",
+      "     |      array([ 1.        ,  0.70710678])\n",
+      "     |      >>> x.real.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.real : equivalent function\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      Tuple of array dimensions.\n",
+      "     |      \n",
+      "     |      The shape property is usually used to get the current shape of an array,\n",
+      "     |      but may also be used to reshape the array in-place by assigning a tuple of\n",
+      "     |      array dimensions to it.  As with `numpy.reshape`, one of the new shape\n",
+      "     |      dimensions can be -1, in which case its value is inferred from the size of\n",
+      "     |      the array and the remaining dimensions. Reshaping an array in-place will\n",
+      "     |      fail if a copy is required.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3, 4])\n",
+      "     |      >>> x.shape\n",
+      "     |      (4,)\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.shape\n",
+      "     |      (2, 3, 4)\n",
+      "     |      >>> y.shape = (3, 8)\n",
+      "     |      >>> y\n",
+      "     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
+      "     |      >>> y.shape = (3, 6)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: total size of new array must be unchanged\n",
+      "     |      >>> np.zeros((4,2))[::2].shape = (-1,)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      AttributeError: Incompatible shape for in-place modification. Use\n",
+      "     |      `.reshape()` to make a copy with the desired shape.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : similar function\n",
+      "     |      ndarray.reshape : similar method\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      Number of elements in the array.\n",
+      "     |      \n",
+      "     |      Equal to ``np.prod(a.shape)``, i.e., the product of the array's\n",
+      "     |      dimensions.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      `a.size` returns a standard arbitrary precision Python integer. This\n",
+      "     |      may not be the case with other methods of obtaining the same value\n",
+      "     |      (like the suggested ``np.prod(a.shape)``, which returns an instance\n",
+      "     |      of ``np.int_``), and may be relevant if the value is used further in\n",
+      "     |      calculations that may overflow a fixed size integer type.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n",
+      "     |      >>> x.size\n",
+      "     |      30\n",
+      "     |      >>> np.prod(x.shape)\n",
+      "     |      30\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      Tuple of bytes to step in each dimension when traversing an array.\n",
+      "     |      \n",
+      "     |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n",
+      "     |      is::\n",
+      "     |      \n",
+      "     |          offset = sum(np.array(i) * a.strides)\n",
+      "     |      \n",
+      "     |      A more detailed explanation of strides can be found in the\n",
+      "     |      \"ndarray.rst\" file in the NumPy reference guide.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Imagine an array of 32-bit integers (each 4 bytes)::\n",
+      "     |      \n",
+      "     |        x = np.array([[0, 1, 2, 3, 4],\n",
+      "     |                      [5, 6, 7, 8, 9]], dtype=np.int32)\n",
+      "     |      \n",
+      "     |      This array is stored in memory as 40 bytes, one after the other\n",
+      "     |      (known as a contiguous block of memory).  The strides of an array tell\n",
+      "     |      us how many bytes we have to skip in memory to move to the next position\n",
+      "     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to\n",
+      "     |      move to the next column, but 20 bytes (5 values) to get to the same\n",
+      "     |      position in the next row.  As such, the strides for the array `x` will be\n",
+      "     |      ``(20, 4)``.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.lib.stride_tricks.as_strided\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n",
+      "     |      >>> y\n",
+      "     |      array([[[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]],\n",
+      "     |             [[12, 13, 14, 15],\n",
+      "     |              [16, 17, 18, 19],\n",
+      "     |              [20, 21, 22, 23]]])\n",
+      "     |      >>> y.strides\n",
+      "     |      (48, 16, 4)\n",
+      "     |      >>> y[1,1,1]\n",
+      "     |      17\n",
+      "     |      >>> offset=sum(y.strides * np.array((1,1,1)))\n",
+      "     |      >>> offset/y.itemsize\n",
+      "     |      17\n",
+      "     |      \n",
+      "     |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n",
+      "     |      >>> x.strides\n",
+      "     |      (32, 4, 224, 1344)\n",
+      "     |      >>> i = np.array([3,5,2,2])\n",
+      "     |      >>> offset = sum(i * x.strides)\n",
+      "     |      >>> x[3,5,2,2]\n",
+      "     |      813\n",
+      "     |      >>> offset / x.itemsize\n",
+      "     |      813\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class ndenumerate(builtins.object)\n",
+      "     |  ndenumerate(arr)\n",
+      "     |  \n",
+      "     |  Multidimensional index iterator.\n",
+      "     |  \n",
+      "     |  Return an iterator yielding pairs of array coordinates and values.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  arr : ndarray\n",
+      "     |    Input array.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  ndindex, flatiter\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |  >>> for index, x in np.ndenumerate(a):\n",
+      "     |  ...     print(index, x)\n",
+      "     |  (0, 0) 1\n",
+      "     |  (0, 1) 2\n",
+      "     |  (1, 0) 3\n",
+      "     |  (1, 1) 4\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __init__(self, arr)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  __iter__(self)\n",
+      "     |  \n",
+      "     |  __next__(self)\n",
+      "     |      Standard iterator method, returns the index tuple and array value.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      coords : tuple of ints\n",
+      "     |          The indices of the current iteration.\n",
+      "     |      val : scalar\n",
+      "     |          The array element of the current iteration.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class ndindex(builtins.object)\n",
+      "     |  ndindex(*shape)\n",
+      "     |  \n",
+      "     |  An N-dimensional iterator object to index arrays.\n",
+      "     |  \n",
+      "     |  Given the shape of an array, an `ndindex` instance iterates over\n",
+      "     |  the N-dimensional index of the array. At each iteration a tuple\n",
+      "     |  of indices is returned, the last dimension is iterated over first.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  `*args` : ints\n",
+      "     |    The size of each dimension of the array.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  ndenumerate, flatiter\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> for index in np.ndindex(3, 2, 1):\n",
+      "     |  ...     print(index)\n",
+      "     |  (0, 0, 0)\n",
+      "     |  (0, 1, 0)\n",
+      "     |  (1, 0, 0)\n",
+      "     |  (1, 1, 0)\n",
+      "     |  (2, 0, 0)\n",
+      "     |  (2, 1, 0)\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __init__(self, *shape)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  __iter__(self)\n",
+      "     |  \n",
+      "     |  __next__(self)\n",
+      "     |      Standard iterator method, updates the index and returns the index\n",
+      "     |      tuple.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      val : tuple of ints\n",
+      "     |          Returns a tuple containing the indices of the current\n",
+      "     |          iteration.\n",
+      "     |  \n",
+      "     |  ndincr(self)\n",
+      "     |      Increment the multi-dimensional index by one.\n",
+      "     |      \n",
+      "     |      This method is for backward compatibility only: do not use.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class nditer(builtins.object)\n",
+      "     |  nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)\n",
+      "     |  \n",
+      "     |  Efficient multi-dimensional iterator object to iterate over arrays.\n",
+      "     |  To get started using this object, see the\n",
+      "     |  :ref:`introductory guide to array iteration <arrays.nditer>`.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  op : ndarray or sequence of array_like\n",
+      "     |      The array(s) to iterate over.\n",
+      "     |  \n",
+      "     |  flags : sequence of str, optional\n",
+      "     |        Flags to control the behavior of the iterator.\n",
+      "     |  \n",
+      "     |        * ``buffered`` enables buffering when required.\n",
+      "     |        * ``c_index`` causes a C-order index to be tracked.\n",
+      "     |        * ``f_index`` causes a Fortran-order index to be tracked.\n",
+      "     |        * ``multi_index`` causes a multi-index, or a tuple of indices\n",
+      "     |          with one per iteration dimension, to be tracked.\n",
+      "     |        * ``common_dtype`` causes all the operands to be converted to\n",
+      "     |          a common data type, with copying or buffering as necessary.\n",
+      "     |        * ``copy_if_overlap`` causes the iterator to determine if read\n",
+      "     |          operands have overlap with write operands, and make temporary\n",
+      "     |          copies as necessary to avoid overlap. False positives (needless\n",
+      "     |          copying) are possible in some cases.\n",
+      "     |        * ``delay_bufalloc`` delays allocation of the buffers until\n",
+      "     |          a reset() call is made. Allows ``allocate`` operands to\n",
+      "     |          be initialized before their values are copied into the buffers.\n",
+      "     |        * ``external_loop`` causes the ``values`` given to be\n",
+      "     |          one-dimensional arrays with multiple values instead of\n",
+      "     |          zero-dimensional arrays.\n",
+      "     |        * ``grow_inner`` allows the ``value`` array sizes to be made\n",
+      "     |          larger than the buffer size when both ``buffered`` and\n",
+      "     |          ``external_loop`` is used.\n",
+      "     |        * ``ranged`` allows the iterator to be restricted to a sub-range\n",
+      "     |          of the iterindex values.\n",
+      "     |        * ``refs_ok`` enables iteration of reference types, such as\n",
+      "     |          object arrays.\n",
+      "     |        * ``reduce_ok`` enables iteration of ``readwrite`` operands\n",
+      "     |          which are broadcasted, also known as reduction operands.\n",
+      "     |        * ``zerosize_ok`` allows `itersize` to be zero.\n",
+      "     |  op_flags : list of list of str, optional\n",
+      "     |        This is a list of flags for each operand. At minimum, one of\n",
+      "     |        ``readonly``, ``readwrite``, or ``writeonly`` must be specified.\n",
+      "     |  \n",
+      "     |        * ``readonly`` indicates the operand will only be read from.\n",
+      "     |        * ``readwrite`` indicates the operand will be read from and written to.\n",
+      "     |        * ``writeonly`` indicates the operand will only be written to.\n",
+      "     |        * ``no_broadcast`` prevents the operand from being broadcasted.\n",
+      "     |        * ``contig`` forces the operand data to be contiguous.\n",
+      "     |        * ``aligned`` forces the operand data to be aligned.\n",
+      "     |        * ``nbo`` forces the operand data to be in native byte order.\n",
+      "     |        * ``copy`` allows a temporary read-only copy if required.\n",
+      "     |        * ``updateifcopy`` allows a temporary read-write copy if required.\n",
+      "     |        * ``allocate`` causes the array to be allocated if it is None\n",
+      "     |          in the ``op`` parameter.\n",
+      "     |        * ``no_subtype`` prevents an ``allocate`` operand from using a subtype.\n",
+      "     |        * ``arraymask`` indicates that this operand is the mask to use\n",
+      "     |          for selecting elements when writing to operands with the\n",
+      "     |          'writemasked' flag set. The iterator does not enforce this,\n",
+      "     |          but when writing from a buffer back to the array, it only\n",
+      "     |          copies those elements indicated by this mask.\n",
+      "     |        * ``writemasked`` indicates that only elements where the chosen\n",
+      "     |          ``arraymask`` operand is True will be written to.\n",
+      "     |        * ``overlap_assume_elementwise`` can be used to mark operands that are\n",
+      "     |          accessed only in the iterator order, to allow less conservative\n",
+      "     |          copying when ``copy_if_overlap`` is present.\n",
+      "     |  op_dtypes : dtype or tuple of dtype(s), optional\n",
+      "     |      The required data type(s) of the operands. If copying or buffering\n",
+      "     |      is enabled, the data will be converted to/from their original types.\n",
+      "     |  order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |      Controls the iteration order. 'C' means C order, 'F' means\n",
+      "     |      Fortran order, 'A' means 'F' order if all the arrays are Fortran\n",
+      "     |      contiguous, 'C' order otherwise, and 'K' means as close to the\n",
+      "     |      order the array elements appear in memory as possible. This also\n",
+      "     |      affects the element memory order of ``allocate`` operands, as they\n",
+      "     |      are allocated to be compatible with iteration order.\n",
+      "     |      Default is 'K'.\n",
+      "     |  casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n",
+      "     |      Controls what kind of data casting may occur when making a copy\n",
+      "     |      or buffering.  Setting this to 'unsafe' is not recommended,\n",
+      "     |      as it can adversely affect accumulations.\n",
+      "     |  \n",
+      "     |      * 'no' means the data types should not be cast at all.\n",
+      "     |      * 'equiv' means only byte-order changes are allowed.\n",
+      "     |      * 'safe' means only casts which can preserve values are allowed.\n",
+      "     |      * 'same_kind' means only safe casts or casts within a kind,\n",
+      "     |        like float64 to float32, are allowed.\n",
+      "     |      * 'unsafe' means any data conversions may be done.\n",
+      "     |  op_axes : list of list of ints, optional\n",
+      "     |      If provided, is a list of ints or None for each operands.\n",
+      "     |      The list of axes for an operand is a mapping from the dimensions\n",
+      "     |      of the iterator to the dimensions of the operand. A value of\n",
+      "     |      -1 can be placed for entries, causing that dimension to be\n",
+      "     |      treated as `newaxis`.\n",
+      "     |  itershape : tuple of ints, optional\n",
+      "     |      The desired shape of the iterator. This allows ``allocate`` operands\n",
+      "     |      with a dimension mapped by op_axes not corresponding to a dimension\n",
+      "     |      of a different operand to get a value not equal to 1 for that\n",
+      "     |      dimension.\n",
+      "     |  buffersize : int, optional\n",
+      "     |      When buffering is enabled, controls the size of the temporary\n",
+      "     |      buffers. Set to 0 for the default value.\n",
+      "     |  \n",
+      "     |  Attributes\n",
+      "     |  ----------\n",
+      "     |  dtypes : tuple of dtype(s)\n",
+      "     |      The data types of the values provided in `value`. This may be\n",
+      "     |      different from the operand data types if buffering is enabled.\n",
+      "     |      Valid only before the iterator is closed.\n",
+      "     |  finished : bool\n",
+      "     |      Whether the iteration over the operands is finished or not.\n",
+      "     |  has_delayed_bufalloc : bool\n",
+      "     |      If True, the iterator was created with the ``delay_bufalloc`` flag,\n",
+      "     |      and no reset() function was called on it yet.\n",
+      "     |  has_index : bool\n",
+      "     |      If True, the iterator was created with either the ``c_index`` or\n",
+      "     |      the ``f_index`` flag, and the property `index` can be used to\n",
+      "     |      retrieve it.\n",
+      "     |  has_multi_index : bool\n",
+      "     |      If True, the iterator was created with the ``multi_index`` flag,\n",
+      "     |      and the property `multi_index` can be used to retrieve it.\n",
+      "     |  index\n",
+      "     |      When the ``c_index`` or ``f_index`` flag was used, this property\n",
+      "     |      provides access to the index. Raises a ValueError if accessed\n",
+      "     |      and ``has_index`` is False.\n",
+      "     |  iterationneedsapi : bool\n",
+      "     |      Whether iteration requires access to the Python API, for example\n",
+      "     |      if one of the operands is an object array.\n",
+      "     |  iterindex : int\n",
+      "     |      An index which matches the order of iteration.\n",
+      "     |  itersize : int\n",
+      "     |      Size of the iterator.\n",
+      "     |  itviews\n",
+      "     |      Structured view(s) of `operands` in memory, matching the reordered\n",
+      "     |      and optimized iterator access pattern. Valid only before the iterator\n",
+      "     |      is closed.\n",
+      "     |  multi_index\n",
+      "     |      When the ``multi_index`` flag was used, this property\n",
+      "     |      provides access to the index. Raises a ValueError if accessed\n",
+      "     |      accessed and ``has_multi_index`` is False.\n",
+      "     |  ndim : int\n",
+      "     |      The dimensions of the iterator.\n",
+      "     |  nop : int\n",
+      "     |      The number of iterator operands.\n",
+      "     |  operands : tuple of operand(s)\n",
+      "     |      The array(s) to be iterated over. Valid only before the iterator is\n",
+      "     |      closed.\n",
+      "     |  shape : tuple of ints\n",
+      "     |      Shape tuple, the shape of the iterator.\n",
+      "     |  value\n",
+      "     |      Value of ``operands`` at current iteration. Normally, this is a\n",
+      "     |      tuple of array scalars, but if the flag ``external_loop`` is used,\n",
+      "     |      it is a tuple of one dimensional arrays.\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  `nditer` supersedes `flatiter`.  The iterator implementation behind\n",
+      "     |  `nditer` is also exposed by the NumPy C API.\n",
+      "     |  \n",
+      "     |  The Python exposure supplies two iteration interfaces, one which follows\n",
+      "     |  the Python iterator protocol, and another which mirrors the C-style\n",
+      "     |  do-while pattern.  The native Python approach is better in most cases, but\n",
+      "     |  if you need the coordinates or index of an iterator, use the C-style pattern.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  Here is how we might write an ``iter_add`` function, using the\n",
+      "     |  Python iterator protocol:\n",
+      "     |  \n",
+      "     |  >>> def iter_add_py(x, y, out=None):\n",
+      "     |  ...     addop = np.add\n",
+      "     |  ...     it = np.nditer([x, y, out], [],\n",
+      "     |  ...                 [['readonly'], ['readonly'], ['writeonly','allocate']])\n",
+      "     |  ...     with it:\n",
+      "     |  ...         for (a, b, c) in it:\n",
+      "     |  ...             addop(a, b, out=c)\n",
+      "     |  ...     return it.operands[2]\n",
+      "     |  \n",
+      "     |  Here is the same function, but following the C-style pattern:\n",
+      "     |  \n",
+      "     |  >>> def iter_add(x, y, out=None):\n",
+      "     |  ...    addop = np.add\n",
+      "     |  ...    it = np.nditer([x, y, out], [],\n",
+      "     |  ...                [['readonly'], ['readonly'], ['writeonly','allocate']])\n",
+      "     |  ...    with it:\n",
+      "     |  ...        while not it.finished:\n",
+      "     |  ...            addop(it[0], it[1], out=it[2])\n",
+      "     |  ...            it.iternext()\n",
+      "     |  ...        return it.operands[2]\n",
+      "     |  \n",
+      "     |  Here is an example outer product function:\n",
+      "     |  \n",
+      "     |  >>> def outer_it(x, y, out=None):\n",
+      "     |  ...     mulop = np.multiply\n",
+      "     |  ...     it = np.nditer([x, y, out], ['external_loop'],\n",
+      "     |  ...             [['readonly'], ['readonly'], ['writeonly', 'allocate']],\n",
+      "     |  ...             op_axes=[list(range(x.ndim)) + [-1] * y.ndim,\n",
+      "     |  ...                      [-1] * x.ndim + list(range(y.ndim)),\n",
+      "     |  ...                      None])\n",
+      "     |  ...     with it:\n",
+      "     |  ...         for (a, b, c) in it:\n",
+      "     |  ...             mulop(a, b, out=c)\n",
+      "     |  ...         return it.operands[2]\n",
+      "     |  \n",
+      "     |  >>> a = np.arange(2)+1\n",
+      "     |  >>> b = np.arange(3)+1\n",
+      "     |  >>> outer_it(a,b)\n",
+      "     |  array([[1, 2, 3],\n",
+      "     |         [2, 4, 6]])\n",
+      "     |  \n",
+      "     |  Here is an example function which operates like a \"lambda\" ufunc:\n",
+      "     |  \n",
+      "     |  >>> def luf(lamdaexpr, *args, **kwargs):\n",
+      "     |  ...    '''luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)'''\n",
+      "     |  ...    nargs = len(args)\n",
+      "     |  ...    op = (kwargs.get('out',None),) + args\n",
+      "     |  ...    it = np.nditer(op, ['buffered','external_loop'],\n",
+      "     |  ...            [['writeonly','allocate','no_broadcast']] +\n",
+      "     |  ...                            [['readonly','nbo','aligned']]*nargs,\n",
+      "     |  ...            order=kwargs.get('order','K'),\n",
+      "     |  ...            casting=kwargs.get('casting','safe'),\n",
+      "     |  ...            buffersize=kwargs.get('buffersize',0))\n",
+      "     |  ...    while not it.finished:\n",
+      "     |  ...        it[0] = lamdaexpr(*it[1:])\n",
+      "     |  ...        it.iternext()\n",
+      "     |  ...        return it.operands[0]\n",
+      "     |  \n",
+      "     |  >>> a = np.arange(5)\n",
+      "     |  >>> b = np.ones(5)\n",
+      "     |  >>> luf(lambda i,j:i*i + j/2, a, b)\n",
+      "     |  array([  0.5,   1.5,   4.5,   9.5,  16.5])\n",
+      "     |  \n",
+      "     |  If operand flags `\"writeonly\"` or `\"readwrite\"` are used the\n",
+      "     |  operands may be views into the original data with the\n",
+      "     |  `WRITEBACKIFCOPY` flag. In this case `nditer` must be used as a\n",
+      "     |  context manager or the `nditer.close` method must be called before\n",
+      "     |  using the result. The temporary data will be written back to the\n",
+      "     |  original data when the `__exit__` function is called but not before:\n",
+      "     |  \n",
+      "     |  >>> a = np.arange(6, dtype='i4')[::-2]\n",
+      "     |  >>> with np.nditer(a, [],\n",
+      "     |  ...        [['writeonly', 'updateifcopy']],\n",
+      "     |  ...        casting='unsafe',\n",
+      "     |  ...        op_dtypes=[np.dtype('f4')]) as i:\n",
+      "     |  ...    x = i.operands[0]\n",
+      "     |  ...    x[:] = [-1, -2, -3]\n",
+      "     |  ...    # a still unchanged here\n",
+      "     |  >>> a, x\n",
+      "     |  (array([-1, -2, -3], dtype=int32), array([-1., -2., -3.], dtype=float32))\n",
+      "     |  \n",
+      "     |  It is important to note that once the iterator is exited, dangling\n",
+      "     |  references (like `x` in the example) may or may not share data with\n",
+      "     |  the original data `a`. If writeback semantics were active, i.e. if\n",
+      "     |  `x.base.flags.writebackifcopy` is `True`, then exiting the iterator\n",
+      "     |  will sever the connection between `x` and `a`, writing to `x` will\n",
+      "     |  no longer write to `a`. If writeback semantics are not active, then\n",
+      "     |  `x.data` will still point at some part of `a.data`, and writing to\n",
+      "     |  one will affect the other.\n",
+      "     |  \n",
+      "     |  Context management and the `close` method appeared in version 1.15.0.\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __enter__(...)\n",
+      "     |  \n",
+      "     |  __exit__(...)\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __init__(self, /, *args, **kwargs)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __next__(self, /)\n",
+      "     |      Implement next(self).\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  close(...)\n",
+      "     |      close()\n",
+      "     |      \n",
+      "     |      Resolve all writeback semantics in writeable operands.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.15.0\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      \n",
+      "     |      :ref:`nditer-context-manager`\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      copy()\n",
+      "     |      \n",
+      "     |      Get a copy of the iterator in its current state.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(10)\n",
+      "     |      >>> y = x + 1\n",
+      "     |      >>> it = np.nditer([x, y])\n",
+      "     |      >>> next(it)\n",
+      "     |      (array(0), array(1))\n",
+      "     |      >>> it2 = it.copy()\n",
+      "     |      >>> next(it2)\n",
+      "     |      (array(1), array(2))\n",
+      "     |  \n",
+      "     |  debug_print(...)\n",
+      "     |      debug_print()\n",
+      "     |      \n",
+      "     |      Print the current state of the `nditer` instance and debug info to stdout.\n",
+      "     |  \n",
+      "     |  enable_external_loop(...)\n",
+      "     |      enable_external_loop()\n",
+      "     |      \n",
+      "     |      When the \"external_loop\" was not used during construction, but\n",
+      "     |      is desired, this modifies the iterator to behave as if the flag\n",
+      "     |      was specified.\n",
+      "     |  \n",
+      "     |  iternext(...)\n",
+      "     |      iternext()\n",
+      "     |      \n",
+      "     |      Check whether iterations are left, and perform a single internal iteration\n",
+      "     |      without returning the result.  Used in the C-style pattern do-while\n",
+      "     |      pattern.  For an example, see `nditer`.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      iternext : bool\n",
+      "     |          Whether or not there are iterations left.\n",
+      "     |  \n",
+      "     |  remove_axis(...)\n",
+      "     |      remove_axis(i)\n",
+      "     |      \n",
+      "     |      Removes axis `i` from the iterator. Requires that the flag \"multi_index\"\n",
+      "     |      be enabled.\n",
+      "     |  \n",
+      "     |  remove_multi_index(...)\n",
+      "     |      remove_multi_index()\n",
+      "     |      \n",
+      "     |      When the \"multi_index\" flag was specified, this removes it, allowing\n",
+      "     |      the internal iteration structure to be optimized further.\n",
+      "     |  \n",
+      "     |  reset(...)\n",
+      "     |      reset()\n",
+      "     |      \n",
+      "     |      Reset the iterator to its initial state.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  dtypes\n",
+      "     |  \n",
+      "     |  finished\n",
+      "     |  \n",
+      "     |  has_delayed_bufalloc\n",
+      "     |  \n",
+      "     |  has_index\n",
+      "     |  \n",
+      "     |  has_multi_index\n",
+      "     |  \n",
+      "     |  index\n",
+      "     |  \n",
+      "     |  iterationneedsapi\n",
+      "     |  \n",
+      "     |  iterindex\n",
+      "     |  \n",
+      "     |  iterrange\n",
+      "     |  \n",
+      "     |  itersize\n",
+      "     |  \n",
+      "     |  itviews\n",
+      "     |  \n",
+      "     |  multi_index\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |  \n",
+      "     |  nop\n",
+      "     |  \n",
+      "     |  operands\n",
+      "     |      operands[`Slice`]\n",
+      "     |      \n",
+      "     |      The array(s) to be iterated over. Valid only before the iterator is closed.\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |  \n",
+      "     |  value\n",
+      "    \n",
+      "    class number(generic)\n",
+      "     |  Abstract base class of all numeric scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    object0 = class object_(generic)\n",
+      "     |  Any Python object.\n",
+      "     |  Character code: ``'O'``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      object_\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __call__(self, /, *args, **kwargs)\n",
+      "     |      Call self as a function.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __iadd__(self, value, /)\n",
+      "     |      Implement self+=value.\n",
+      "     |  \n",
+      "     |  __imul__(self, value, /)\n",
+      "     |      Implement self*=value.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class object_(generic)\n",
+      "     |  Any Python object.\n",
+      "     |  Character code: ``'O'``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      object_\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __call__(self, /, *args, **kwargs)\n",
+      "     |      Call self as a function.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __delattr__(self, name, /)\n",
+      "     |      Implement delattr(self, name).\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __iadd__(self, value, /)\n",
+      "     |      Implement self+=value.\n",
+      "     |  \n",
+      "     |  __imul__(self, value, /)\n",
+      "     |      Implement self*=value.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __setattr__(self, name, value, /)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class poly1d(builtins.object)\n",
+      "     |  poly1d(c_or_r, r=False, variable=None)\n",
+      "     |  \n",
+      "     |  A one-dimensional polynomial class.\n",
+      "     |  \n",
+      "     |  A convenience class, used to encapsulate \"natural\" operations on\n",
+      "     |  polynomials so that said operations may take on their customary\n",
+      "     |  form in code (see Examples).\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  c_or_r : array_like\n",
+      "     |      The polynomial's coefficients, in decreasing powers, or if\n",
+      "     |      the value of the second parameter is True, the polynomial's\n",
+      "     |      roots (values where the polynomial evaluates to 0).  For example,\n",
+      "     |      ``poly1d([1, 2, 3])`` returns an object that represents\n",
+      "     |      :math:`x^2 + 2x + 3`, whereas ``poly1d([1, 2, 3], True)`` returns\n",
+      "     |      one that represents :math:`(x-1)(x-2)(x-3) = x^3 - 6x^2 + 11x -6`.\n",
+      "     |  r : bool, optional\n",
+      "     |      If True, `c_or_r` specifies the polynomial's roots; the default\n",
+      "     |      is False.\n",
+      "     |  variable : str, optional\n",
+      "     |      Changes the variable used when printing `p` from `x` to `variable`\n",
+      "     |      (see Examples).\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  Construct the polynomial :math:`x^2 + 2x + 3`:\n",
+      "     |  \n",
+      "     |  >>> p = np.poly1d([1, 2, 3])\n",
+      "     |  >>> print(np.poly1d(p))\n",
+      "     |     2\n",
+      "     |  1 x + 2 x + 3\n",
+      "     |  \n",
+      "     |  Evaluate the polynomial at :math:`x = 0.5`:\n",
+      "     |  \n",
+      "     |  >>> p(0.5)\n",
+      "     |  4.25\n",
+      "     |  \n",
+      "     |  Find the roots:\n",
+      "     |  \n",
+      "     |  >>> p.r\n",
+      "     |  array([-1.+1.41421356j, -1.-1.41421356j])\n",
+      "     |  >>> p(p.r)\n",
+      "     |  array([ -4.44089210e-16+0.j,  -4.44089210e-16+0.j]) # may vary\n",
+      "     |  \n",
+      "     |  These numbers in the previous line represent (0, 0) to machine precision\n",
+      "     |  \n",
+      "     |  Show the coefficients:\n",
+      "     |  \n",
+      "     |  >>> p.c\n",
+      "     |  array([1, 2, 3])\n",
+      "     |  \n",
+      "     |  Display the order (the leading zero-coefficients are removed):\n",
+      "     |  \n",
+      "     |  >>> p.order\n",
+      "     |  2\n",
+      "     |  \n",
+      "     |  Show the coefficient of the k-th power in the polynomial\n",
+      "     |  (which is equivalent to ``p.c[-(i+1)]``):\n",
+      "     |  \n",
+      "     |  >>> p[1]\n",
+      "     |  2\n",
+      "     |  \n",
+      "     |  Polynomials can be added, subtracted, multiplied, and divided\n",
+      "     |  (returns quotient and remainder):\n",
+      "     |  \n",
+      "     |  >>> p * p\n",
+      "     |  poly1d([ 1,  4, 10, 12,  9])\n",
+      "     |  \n",
+      "     |  >>> (p**3 + 4) / p\n",
+      "     |  (poly1d([ 1.,  4., 10., 12.,  9.]), poly1d([4.]))\n",
+      "     |  \n",
+      "     |  ``asarray(p)`` gives the coefficient array, so polynomials can be\n",
+      "     |  used in all functions that accept arrays:\n",
+      "     |  \n",
+      "     |  >>> p**2 # square of polynomial\n",
+      "     |  poly1d([ 1,  4, 10, 12,  9])\n",
+      "     |  \n",
+      "     |  >>> np.square(p) # square of individual coefficients\n",
+      "     |  array([1, 4, 9])\n",
+      "     |  \n",
+      "     |  The variable used in the string representation of `p` can be modified,\n",
+      "     |  using the `variable` parameter:\n",
+      "     |  \n",
+      "     |  >>> p = np.poly1d([1,2,3], variable='z')\n",
+      "     |  >>> print(p)\n",
+      "     |     2\n",
+      "     |  1 z + 2 z + 3\n",
+      "     |  \n",
+      "     |  Construct a polynomial from its roots:\n",
+      "     |  \n",
+      "     |  >>> np.poly1d([1, 2], True)\n",
+      "     |  poly1d([ 1., -3.,  2.])\n",
+      "     |  \n",
+      "     |  This is the same polynomial as obtained by:\n",
+      "     |  \n",
+      "     |  >>> np.poly1d([1, -1]) * np.poly1d([1, -2])\n",
+      "     |  poly1d([ 1, -3,  2])\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __add__(self, other)\n",
+      "     |  \n",
+      "     |  __array__(self, t=None)\n",
+      "     |  \n",
+      "     |  __call__(self, val)\n",
+      "     |      Call self as a function.\n",
+      "     |  \n",
+      "     |  __div__(self, other)\n",
+      "     |  \n",
+      "     |  __eq__(self, other)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, val)\n",
+      "     |  \n",
+      "     |  __init__(self, c_or_r, r=False, variable=None)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  __iter__(self)\n",
+      "     |  \n",
+      "     |  __len__(self)\n",
+      "     |  \n",
+      "     |  __mul__(self, other)\n",
+      "     |  \n",
+      "     |  __ne__(self, other)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self)\n",
+      "     |  \n",
+      "     |  __pos__(self)\n",
+      "     |  \n",
+      "     |  __pow__(self, val)\n",
+      "     |  \n",
+      "     |  __radd__(self, other)\n",
+      "     |  \n",
+      "     |  __rdiv__(self, other)\n",
+      "     |  \n",
+      "     |  __repr__(self)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rmul__(self, other)\n",
+      "     |  \n",
+      "     |  __rsub__(self, other)\n",
+      "     |  \n",
+      "     |  __rtruediv__ = __rdiv__(self, other)\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, val)\n",
+      "     |  \n",
+      "     |  __str__(self)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, other)\n",
+      "     |  \n",
+      "     |  __truediv__ = __div__(self, other)\n",
+      "     |  \n",
+      "     |  deriv(self, m=1)\n",
+      "     |      Return a derivative of this polynomial.\n",
+      "     |      \n",
+      "     |      Refer to `polyder` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      polyder : equivalent function\n",
+      "     |  \n",
+      "     |  integ(self, m=1, k=0)\n",
+      "     |      Return an antiderivative (indefinite integral) of this polynomial.\n",
+      "     |      \n",
+      "     |      Refer to `polyint` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      polyint : equivalent function\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  c\n",
+      "     |      The polynomial coefficients\n",
+      "     |  \n",
+      "     |  coef\n",
+      "     |      The polynomial coefficients\n",
+      "     |  \n",
+      "     |  coefficients\n",
+      "     |      The polynomial coefficients\n",
+      "     |  \n",
+      "     |  coeffs\n",
+      "     |      The polynomial coefficients\n",
+      "     |  \n",
+      "     |  o\n",
+      "     |      The order or degree of the polynomial\n",
+      "     |  \n",
+      "     |  order\n",
+      "     |      The order or degree of the polynomial\n",
+      "     |  \n",
+      "     |  r\n",
+      "     |      The roots of the polynomial, where self(x) == 0\n",
+      "     |  \n",
+      "     |  roots\n",
+      "     |      The roots of the polynomial, where self(x) == 0\n",
+      "     |  \n",
+      "     |  variable\n",
+      "     |      The name of the polynomial variable\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class recarray(ndarray)\n",
+      "     |  recarray(shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False, order='C')\n",
+      "     |  \n",
+      "     |  Construct an ndarray that allows field access using attributes.\n",
+      "     |  \n",
+      "     |  Arrays may have a data-types containing fields, analogous\n",
+      "     |  to columns in a spread sheet.  An example is ``[(x, int), (y, float)]``,\n",
+      "     |  where each entry in the array is a pair of ``(int, float)``.  Normally,\n",
+      "     |  these attributes are accessed using dictionary lookups such as ``arr['x']``\n",
+      "     |  and ``arr['y']``.  Record arrays allow the fields to be accessed as members\n",
+      "     |  of the array, using ``arr.x`` and ``arr.y``.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  shape : tuple\n",
+      "     |      Shape of output array.\n",
+      "     |  dtype : data-type, optional\n",
+      "     |      The desired data-type.  By default, the data-type is determined\n",
+      "     |      from `formats`, `names`, `titles`, `aligned` and `byteorder`.\n",
+      "     |  formats : list of data-types, optional\n",
+      "     |      A list containing the data-types for the different columns, e.g.\n",
+      "     |      ``['i4', 'f8', 'i4']``.  `formats` does *not* support the new\n",
+      "     |      convention of using types directly, i.e. ``(int, float, int)``.\n",
+      "     |      Note that `formats` must be a list, not a tuple.\n",
+      "     |      Given that `formats` is somewhat limited, we recommend specifying\n",
+      "     |      `dtype` instead.\n",
+      "     |  names : tuple of str, optional\n",
+      "     |      The name of each column, e.g. ``('x', 'y', 'z')``.\n",
+      "     |  buf : buffer, optional\n",
+      "     |      By default, a new array is created of the given shape and data-type.\n",
+      "     |      If `buf` is specified and is an object exposing the buffer interface,\n",
+      "     |      the array will use the memory from the existing buffer.  In this case,\n",
+      "     |      the `offset` and `strides` keywords are available.\n",
+      "     |  \n",
+      "     |  Other Parameters\n",
+      "     |  ----------------\n",
+      "     |  titles : tuple of str, optional\n",
+      "     |      Aliases for column names.  For example, if `names` were\n",
+      "     |      ``('x', 'y', 'z')`` and `titles` is\n",
+      "     |      ``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then\n",
+      "     |      ``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.\n",
+      "     |  byteorder : {'<', '>', '='}, optional\n",
+      "     |      Byte-order for all fields.\n",
+      "     |  aligned : bool, optional\n",
+      "     |      Align the fields in memory as the C-compiler would.\n",
+      "     |  strides : tuple of ints, optional\n",
+      "     |      Buffer (`buf`) is interpreted according to these strides (strides\n",
+      "     |      define how many bytes each array element, row, column, etc.\n",
+      "     |      occupy in memory).\n",
+      "     |  offset : int, optional\n",
+      "     |      Start reading buffer (`buf`) from this offset onwards.\n",
+      "     |  order : {'C', 'F'}, optional\n",
+      "     |      Row-major (C-style) or column-major (Fortran-style) order.\n",
+      "     |  \n",
+      "     |  Returns\n",
+      "     |  -------\n",
+      "     |  rec : recarray\n",
+      "     |      Empty array of the given shape and type.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  rec.fromrecords : Construct a record array from data.\n",
+      "     |  record : fundamental data-type for `recarray`.\n",
+      "     |  format_parser : determine a data-type from formats, names, titles.\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  This constructor can be compared to ``empty``: it creates a new record\n",
+      "     |  array but does not fill it with data.  To create a record array from data,\n",
+      "     |  use one of the following methods:\n",
+      "     |  \n",
+      "     |  1. Create a standard ndarray and convert it to a record array,\n",
+      "     |     using ``arr.view(np.recarray)``\n",
+      "     |  2. Use the `buf` keyword.\n",
+      "     |  3. Use `np.rec.fromrecords`.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  Create an array with two fields, ``x`` and ``y``:\n",
+      "     |  \n",
+      "     |  >>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i8')])\n",
+      "     |  >>> x\n",
+      "     |  array([(1., 2), (3., 4)], dtype=[('x', '<f8'), ('y', '<i8')])\n",
+      "     |  \n",
+      "     |  >>> x['x']\n",
+      "     |  array([1., 3.])\n",
+      "     |  \n",
+      "     |  View the array as a record array:\n",
+      "     |  \n",
+      "     |  >>> x = x.view(np.recarray)\n",
+      "     |  \n",
+      "     |  >>> x.x\n",
+      "     |  array([1., 3.])\n",
+      "     |  \n",
+      "     |  >>> x.y\n",
+      "     |  array([2, 4])\n",
+      "     |  \n",
+      "     |  Create a new, empty record array:\n",
+      "     |  \n",
+      "     |  >>> np.recarray((2,),\n",
+      "     |  ... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP\n",
+      "     |  rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),\n",
+      "     |         (3471280, 1.2134086255804012e-316, 0)],\n",
+      "     |        dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      recarray\n",
+      "     |      ndarray\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __array_finalize__(self, obj)\n",
+      "     |      None.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, attr)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, indx)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __repr__(self)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, attr, val)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  field(self, attr, val=None)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False, order='C')\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      a.__array__([dtype], /) -> reference if type unchanged, copy otherwise.\n",
+      "     |      \n",
+      "     |      Returns either a new reference to self if dtype is not given or a new array\n",
+      "     |      of provided data type if dtype is different from the current dtype of the\n",
+      "     |      array.\n",
+      "     |  \n",
+      "     |  __array_function__(...)\n",
+      "     |  \n",
+      "     |  __array_prepare__(...)\n",
+      "     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n",
+      "     |  \n",
+      "     |  __array_ufunc__(...)\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |      a.__copy__()\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.copy` is called on an array. Returns a copy of the array.\n",
+      "     |      \n",
+      "     |      Equivalent to ``a.copy(order='K')``.\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |      a.__deepcopy__(memo, /) -> Deep copy of array.\n",
+      "     |      \n",
+      "     |      Used if :func:`copy.deepcopy` is called on an array.\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      Default object formatter.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __iadd__(self, value, /)\n",
+      "     |      Return self+=value.\n",
+      "     |  \n",
+      "     |  __iand__(self, value, /)\n",
+      "     |      Return self&=value.\n",
+      "     |  \n",
+      "     |  __ifloordiv__(self, value, /)\n",
+      "     |      Return self//=value.\n",
+      "     |  \n",
+      "     |  __ilshift__(self, value, /)\n",
+      "     |      Return self<<=value.\n",
+      "     |  \n",
+      "     |  __imatmul__(self, value, /)\n",
+      "     |      Return self@=value.\n",
+      "     |  \n",
+      "     |  __imod__(self, value, /)\n",
+      "     |      Return self%=value.\n",
+      "     |  \n",
+      "     |  __imul__(self, value, /)\n",
+      "     |      Return self*=value.\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __ior__(self, value, /)\n",
+      "     |      Return self|=value.\n",
+      "     |  \n",
+      "     |  __ipow__(self, value, /)\n",
+      "     |      Return self**=value.\n",
+      "     |  \n",
+      "     |  __irshift__(self, value, /)\n",
+      "     |      Return self>>=value.\n",
+      "     |  \n",
+      "     |  __isub__(self, value, /)\n",
+      "     |      Return self-=value.\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __itruediv__(self, value, /)\n",
+      "     |      Return self/=value.\n",
+      "     |  \n",
+      "     |  __ixor__(self, value, /)\n",
+      "     |      Return self^=value.\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __matmul__(self, value, /)\n",
+      "     |      Return self@value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      a.__reduce__()\n",
+      "     |      \n",
+      "     |      For pickling.\n",
+      "     |  \n",
+      "     |  __reduce_ex__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmatmul__(self, value, /)\n",
+      "     |      Return value@self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |      a.__setstate__(state, /)\n",
+      "     |      \n",
+      "     |      For unpickling.\n",
+      "     |      \n",
+      "     |      The `state` argument must be a sequence that contains the following\n",
+      "     |      elements:\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      version : int\n",
+      "     |          optional pickle version. If omitted defaults to 0.\n",
+      "     |      shape : tuple\n",
+      "     |      dtype : data-type\n",
+      "     |      isFortran : bool\n",
+      "     |      rawdata : string or list\n",
+      "     |          a binary string with the data (or a list if 'a' is an object array)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      a.all(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if all elements evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.all` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.all : equivalent function\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      a.any(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns True if any of the elements of `a` evaluate to True.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.any` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.any : equivalent function\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      a.argmax(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the maximum values along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmax : equivalent function\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      a.argmin(axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return indices of the minimum values along the given axis of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argmin` for detailed documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argmin : equivalent function\n",
+      "     |  \n",
+      "     |  argpartition(...)\n",
+      "     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would partition this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argpartition` for full documentation.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argpartition : equivalent function\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      a.argsort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Returns the indices that would sort this array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.argsort` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.argsort : equivalent function\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n",
+      "     |      \n",
+      "     |      Copy of the array, cast to a specified type.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          Typecode or data-type to which the array is cast.\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout order of the result.\n",
+      "     |          'C' means C order, 'F' means Fortran order, 'A'\n",
+      "     |          means 'F' order if all the arrays are Fortran contiguous,\n",
+      "     |          'C' order otherwise, and 'K' means as close to the\n",
+      "     |          order the array elements appear in memory as possible.\n",
+      "     |          Default is 'K'.\n",
+      "     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n",
+      "     |          Controls what kind of data casting may occur. Defaults to 'unsafe'\n",
+      "     |          for backwards compatibility.\n",
+      "     |      \n",
+      "     |            * 'no' means the data types should not be cast at all.\n",
+      "     |            * 'equiv' means only byte-order changes are allowed.\n",
+      "     |            * 'safe' means only casts which can preserve values are allowed.\n",
+      "     |            * 'same_kind' means only safe casts or casts within a kind,\n",
+      "     |              like float64 to float32, are allowed.\n",
+      "     |            * 'unsafe' means any data conversions may be done.\n",
+      "     |      subok : bool, optional\n",
+      "     |          If True, then sub-classes will be passed-through (default), otherwise\n",
+      "     |          the returned array will be forced to be a base-class array.\n",
+      "     |      copy : bool, optional\n",
+      "     |          By default, astype always returns a newly allocated array. If this\n",
+      "     |          is set to false, and the `dtype`, `order`, and `subok`\n",
+      "     |          requirements are satisfied, the input array is returned instead\n",
+      "     |          of a copy.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      arr_t : ndarray\n",
+      "     |          Unless `copy` is False and the other conditions for returning the input\n",
+      "     |          array are satisfied (see description for `copy` input parameter), `arr_t`\n",
+      "     |          is a new array of the same shape as the input array, with dtype, order\n",
+      "     |          given by `dtype`, `order`.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      .. versionchanged:: 1.17.0\n",
+      "     |         Casting between a simple data type and a structured one is possible only\n",
+      "     |         for \"unsafe\" casting.  Casting to multiple fields is allowed, but\n",
+      "     |         casting from multiple fields is not.\n",
+      "     |      \n",
+      "     |      .. versionchanged:: 1.9.0\n",
+      "     |         Casting from numeric to string types in 'safe' casting mode requires\n",
+      "     |         that the string dtype length is long enough to store the max\n",
+      "     |         integer/float value converted.\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ComplexWarning\n",
+      "     |          When casting from complex to float or int. To avoid this,\n",
+      "     |          one should use ``a.real.astype(t)``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 2.5])\n",
+      "     |      >>> x\n",
+      "     |      array([1. ,  2. ,  2.5])\n",
+      "     |      \n",
+      "     |      >>> x.astype(int)\n",
+      "     |      array([1, 2, 2])\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      a.byteswap(inplace=False)\n",
+      "     |      \n",
+      "     |      Swap the bytes of the array elements\n",
+      "     |      \n",
+      "     |      Toggle between low-endian and big-endian data representation by\n",
+      "     |      returning a byteswapped array, optionally swapped in-place.\n",
+      "     |      Arrays of byte-strings are not swapped. The real and imaginary\n",
+      "     |      parts of a complex number are swapped individually.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      inplace : bool, optional\n",
+      "     |          If ``True``, swap bytes in-place, default is ``False``.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          The byteswapped array. If `inplace` is ``True``, this is\n",
+      "     |          a view to self.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x1', '0x100', '0x2233']\n",
+      "     |      >>> A.byteswap(inplace=True)\n",
+      "     |      array([  256,     1, 13090], dtype=int16)\n",
+      "     |      >>> list(map(hex, A))\n",
+      "     |      ['0x100', '0x1', '0x3322']\n",
+      "     |      \n",
+      "     |      Arrays of byte-strings are not swapped\n",
+      "     |      \n",
+      "     |      >>> A = np.array([b'ceg', b'fac'])\n",
+      "     |      >>> A.byteswap()\n",
+      "     |      array([b'ceg', b'fac'], dtype='|S3')\n",
+      "     |      \n",
+      "     |      ``A.newbyteorder().byteswap()`` produces an array with the same values\n",
+      "     |        but different representation in memory\n",
+      "     |      \n",
+      "     |      >>> A = np.array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,\n",
+      "     |             0, 0], dtype=uint8)\n",
+      "     |      >>> A.newbyteorder().byteswap(inplace=True)\n",
+      "     |      array([1, 2, 3])\n",
+      "     |      >>> A.view(np.uint8)\n",
+      "     |      array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,\n",
+      "     |             0, 3], dtype=uint8)\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      a.choose(choices, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Use an index array to construct a new array from a set of choices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.choose` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.choose : equivalent function\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      a.clip(min=None, max=None, out=None, **kwargs)\n",
+      "     |      \n",
+      "     |      Return an array whose values are limited to ``[min, max]``.\n",
+      "     |      One of max or min must be given.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.clip` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.clip : equivalent function\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      a.compress(condition, axis=None, out=None)\n",
+      "     |      \n",
+      "     |      Return selected slices of this array along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.compress` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.compress : equivalent function\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |      a.conj()\n",
+      "     |      \n",
+      "     |      Complex-conjugate all elements.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      a.conjugate()\n",
+      "     |      \n",
+      "     |      Return the complex conjugate, element-wise.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.conjugate` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.conjugate : equivalent function\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      a.copy(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          Controls the memory layout of the copy. 'C' means C-order,\n",
+      "     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n",
+      "     |          'C' otherwise. 'K' means match the layout of `a` as closely\n",
+      "     |          as possible. (Note that this function and :func:`numpy.copy` are very\n",
+      "     |          similar, but have different default values for their order=\n",
+      "     |          arguments.)\n",
+      "     |      \n",
+      "     |      See also\n",
+      "     |      --------\n",
+      "     |      numpy.copy\n",
+      "     |      numpy.copyto\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n",
+      "     |      \n",
+      "     |      >>> y = x.copy()\n",
+      "     |      \n",
+      "     |      >>> x.fill(0)\n",
+      "     |      \n",
+      "     |      >>> x\n",
+      "     |      array([[0, 0, 0],\n",
+      "     |             [0, 0, 0]])\n",
+      "     |      \n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      \n",
+      "     |      >>> y.flags['C_CONTIGUOUS']\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      a.cumprod(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative product of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumprod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumprod : equivalent function\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      a.cumsum(axis=None, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the cumulative sum of the elements along the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.cumsum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.cumsum : equivalent function\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      a.diagonal(offset=0, axis1=0, axis2=1)\n",
+      "     |      \n",
+      "     |      Return specified diagonals. In NumPy 1.9 the returned array is a\n",
+      "     |      read-only view instead of a copy as in previous NumPy versions.  In\n",
+      "     |      a future version the read-only restriction will be removed.\n",
+      "     |      \n",
+      "     |      Refer to :func:`numpy.diagonal` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.diagonal : equivalent function\n",
+      "     |  \n",
+      "     |  dot(...)\n",
+      "     |      a.dot(b, out=None)\n",
+      "     |      \n",
+      "     |      Dot product of two arrays.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.dot` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dot : equivalent function\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.eye(2)\n",
+      "     |      >>> b = np.ones((2, 2)) * 2\n",
+      "     |      >>> a.dot(b)\n",
+      "     |      array([[2.,  2.],\n",
+      "     |             [2.,  2.]])\n",
+      "     |      \n",
+      "     |      This array method can be conveniently chained:\n",
+      "     |      \n",
+      "     |      >>> a.dot(b).dot(b)\n",
+      "     |      array([[8.,  8.],\n",
+      "     |             [8.,  8.]])\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      a.dump(file)\n",
+      "     |      \n",
+      "     |      Dump a pickle of the array to the specified file.\n",
+      "     |      The array can be read back with pickle.load or numpy.load.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      file : str or Path\n",
+      "     |          A string naming the dump file.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      a.dumps()\n",
+      "     |      \n",
+      "     |      Returns the pickle of the array as a string.\n",
+      "     |      pickle.loads or numpy.loads will convert the string back to an array.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      a.fill(value)\n",
+      "     |      \n",
+      "     |      Fill the array with a scalar value.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      value : scalar\n",
+      "     |          All elements of `a` will be assigned this value.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([1, 2])\n",
+      "     |      >>> a.fill(0)\n",
+      "     |      >>> a\n",
+      "     |      array([0, 0])\n",
+      "     |      >>> a = np.empty(2)\n",
+      "     |      >>> a.fill(1)\n",
+      "     |      >>> a\n",
+      "     |      array([1.,  1.])\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      a.flatten(order='C')\n",
+      "     |      \n",
+      "     |      Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', 'A', 'K'}, optional\n",
+      "     |          'C' means to flatten in row-major (C-style) order.\n",
+      "     |          'F' means to flatten in column-major (Fortran-\n",
+      "     |          style) order. 'A' means to flatten in column-major\n",
+      "     |          order if `a` is Fortran *contiguous* in memory,\n",
+      "     |          row-major order otherwise. 'K' means to flatten\n",
+      "     |          `a` in the order the elements occur in memory.\n",
+      "     |          The default is 'C'.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : ndarray\n",
+      "     |          A copy of the input array, flattened to one dimension.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ravel : Return a flattened array.\n",
+      "     |      flat : A 1-D flat iterator over the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,2], [3,4]])\n",
+      "     |      >>> a.flatten()\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |      >>> a.flatten('F')\n",
+      "     |      array([1, 3, 2, 4])\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      a.getfield(dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Returns a field of the given array as a certain type.\n",
+      "     |      \n",
+      "     |      A field is a view of the array data with a given data-type. The values in\n",
+      "     |      the view are determined by the given type and the offset into the current\n",
+      "     |      array in bytes. The offset needs to be such that the view dtype fits in the\n",
+      "     |      array dtype; for example an array of dtype complex128 has 16-byte elements.\n",
+      "     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n",
+      "     |      between 0 and 12 bytes.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : str or dtype\n",
+      "     |          The data type of the view. The dtype size of the view can not be larger\n",
+      "     |          than that of the array itself.\n",
+      "     |      offset : int\n",
+      "     |          Number of bytes to skip before beginning the element view.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.diag([1.+1.j]*2)\n",
+      "     |      >>> x[1, 1] = 2 + 4.j\n",
+      "     |      >>> x\n",
+      "     |      array([[1.+1.j,  0.+0.j],\n",
+      "     |             [0.+0.j,  2.+4.j]])\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  2.]])\n",
+      "     |      \n",
+      "     |      By choosing an offset of 8 bytes we can select the complex part of the\n",
+      "     |      array for our view:\n",
+      "     |      \n",
+      "     |      >>> x.getfield(np.float64, offset=8)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  4.]])\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      a.item(*args)\n",
+      "     |      \n",
+      "     |      Copy an element of an array to a standard Python scalar and return it.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments (variable number and type)\n",
+      "     |      \n",
+      "     |          * none: in this case, the method only works for arrays\n",
+      "     |            with one element (`a.size == 1`), which element is\n",
+      "     |            copied into a standard Python scalar object and returned.\n",
+      "     |      \n",
+      "     |          * int_type: this argument is interpreted as a flat index into\n",
+      "     |            the array, specifying which element to copy and return.\n",
+      "     |      \n",
+      "     |          * tuple of int_types: functions as does a single int_type argument,\n",
+      "     |            except that the argument is interpreted as an nd-index into the\n",
+      "     |            array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      z : Standard Python scalar object\n",
+      "     |          A copy of the specified element of the array as a suitable\n",
+      "     |          Python scalar\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      When the data type of `a` is longdouble or clongdouble, item() returns\n",
+      "     |      a scalar array object because there is no available Python scalar that\n",
+      "     |      would not lose information. Void arrays return a buffer object for item(),\n",
+      "     |      unless fields are defined, in which case a tuple is returned.\n",
+      "     |      \n",
+      "     |      `item` is very similar to a[args], except, instead of an array scalar,\n",
+      "     |      a standard Python scalar is returned. This can be useful for speeding up\n",
+      "     |      access to elements of the array and doing arithmetic on elements of the\n",
+      "     |      array using Python's optimized math.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.item(3)\n",
+      "     |      1\n",
+      "     |      >>> x.item(7)\n",
+      "     |      0\n",
+      "     |      >>> x.item((0, 1))\n",
+      "     |      2\n",
+      "     |      >>> x.item((2, 2))\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      a.itemset(*args)\n",
+      "     |      \n",
+      "     |      Insert scalar into an array (scalar is cast to array's dtype, if possible)\n",
+      "     |      \n",
+      "     |      There must be at least 1 argument, and define the last argument\n",
+      "     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster\n",
+      "     |      than ``a[args] = item``.  The item should be a scalar value and `args`\n",
+      "     |      must select a single item in the array `a`.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      \\*args : Arguments\n",
+      "     |          If one argument: a scalar, only used in case `a` is of size 1.\n",
+      "     |          If two arguments: the last argument is the value to be set\n",
+      "     |          and must be a scalar, the first argument specifies a single array\n",
+      "     |          element location. It is either an int or a tuple.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Compared to indexing syntax, `itemset` provides some speed increase\n",
+      "     |      for placing a scalar into a particular location in an `ndarray`,\n",
+      "     |      if you must do this.  However, generally this is discouraged:\n",
+      "     |      among other problems, it complicates the appearance of the code.\n",
+      "     |      Also, when using `itemset` (and `item`) inside a loop, be sure\n",
+      "     |      to assign the methods to a local variable to avoid the attribute\n",
+      "     |      look-up at each loop iteration.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.random.seed(123)\n",
+      "     |      >>> x = np.random.randint(9, size=(3, 3))\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 3, 6],\n",
+      "     |             [1, 0, 1]])\n",
+      "     |      >>> x.itemset(4, 0)\n",
+      "     |      >>> x.itemset((2, 2), 9)\n",
+      "     |      >>> x\n",
+      "     |      array([[2, 2, 6],\n",
+      "     |             [1, 0, 6],\n",
+      "     |             [1, 0, 9]])\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      a.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the maximum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amax` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amax : equivalent function\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      a.mean(axis=None, dtype=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the average of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.mean` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.mean : equivalent function\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      a.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Return the minimum along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.amin` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.amin : equivalent function\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      arr.newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return the array with the same data viewed with a different byte order.\n",
+      "     |      \n",
+      "     |      Equivalent to::\n",
+      "     |      \n",
+      "     |          arr.view(arr.dtype.newbytorder(new_order))\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the array data\n",
+      "     |      type.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : string, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          below. `new_order` codes can be any of:\n",
+      "     |      \n",
+      "     |          * 'S' - swap dtype from current to opposite endian\n",
+      "     |          * {'<', 'L'} - little endian\n",
+      "     |          * {'>', 'B'} - big endian\n",
+      "     |          * {'=', 'N'} - native order\n",
+      "     |          * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |          The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_arr : array\n",
+      "     |          New array object with the dtype reflecting given change to the\n",
+      "     |          byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      a.nonzero()\n",
+      "     |      \n",
+      "     |      Return the indices of the elements that are non-zero.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.nonzero` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.nonzero : equivalent function\n",
+      "     |  \n",
+      "     |  partition(...)\n",
+      "     |      a.partition(kth, axis=-1, kind='introselect', order=None)\n",
+      "     |      \n",
+      "     |      Rearranges the elements in the array in such a way that the value of the\n",
+      "     |      element in kth position is in the position it would be in a sorted array.\n",
+      "     |      All elements smaller than the kth element are moved before this element and\n",
+      "     |      all equal or greater are moved behind it. The ordering of the elements in\n",
+      "     |      the two partitions is undefined.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      kth : int or sequence of ints\n",
+      "     |          Element index to partition by. The kth element value will be in its\n",
+      "     |          final sorted position and all smaller elements will be moved before it\n",
+      "     |          and all equal or greater elements behind it.\n",
+      "     |          The order of all elements in the partitions is undefined.\n",
+      "     |          If provided with a sequence of kth it will partition all elements\n",
+      "     |          indexed by kth of them into their sorted position at once.\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'introselect'}, optional\n",
+      "     |          Selection algorithm. Default is 'introselect'.\n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc. A single field can\n",
+      "     |          be specified as a string, and not all fields need to be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.partition : Return a parititioned copy of an array.\n",
+      "     |      argpartition : Indirect partition.\n",
+      "     |      sort : Full sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See ``np.partition`` for notes on the different algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([3, 4, 2, 1])\n",
+      "     |      >>> a.partition(3)\n",
+      "     |      >>> a\n",
+      "     |      array([2, 1, 3, 4])\n",
+      "     |      \n",
+      "     |      >>> a.partition((1, 3))\n",
+      "     |      >>> a\n",
+      "     |      array([1, 2, 3, 4])\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)\n",
+      "     |      \n",
+      "     |      Return the product of the array elements over the given axis\n",
+      "     |      \n",
+      "     |      Refer to `numpy.prod` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.prod : equivalent function\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      a.ptp(axis=None, out=None, keepdims=False)\n",
+      "     |      \n",
+      "     |      Peak to peak (maximum - minimum) value along a given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ptp` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ptp : equivalent function\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      a.put(indices, values, mode='raise')\n",
+      "     |      \n",
+      "     |      Set ``a.flat[n] = values[n]`` for all `n` in indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.put` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.put : equivalent function\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      a.ravel([order])\n",
+      "     |      \n",
+      "     |      Return a flattened array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.ravel` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ravel : equivalent function\n",
+      "     |      \n",
+      "     |      ndarray.flat : a flat iterator on the array.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      a.repeat(repeats, axis=None)\n",
+      "     |      \n",
+      "     |      Repeat elements of an array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.repeat` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.repeat : equivalent function\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      a.reshape(shape, order='C')\n",
+      "     |      \n",
+      "     |      Returns an array containing the same data with a new shape.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.reshape` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : equivalent function\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Unlike the free function `numpy.reshape`, this method on `ndarray` allows\n",
+      "     |      the elements of the shape parameter to be passed in as separate arguments.\n",
+      "     |      For example, ``a.reshape(10, 11)`` is equivalent to\n",
+      "     |      ``a.reshape((10, 11))``.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      a.resize(new_shape, refcheck=True)\n",
+      "     |      \n",
+      "     |      Change shape and size of array in-place.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_shape : tuple of ints, or `n` ints\n",
+      "     |          Shape of resized array.\n",
+      "     |      refcheck : bool, optional\n",
+      "     |          If False, reference count will not be checked. Default is True.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Raises\n",
+      "     |      ------\n",
+      "     |      ValueError\n",
+      "     |          If `a` does not own its own data or references or views to it exist,\n",
+      "     |          and the data memory must be changed.\n",
+      "     |          PyPy only: will always raise if the data memory must be changed, since\n",
+      "     |          there is no reliable way to determine if references or views to it\n",
+      "     |          exist.\n",
+      "     |      \n",
+      "     |      SystemError\n",
+      "     |          If the `order` keyword argument is specified. This behaviour is a\n",
+      "     |          bug in NumPy.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      resize : Return a new array with the specified shape.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This reallocates space for the data area if necessary.\n",
+      "     |      \n",
+      "     |      Only contiguous arrays (data elements consecutive in memory) can be\n",
+      "     |      resized.\n",
+      "     |      \n",
+      "     |      The purpose of the reference count check is to make sure you\n",
+      "     |      do not use this array as a buffer for another Python object and then\n",
+      "     |      reallocate the memory. However, reference counts can increase in\n",
+      "     |      other ways so if you are sure that you have not shared the memory\n",
+      "     |      for this array with another Python object, then you may safely set\n",
+      "     |      `refcheck` to False.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      Shrinking an array: array is flattened (in the order that the data are\n",
+      "     |      stored in memory), resized, and reshaped:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='C')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [1]])\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[0, 1], [2, 3]], order='F')\n",
+      "     |      >>> a.resize((2, 1))\n",
+      "     |      >>> a\n",
+      "     |      array([[0],\n",
+      "     |             [2]])\n",
+      "     |      \n",
+      "     |      Enlarging an array: as above, but missing entries are filled with zeros:\n",
+      "     |      \n",
+      "     |      >>> b = np.array([[0, 1], [2, 3]])\n",
+      "     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n",
+      "     |      >>> b\n",
+      "     |      array([[0, 1, 2],\n",
+      "     |             [3, 0, 0]])\n",
+      "     |      \n",
+      "     |      Referencing an array prevents resizing...\n",
+      "     |      \n",
+      "     |      >>> c = a\n",
+      "     |      >>> a.resize((1, 1))\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |      ...\n",
+      "     |      ValueError: cannot resize an array that references or is referenced ...\n",
+      "     |      \n",
+      "     |      Unless `refcheck` is False:\n",
+      "     |      \n",
+      "     |      >>> a.resize((1, 1), refcheck=False)\n",
+      "     |      >>> a\n",
+      "     |      array([[0]])\n",
+      "     |      >>> c\n",
+      "     |      array([[0]])\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      a.round(decimals=0, out=None)\n",
+      "     |      \n",
+      "     |      Return `a` with each element rounded to the given number of decimals.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.around` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.around : equivalent function\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      a.searchsorted(v, side='left', sorter=None)\n",
+      "     |      \n",
+      "     |      Find indices where elements of v should be inserted in a to maintain order.\n",
+      "     |      \n",
+      "     |      For full documentation, see `numpy.searchsorted`\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.searchsorted : equivalent function\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      a.setfield(val, dtype, offset=0)\n",
+      "     |      \n",
+      "     |      Put a value into a specified place in a field defined by a data-type.\n",
+      "     |      \n",
+      "     |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n",
+      "     |      bytes into the field.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      val : object\n",
+      "     |          Value to be placed in field.\n",
+      "     |      dtype : dtype object\n",
+      "     |          Data-type of the field in which to place `val`.\n",
+      "     |      offset : int, optional\n",
+      "     |          The number of bytes into the field at which to place `val`.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      getfield\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.eye(3)\n",
+      "     |      >>> x.getfield(np.float64)\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |      >>> x.setfield(3, np.int32)\n",
+      "     |      >>> x.getfield(np.int32)\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3],\n",
+      "     |             [3, 3, 3]], dtype=int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.0e+000, 1.5e-323, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.0e+000, 1.5e-323],\n",
+      "     |             [1.5e-323, 1.5e-323, 1.0e+000]])\n",
+      "     |      >>> x.setfield(np.eye(3), np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[1.,  0.,  0.],\n",
+      "     |             [0.,  1.,  0.],\n",
+      "     |             [0.,  0.,  1.]])\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      a.setflags(write=None, align=None, uic=None)\n",
+      "     |      \n",
+      "     |      Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),\n",
+      "     |      respectively.\n",
+      "     |      \n",
+      "     |      These Boolean-valued flags affect how numpy interprets the memory\n",
+      "     |      area used by `a` (see Notes below). The ALIGNED flag can only\n",
+      "     |      be set to True if the data is actually aligned according to the type.\n",
+      "     |      The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set\n",
+      "     |      to True. The flag WRITEABLE can only be set to True if the array owns its\n",
+      "     |      own memory, or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |      interface, or is a string. (The exception for string is made so that\n",
+      "     |      unpickling can be done without copying memory.)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      write : bool, optional\n",
+      "     |          Describes whether or not `a` can be written to.\n",
+      "     |      align : bool, optional\n",
+      "     |          Describes whether or not `a` is aligned properly for its type.\n",
+      "     |      uic : bool, optional\n",
+      "     |          Describes whether or not `a` is a copy of another \"base\" array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Array flags provide information about how the memory area used\n",
+      "     |      for the array is to be interpreted. There are 7 Boolean flags\n",
+      "     |      in use, only four of which can be changed by the user:\n",
+      "     |      WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n",
+      "     |      \n",
+      "     |      WRITEABLE (W) the data area can be written to;\n",
+      "     |      \n",
+      "     |      ALIGNED (A) the data and strides are aligned appropriately for the hardware\n",
+      "     |      (as determined by the compiler);\n",
+      "     |      \n",
+      "     |      UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;\n",
+      "     |      \n",
+      "     |      WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced\n",
+      "     |      by .base). When the C-API function PyArray_ResolveWritebackIfCopy is\n",
+      "     |      called, the base array will be updated with the contents of this array.\n",
+      "     |      \n",
+      "     |      All flags can be accessed using the single (upper case) letter as well\n",
+      "     |      as the full name.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.array([[3, 1, 7],\n",
+      "     |      ...               [2, 0, 0],\n",
+      "     |      ...               [8, 5, 9]])\n",
+      "     |      >>> y\n",
+      "     |      array([[3, 1, 7],\n",
+      "     |             [2, 0, 0],\n",
+      "     |             [8, 5, 9]])\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : True\n",
+      "     |        ALIGNED : True\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(write=0, align=0)\n",
+      "     |      >>> y.flags\n",
+      "     |        C_CONTIGUOUS : True\n",
+      "     |        F_CONTIGUOUS : False\n",
+      "     |        OWNDATA : True\n",
+      "     |        WRITEABLE : False\n",
+      "     |        ALIGNED : False\n",
+      "     |        WRITEBACKIFCOPY : False\n",
+      "     |        UPDATEIFCOPY : False\n",
+      "     |      >>> y.setflags(uic=1)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: cannot set WRITEBACKIFCOPY flag to True\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      a.sort(axis=-1, kind=None, order=None)\n",
+      "     |      \n",
+      "     |      Sort an array in-place. Refer to `numpy.sort` for full documentation.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axis : int, optional\n",
+      "     |          Axis along which to sort. Default is -1, which means sort along the\n",
+      "     |          last axis.\n",
+      "     |      kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n",
+      "     |          Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n",
+      "     |          and 'mergesort' use timsort under the covers and, in general, the\n",
+      "     |          actual implementation will vary with datatype. The 'mergesort' option\n",
+      "     |          is retained for backwards compatibility.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.15.0.\n",
+      "     |             The 'stable' option was added.\n",
+      "     |      \n",
+      "     |      order : str or list of str, optional\n",
+      "     |          When `a` is an array with fields defined, this argument specifies\n",
+      "     |          which fields to compare first, second, etc.  A single field can\n",
+      "     |          be specified as a string, and not all fields need be specified,\n",
+      "     |          but unspecified fields will still be used, in the order in which\n",
+      "     |          they come up in the dtype, to break ties.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sort : Return a sorted copy of an array.\n",
+      "     |      numpy.argsort : Indirect sort.\n",
+      "     |      numpy.lexsort : Indirect stable sort on multiple keys.\n",
+      "     |      numpy.searchsorted : Find elements in sorted array.\n",
+      "     |      numpy.partition: Partial sort.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      See `numpy.sort` for notes on the different sorting algorithms.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1,4], [3,1]])\n",
+      "     |      >>> a.sort(axis=1)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 4],\n",
+      "     |             [1, 3]])\n",
+      "     |      >>> a.sort(axis=0)\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 3],\n",
+      "     |             [1, 4]])\n",
+      "     |      \n",
+      "     |      Use the `order` keyword to specify a field to use when sorting a\n",
+      "     |      structured array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n",
+      "     |      >>> a.sort(order='y')\n",
+      "     |      >>> a\n",
+      "     |      array([(b'c', 1), (b'a', 2)],\n",
+      "     |            dtype=[('x', 'S1'), ('y', '<i8')])\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      a.squeeze(axis=None)\n",
+      "     |      \n",
+      "     |      Remove single-dimensional entries from the shape of `a`.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.squeeze` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.squeeze : equivalent function\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the standard deviation of the array elements along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.std` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.std : equivalent function\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)\n",
+      "     |      \n",
+      "     |      Return the sum of the array elements over the given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.sum` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.sum : equivalent function\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      a.swapaxes(axis1, axis2)\n",
+      "     |      \n",
+      "     |      Return a view of the array with `axis1` and `axis2` interchanged.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.swapaxes` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.swapaxes : equivalent function\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      a.take(indices, axis=None, out=None, mode='raise')\n",
+      "     |      \n",
+      "     |      Return an array formed from the elements of `a` at the given indices.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.take` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.take : equivalent function\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |      a.tobytes(order='C')\n",
+      "     |      \n",
+      "     |      Construct Python bytes containing the raw data bytes in the array.\n",
+      "     |      \n",
+      "     |      Constructs Python bytes showing a copy of the raw contents of\n",
+      "     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',\n",
+      "     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n",
+      "     |      unless the F_CONTIGUOUS flag in the array is set, in which case it\n",
+      "     |      means 'Fortran' order.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.9.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      order : {'C', 'F', None}, optional\n",
+      "     |          Order of the data for multidimensional arrays:\n",
+      "     |          C, Fortran, or the same as for the original array.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      s : bytes\n",
+      "     |          Python bytes exhibiting a copy of `a`'s raw data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype='<u2')\n",
+      "     |      >>> x.tobytes()\n",
+      "     |      b'\\x00\\x00\\x01\\x00\\x02\\x00\\x03\\x00'\n",
+      "     |      >>> x.tobytes('C') == x.tobytes()\n",
+      "     |      True\n",
+      "     |      >>> x.tobytes('F')\n",
+      "     |      b'\\x00\\x00\\x02\\x00\\x01\\x00\\x03\\x00'\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      a.tofile(fid, sep=\"\", format=\"%s\")\n",
+      "     |      \n",
+      "     |      Write array to a file as text or binary (default).\n",
+      "     |      \n",
+      "     |      Data is always written in 'C' order, independent of the order of `a`.\n",
+      "     |      The data produced by this method can be recovered using the function\n",
+      "     |      fromfile().\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      fid : file or str or Path\n",
+      "     |          An open file object, or a string containing a filename.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.17.0\n",
+      "     |              `pathlib.Path` objects are now accepted.\n",
+      "     |      \n",
+      "     |      sep : str\n",
+      "     |          Separator between array items for text output.\n",
+      "     |          If \"\" (empty), a binary file is written, equivalent to\n",
+      "     |          ``file.write(a.tobytes())``.\n",
+      "     |      format : str\n",
+      "     |          Format string for text file output.\n",
+      "     |          Each entry in the array is formatted to text by first converting\n",
+      "     |          it to the closest Python type, and then using \"format\" % item.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      This is a convenience function for quick storage of array data.\n",
+      "     |      Information on endianness and precision is lost, so this method is not a\n",
+      "     |      good choice for files intended to archive data or transport data between\n",
+      "     |      machines with different endianness. Some of these problems can be overcome\n",
+      "     |      by outputting the data as text files, at the expense of speed and file\n",
+      "     |      size.\n",
+      "     |      \n",
+      "     |      When fid is a file object, array contents are directly written to the\n",
+      "     |      file, bypassing the file object's ``write`` method. As a result, tofile\n",
+      "     |      cannot be used with files objects supporting compression (e.g., GzipFile)\n",
+      "     |      or file-like objects that do not support ``fileno()`` (e.g., BytesIO).\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      a.tolist()\n",
+      "     |      \n",
+      "     |      Return the array as an ``a.ndim``-levels deep nested list of Python scalars.\n",
+      "     |      \n",
+      "     |      Return a copy of the array data as a (nested) Python list.\n",
+      "     |      Data items are converted to the nearest compatible builtin Python type, via\n",
+      "     |      the `~numpy.ndarray.item` function.\n",
+      "     |      \n",
+      "     |      If ``a.ndim`` is 0, then since the depth of the nested list is 0, it will\n",
+      "     |      not be a list at all, but a simple Python scalar.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      none\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      y : object, or list of object, or list of list of object, or ...\n",
+      "     |          The possibly nested list of array elements.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The array may be recreated via ``a = np.array(a.tolist())``, although this\n",
+      "     |      may sometimes lose precision.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      For a 1D array, ``a.tolist()`` is almost the same as ``list(a)``,\n",
+      "     |      except that ``tolist`` changes numpy scalars to Python scalars:\n",
+      "     |      \n",
+      "     |      >>> a = np.uint32([1, 2])\n",
+      "     |      >>> a_list = list(a)\n",
+      "     |      >>> a_list\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_list[0])\n",
+      "     |      <class 'numpy.uint32'>\n",
+      "     |      >>> a_tolist = a.tolist()\n",
+      "     |      >>> a_tolist\n",
+      "     |      [1, 2]\n",
+      "     |      >>> type(a_tolist[0])\n",
+      "     |      <class 'int'>\n",
+      "     |      \n",
+      "     |      Additionally, for a 2D array, ``tolist`` applies recursively:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> list(a)\n",
+      "     |      [array([1, 2]), array([3, 4])]\n",
+      "     |      >>> a.tolist()\n",
+      "     |      [[1, 2], [3, 4]]\n",
+      "     |      \n",
+      "     |      The base case for this recursion is a 0D array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array(1)\n",
+      "     |      >>> list(a)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        ...\n",
+      "     |      TypeError: iteration over a 0-d array\n",
+      "     |      >>> a.tolist()\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      a.tostring(order='C')\n",
+      "     |      \n",
+      "     |      A compatibility alias for `tobytes`, with exactly the same behavior.\n",
+      "     |      \n",
+      "     |      Despite its name, it returns `bytes` not `str`\\ s.\n",
+      "     |      \n",
+      "     |      .. deprecated:: 1.19.0\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Return the sum along diagonals of the array.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.trace` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.trace : equivalent function\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      a.transpose(*axes)\n",
+      "     |      \n",
+      "     |      Returns a view of the array with axes transposed.\n",
+      "     |      \n",
+      "     |      For a 1-D array this has no effect, as a transposed vector is simply the\n",
+      "     |      same vector. To convert a 1-D array into a 2D column vector, an additional\n",
+      "     |      dimension must be added. `np.atleast2d(a).T` achieves this, as does\n",
+      "     |      `a[:, np.newaxis]`.\n",
+      "     |      For a 2-D array, this is a standard matrix transpose.\n",
+      "     |      For an n-D array, if axes are given, their order indicates how the\n",
+      "     |      axes are permuted (see Examples). If axes are not provided and\n",
+      "     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n",
+      "     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      axes : None, tuple of ints, or `n` ints\n",
+      "     |      \n",
+      "     |       * None or no argument: reverses the order of the axes.\n",
+      "     |      \n",
+      "     |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n",
+      "     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n",
+      "     |      \n",
+      "     |       * `n` ints: same as an n-tuple of the same ints (this form is\n",
+      "     |         intended simply as a \"convenience\" alternative to the tuple form)\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      out : ndarray\n",
+      "     |          View of `a`, with axes suitably permuted.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      ndarray.T : Array property returning the array transposed.\n",
+      "     |      ndarray.reshape : Give a new shape to an array without changing its data.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> a = np.array([[1, 2], [3, 4]])\n",
+      "     |      >>> a\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]])\n",
+      "     |      >>> a.transpose()\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose((1, 0))\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |      >>> a.transpose(1, 0)\n",
+      "     |      array([[1, 3],\n",
+      "     |             [2, 4]])\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
+      "     |      \n",
+      "     |      Returns the variance of the array elements, along given axis.\n",
+      "     |      \n",
+      "     |      Refer to `numpy.var` for full documentation.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.var : equivalent function\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      a.view([dtype][, type])\n",
+      "     |      \n",
+      "     |      New view of array with the same data.\n",
+      "     |      \n",
+      "     |      .. note::\n",
+      "     |          Passing None for ``dtype`` is different from omitting the parameter,\n",
+      "     |          since the former invokes ``dtype(None)`` which is an alias for\n",
+      "     |          ``dtype('float_')``.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      dtype : data-type or ndarray sub-class, optional\n",
+      "     |          Data-type descriptor of the returned view, e.g., float32 or int16.\n",
+      "     |          Omitting it results in the view having the same data-type as `a`.\n",
+      "     |          This argument can also be specified as an ndarray sub-class, which\n",
+      "     |          then specifies the type of the returned object (this is equivalent to\n",
+      "     |          setting the ``type`` parameter).\n",
+      "     |      type : Python type, optional\n",
+      "     |          Type of the returned view, e.g., ndarray or matrix.  Again, omission\n",
+      "     |          of the parameter results in type preservation.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      ``a.view()`` is used two different ways:\n",
+      "     |      \n",
+      "     |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n",
+      "     |      of the array's memory with a different data-type.  This can cause a\n",
+      "     |      reinterpretation of the bytes of memory.\n",
+      "     |      \n",
+      "     |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n",
+      "     |      returns an instance of `ndarray_subclass` that looks at the same array\n",
+      "     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the\n",
+      "     |      memory.\n",
+      "     |      \n",
+      "     |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n",
+      "     |      bytes per entry than the previous dtype (for example, converting a\n",
+      "     |      regular array to a structured array), then the behavior of the view\n",
+      "     |      cannot be predicted just from the superficial appearance of ``a`` (shown\n",
+      "     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in\n",
+      "     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n",
+      "     |      defined as a slice or transpose, etc., the view may give different\n",
+      "     |      results.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      \n",
+      "     |      Viewing array data using a different type and dtype:\n",
+      "     |      \n",
+      "     |      >>> y = x.view(dtype=np.int16, type=np.matrix)\n",
+      "     |      >>> y\n",
+      "     |      matrix([[513]], dtype=int16)\n",
+      "     |      >>> print(type(y))\n",
+      "     |      <class 'numpy.matrix'>\n",
+      "     |      \n",
+      "     |      Creating a view on a structured array so it can be used in calculations\n",
+      "     |      \n",
+      "     |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n",
+      "     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n",
+      "     |      >>> xv\n",
+      "     |      array([[1, 2],\n",
+      "     |             [3, 4]], dtype=int8)\n",
+      "     |      >>> xv.mean(0)\n",
+      "     |      array([2.,  3.])\n",
+      "     |      \n",
+      "     |      Making changes to the view changes the underlying array\n",
+      "     |      \n",
+      "     |      >>> xv[0,1] = 20\n",
+      "     |      >>> x\n",
+      "     |      array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])\n",
+      "     |      \n",
+      "     |      Using a view to convert an array to a recarray:\n",
+      "     |      \n",
+      "     |      >>> z = x.view(np.recarray)\n",
+      "     |      >>> z.a\n",
+      "     |      array([1, 3], dtype=int8)\n",
+      "     |      \n",
+      "     |      Views share data:\n",
+      "     |      \n",
+      "     |      >>> x[0] = (9, 10)\n",
+      "     |      >>> z[0]\n",
+      "     |      (9, 10)\n",
+      "     |      \n",
+      "     |      Views that change the dtype size (bytes per entry) should normally be\n",
+      "     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n",
+      "     |      >>> y = x[:, 0:2]\n",
+      "     |      >>> y\n",
+      "     |      array([[1, 2],\n",
+      "     |             [4, 5]], dtype=int16)\n",
+      "     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |          ...\n",
+      "     |      ValueError: To change to a dtype of a different size, the array must be C-contiguous\n",
+      "     |      >>> z = y.copy()\n",
+      "     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
+      "     |      array([[(1, 2)],\n",
+      "     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from ndarray:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      The transposed array.\n",
+      "     |      \n",
+      "     |      Same as ``self.transpose()``.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([[1.,2.],[3.,4.]])\n",
+      "     |      >>> x\n",
+      "     |      array([[ 1.,  2.],\n",
+      "     |             [ 3.,  4.]])\n",
+      "     |      >>> x.T\n",
+      "     |      array([[ 1.,  3.],\n",
+      "     |             [ 2.,  4.]])\n",
+      "     |      >>> x = np.array([1.,2.,3.,4.])\n",
+      "     |      >>> x\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      >>> x.T\n",
+      "     |      array([ 1.,  2.,  3.,  4.])\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side.\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: C-struct side.\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      Base object if memory is from some other object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      The base of an array that owns its memory is None:\n",
+      "     |      \n",
+      "     |      >>> x = np.array([1,2,3,4])\n",
+      "     |      >>> x.base is None\n",
+      "     |      True\n",
+      "     |      \n",
+      "     |      Slicing creates a view, whose memory is shared with x:\n",
+      "     |      \n",
+      "     |      >>> y = x[2:]\n",
+      "     |      >>> y.base is x\n",
+      "     |      True\n",
+      "     |  \n",
+      "     |  ctypes\n",
+      "     |      An object to simplify the interaction of the array with the ctypes\n",
+      "     |      module.\n",
+      "     |      \n",
+      "     |      This attribute creates an object that makes it easier to use arrays\n",
+      "     |      when calling shared libraries with the ctypes module. The returned\n",
+      "     |      object has, among others, data, shape, and strides attributes (see\n",
+      "     |      Notes below) which themselves return ctypes objects that can be used\n",
+      "     |      as arguments to a shared library.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      c : Python object\n",
+      "     |          Possessing attributes data, shape, strides, etc.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ctypeslib\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Below are the public attributes of this object which were documented\n",
+      "     |      in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n",
+      "     |      as well as documented private attributes):\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.data\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.shape\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. autoattribute:: numpy.core._internal._ctypes.strides\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.data_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.shape_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      .. automethod:: numpy.core._internal._ctypes.strides_as\n",
+      "     |          :noindex:\n",
+      "     |      \n",
+      "     |      If the ctypes module is not available, then the ctypes attribute\n",
+      "     |      of array objects still returns something useful, but ctypes objects\n",
+      "     |      are not returned and errors may be raised instead. In particular,\n",
+      "     |      the object will still have the ``as_parameter`` attribute which will\n",
+      "     |      return an integer equal to the data attribute.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> import ctypes\n",
+      "     |      >>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]], dtype=int32)\n",
+      "     |      >>> x.ctypes.data\n",
+      "     |      31962608 # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))\n",
+      "     |      <__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents\n",
+      "     |      c_uint(0)\n",
+      "     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents\n",
+      "     |      c_ulong(4294967296)\n",
+      "     |      >>> x.ctypes.shape\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary\n",
+      "     |      >>> x.ctypes.strides\n",
+      "     |      <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      Python buffer object pointing to the start of the array's data.\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      Data-type of the array's elements.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      None\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      d : numpy dtype object\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.dtype\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x\n",
+      "     |      array([[0, 1],\n",
+      "     |             [2, 3]])\n",
+      "     |      >>> x.dtype\n",
+      "     |      dtype('int32')\n",
+      "     |      >>> type(x.dtype)\n",
+      "     |      <type 'numpy.dtype'>\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      Information about the memory layout of the array.\n",
+      "     |      \n",
+      "     |      Attributes\n",
+      "     |      ----------\n",
+      "     |      C_CONTIGUOUS (C)\n",
+      "     |          The data is in a single, C-style contiguous segment.\n",
+      "     |      F_CONTIGUOUS (F)\n",
+      "     |          The data is in a single, Fortran-style contiguous segment.\n",
+      "     |      OWNDATA (O)\n",
+      "     |          The array owns the memory it uses or borrows it from another object.\n",
+      "     |      WRITEABLE (W)\n",
+      "     |          The data area can be written to.  Setting this to False locks\n",
+      "     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE\n",
+      "     |          from its base array at creation time, but a view of a writeable\n",
+      "     |          array may be subsequently locked while the base array remains writeable.\n",
+      "     |          (The opposite is not true, in that a view of a locked array may not\n",
+      "     |          be made writeable.  However, currently, locking a base object does not\n",
+      "     |          lock any views that already reference it, so under that circumstance it\n",
+      "     |          is possible to alter the contents of a locked array via a previously\n",
+      "     |          created writeable view onto it.)  Attempting to change a non-writeable\n",
+      "     |          array raises a RuntimeError exception.\n",
+      "     |      ALIGNED (A)\n",
+      "     |          The data and all elements are aligned appropriately for the hardware.\n",
+      "     |      WRITEBACKIFCOPY (X)\n",
+      "     |          This array is a copy of some other array. The C-API function\n",
+      "     |          PyArray_ResolveWritebackIfCopy must be called before deallocating\n",
+      "     |          to the base array will be updated with the contents of this array.\n",
+      "     |      UPDATEIFCOPY (U)\n",
+      "     |          (Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array.\n",
+      "     |          When this array is\n",
+      "     |          deallocated, the base array will be updated with the contents of\n",
+      "     |          this array.\n",
+      "     |      FNC\n",
+      "     |          F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      FORC\n",
+      "     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n",
+      "     |      BEHAVED (B)\n",
+      "     |          ALIGNED and WRITEABLE.\n",
+      "     |      CARRAY (CA)\n",
+      "     |          BEHAVED and C_CONTIGUOUS.\n",
+      "     |      FARRAY (FA)\n",
+      "     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n",
+      "     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n",
+      "     |      names are only supported in dictionary access.\n",
+      "     |      \n",
+      "     |      Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be\n",
+      "     |      changed by the user, via direct assignment to the attribute or dictionary\n",
+      "     |      entry, or by calling `ndarray.setflags`.\n",
+      "     |      \n",
+      "     |      The array flags cannot be set arbitrarily:\n",
+      "     |      \n",
+      "     |      - UPDATEIFCOPY can only be set ``False``.\n",
+      "     |      - WRITEBACKIFCOPY can only be set ``False``.\n",
+      "     |      - ALIGNED can only be set ``True`` if the data is truly aligned.\n",
+      "     |      - WRITEABLE can only be set ``True`` if the array owns its own memory\n",
+      "     |        or the ultimate owner of the memory exposes a writeable buffer\n",
+      "     |        interface or is a string.\n",
+      "     |      \n",
+      "     |      Arrays can be both C-style and Fortran-style contiguous simultaneously.\n",
+      "     |      This is clear for 1-dimensional arrays, but can also be true for higher\n",
+      "     |      dimensional arrays.\n",
+      "     |      \n",
+      "     |      Even for contiguous arrays a stride for a given dimension\n",
+      "     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n",
+      "     |      or the array has no elements.\n",
+      "     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n",
+      "     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n",
+      "     |      Fortran-style contiguous arrays is true.\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      A 1-D iterator over the array.\n",
+      "     |      \n",
+      "     |      This is a `numpy.flatiter` instance, which acts similarly to, but is not\n",
+      "     |      a subclass of, Python's built-in iterator object.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      flatten : Return a copy of the array collapsed into one dimension.\n",
+      "     |      \n",
+      "     |      flatiter\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.arange(1, 7).reshape(2, 3)\n",
+      "     |      >>> x\n",
+      "     |      array([[1, 2, 3],\n",
+      "     |             [4, 5, 6]])\n",
+      "     |      >>> x.flat[3]\n",
+      "     |      4\n",
+      "     |      >>> x.T\n",
+      "     |      array([[1, 4],\n",
+      "     |             [2, 5],\n",
+      "     |             [3, 6]])\n",
+      "     |      >>> x.T.flat[3]\n",
+      "     |      5\n",
+      "     |      >>> type(x.flat)\n",
+      "     |      <class 'numpy.flatiter'>\n",
+      "     |      \n",
+      "     |      An assignment example:\n",
+      "     |      \n",
+      "     |      >>> x.flat = 3; x\n",
+      "     |      array([[3, 3, 3],\n",
+      "     |             [3, 3, 3]])\n",
+      "     |      >>> x.flat[[1,4]] = 1; x\n",
+      "     |      array([[3, 1, 3],\n",
+      "     |             [3, 1, 3]])\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      The imaginary part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.imag\n",
+      "     |      array([ 0.        ,  0.70710678])\n",
+      "     |      >>> x.imag.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      Length of one array element in bytes.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.float64)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      8\n",
+      "     |      >>> x = np.array([1,2,3], dtype=np.complex128)\n",
+      "     |      >>> x.itemsize\n",
+      "     |      16\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      Total bytes consumed by the elements of the array.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Does not include memory consumed by non-element attributes of the\n",
+      "     |      array object.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)\n",
+      "     |      >>> x.nbytes\n",
+      "     |      480\n",
+      "     |      >>> np.prod(x.shape) * x.itemsize\n",
+      "     |      480\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      Number of array dimensions.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3])\n",
+      "     |      >>> x.ndim\n",
+      "     |      1\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.ndim\n",
+      "     |      3\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      The real part of the array.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.sqrt([1+0j, 0+1j])\n",
+      "     |      >>> x.real\n",
+      "     |      array([ 1.        ,  0.70710678])\n",
+      "     |      >>> x.real.dtype\n",
+      "     |      dtype('float64')\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.real : equivalent function\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      Tuple of array dimensions.\n",
+      "     |      \n",
+      "     |      The shape property is usually used to get the current shape of an array,\n",
+      "     |      but may also be used to reshape the array in-place by assigning a tuple of\n",
+      "     |      array dimensions to it.  As with `numpy.reshape`, one of the new shape\n",
+      "     |      dimensions can be -1, in which case its value is inferred from the size of\n",
+      "     |      the array and the remaining dimensions. Reshaping an array in-place will\n",
+      "     |      fail if a copy is required.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.array([1, 2, 3, 4])\n",
+      "     |      >>> x.shape\n",
+      "     |      (4,)\n",
+      "     |      >>> y = np.zeros((2, 3, 4))\n",
+      "     |      >>> y.shape\n",
+      "     |      (2, 3, 4)\n",
+      "     |      >>> y.shape = (3, 8)\n",
+      "     |      >>> y\n",
+      "     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+      "     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
+      "     |      >>> y.shape = (3, 6)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      ValueError: total size of new array must be unchanged\n",
+      "     |      >>> np.zeros((4,2))[::2].shape = (-1,)\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |        File \"<stdin>\", line 1, in <module>\n",
+      "     |      AttributeError: Incompatible shape for in-place modification. Use\n",
+      "     |      `.reshape()` to make a copy with the desired shape.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.reshape : similar function\n",
+      "     |      ndarray.reshape : similar method\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      Number of elements in the array.\n",
+      "     |      \n",
+      "     |      Equal to ``np.prod(a.shape)``, i.e., the product of the array's\n",
+      "     |      dimensions.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      `a.size` returns a standard arbitrary precision Python integer. This\n",
+      "     |      may not be the case with other methods of obtaining the same value\n",
+      "     |      (like the suggested ``np.prod(a.shape)``, which returns an instance\n",
+      "     |      of ``np.int_``), and may be relevant if the value is used further in\n",
+      "     |      calculations that may overflow a fixed size integer type.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n",
+      "     |      >>> x.size\n",
+      "     |      30\n",
+      "     |      >>> np.prod(x.shape)\n",
+      "     |      30\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      Tuple of bytes to step in each dimension when traversing an array.\n",
+      "     |      \n",
+      "     |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n",
+      "     |      is::\n",
+      "     |      \n",
+      "     |          offset = sum(np.array(i) * a.strides)\n",
+      "     |      \n",
+      "     |      A more detailed explanation of strides can be found in the\n",
+      "     |      \"ndarray.rst\" file in the NumPy reference guide.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Imagine an array of 32-bit integers (each 4 bytes)::\n",
+      "     |      \n",
+      "     |        x = np.array([[0, 1, 2, 3, 4],\n",
+      "     |                      [5, 6, 7, 8, 9]], dtype=np.int32)\n",
+      "     |      \n",
+      "     |      This array is stored in memory as 40 bytes, one after the other\n",
+      "     |      (known as a contiguous block of memory).  The strides of an array tell\n",
+      "     |      us how many bytes we have to skip in memory to move to the next position\n",
+      "     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to\n",
+      "     |      move to the next column, but 20 bytes (5 values) to get to the same\n",
+      "     |      position in the next row.  As such, the strides for the array `x` will be\n",
+      "     |      ``(20, 4)``.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.lib.stride_tricks.as_strided\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n",
+      "     |      >>> y\n",
+      "     |      array([[[ 0,  1,  2,  3],\n",
+      "     |              [ 4,  5,  6,  7],\n",
+      "     |              [ 8,  9, 10, 11]],\n",
+      "     |             [[12, 13, 14, 15],\n",
+      "     |              [16, 17, 18, 19],\n",
+      "     |              [20, 21, 22, 23]]])\n",
+      "     |      >>> y.strides\n",
+      "     |      (48, 16, 4)\n",
+      "     |      >>> y[1,1,1]\n",
+      "     |      17\n",
+      "     |      >>> offset=sum(y.strides * np.array((1,1,1)))\n",
+      "     |      >>> offset/y.itemsize\n",
+      "     |      17\n",
+      "     |      \n",
+      "     |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n",
+      "     |      >>> x.strides\n",
+      "     |      (32, 4, 224, 1344)\n",
+      "     |      >>> i = np.array([3,5,2,2])\n",
+      "     |      >>> offset = sum(i * x.strides)\n",
+      "     |      >>> x[3,5,2,2]\n",
+      "     |      813\n",
+      "     |      >>> offset / x.itemsize\n",
+      "     |      813\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from ndarray:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    class record(void)\n",
+      "     |  A data-type scalar that allows field access as attribute lookup.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      record\n",
+      "     |      void\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __getattribute__(self, attr)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, indx)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __repr__(self)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setattr__(self, attr, val)\n",
+      "     |      Implement setattr(self, name, value).\n",
+      "     |  \n",
+      "     |  __str__(self)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  pprint(self)\n",
+      "     |      Pretty-print all fields.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from void:\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from void:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from void:\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      dtype object\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    short = class int16(signedinteger)\n",
+      "     |  Signed integer type, compatible with C ``short``.\n",
+      "     |  Character code: ``'h'``.\n",
+      "     |  Canonical name: ``np.short``.\n",
+      "     |  Alias *on this platform*: ``np.int16``: 16-bit signed integer (-32768 to 32767).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      int16\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class signedinteger(integer)\n",
+      "     |  Abstract base class of all signed integer scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    single = class float32(floating)\n",
+      "     |  Single-precision floating-point number type, compatible with C ``float``.\n",
+      "     |  Character code: ``'f'``.\n",
+      "     |  Canonical name: ``np.single``.\n",
+      "     |  Alias *on this platform*: ``np.float32``: 32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      float32\n",
+      "     |      floating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  as_integer_ratio(...)\n",
+      "     |      single.as_integer_ratio() -> (int, int)\n",
+      "     |      \n",
+      "     |      Return a pair of integers, whose ratio is exactly equal to the original\n",
+      "     |      floating point number, and with a positive denominator.\n",
+      "     |      Raise OverflowError on infinities and a ValueError on NaNs.\n",
+      "     |      \n",
+      "     |      >>> np.single(10.0).as_integer_ratio()\n",
+      "     |      (10, 1)\n",
+      "     |      >>> np.single(0.0).as_integer_ratio()\n",
+      "     |      (0, 1)\n",
+      "     |      >>> np.single(-.25).as_integer_ratio()\n",
+      "     |      (-1, 4)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from floating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    singlecomplex = class complex64(complexfloating)\n",
+      "     |  Complex number type composed of two single-precision floating-point\n",
+      "     |  numbers.\n",
+      "     |  Character code: ``'F'``.\n",
+      "     |  Canonical name: ``np.csingle``.\n",
+      "     |  Alias: ``np.singlecomplex``.\n",
+      "     |  Alias *on this platform*: ``np.complex64``: Complex number type composed of 2 32-bit-precision floating-point numbers.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      complex64\n",
+      "     |      complexfloating\n",
+      "     |      inexact\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __complex__(...)\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from complexfloating:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    str0 = class str_(builtins.str, character)\n",
+      "     |  str(object='') -> str\n",
+      "     |  str(bytes_or_buffer[, encoding[, errors]]) -> str\n",
+      "     |  \n",
+      "     |  Create a new string object from the given object. If encoding or\n",
+      "     |  errors is specified, then the object must expose a data buffer\n",
+      "     |  that will be decoded using the given encoding and error handler.\n",
+      "     |  Otherwise, returns the result of object.__str__() (if defined)\n",
+      "     |  or repr(object).\n",
+      "     |  encoding defaults to sys.getdefaultencoding().\n",
+      "     |  errors defaults to 'strict'.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      str_\n",
+      "     |      builtins.str\n",
+      "     |      character\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.str:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __format__(self, format_spec, /)\n",
+      "     |      Return a formatted version of the string as described by format_spec.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __sizeof__(self, /)\n",
+      "     |      Return the size of the string in memory, in bytes.\n",
+      "     |  \n",
+      "     |  capitalize(self, /)\n",
+      "     |      Return a capitalized version of the string.\n",
+      "     |      \n",
+      "     |      More specifically, make the first character have upper case and the rest lower\n",
+      "     |      case.\n",
+      "     |  \n",
+      "     |  casefold(self, /)\n",
+      "     |      Return a version of the string suitable for caseless comparisons.\n",
+      "     |  \n",
+      "     |  center(self, width, fillchar=' ', /)\n",
+      "     |      Return a centered string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  count(...)\n",
+      "     |      S.count(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the number of non-overlapping occurrences of substring sub in\n",
+      "     |      string S[start:end].  Optional arguments start and end are\n",
+      "     |      interpreted as in slice notation.\n",
+      "     |  \n",
+      "     |  encode(self, /, encoding='utf-8', errors='strict')\n",
+      "     |      Encode the string using the codec registered for encoding.\n",
+      "     |      \n",
+      "     |      encoding\n",
+      "     |        The encoding in which to encode the string.\n",
+      "     |      errors\n",
+      "     |        The error handling scheme to use for encoding errors.\n",
+      "     |        The default is 'strict' meaning that encoding errors raise a\n",
+      "     |        UnicodeEncodeError.  Other possible values are 'ignore', 'replace' and\n",
+      "     |        'xmlcharrefreplace' as well as any other name registered with\n",
+      "     |        codecs.register_error that can handle UnicodeEncodeErrors.\n",
+      "     |  \n",
+      "     |  endswith(...)\n",
+      "     |      S.endswith(suffix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if S ends with the specified suffix, False otherwise.\n",
+      "     |      With optional start, test S beginning at that position.\n",
+      "     |      With optional end, stop comparing S at that position.\n",
+      "     |      suffix can also be a tuple of strings to try.\n",
+      "     |  \n",
+      "     |  expandtabs(self, /, tabsize=8)\n",
+      "     |      Return a copy where all tab characters are expanded using spaces.\n",
+      "     |      \n",
+      "     |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
+      "     |  \n",
+      "     |  find(...)\n",
+      "     |      S.find(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  format(...)\n",
+      "     |      S.format(*args, **kwargs) -> str\n",
+      "     |      \n",
+      "     |      Return a formatted version of S, using substitutions from args and kwargs.\n",
+      "     |      The substitutions are identified by braces ('{' and '}').\n",
+      "     |  \n",
+      "     |  format_map(...)\n",
+      "     |      S.format_map(mapping) -> str\n",
+      "     |      \n",
+      "     |      Return a formatted version of S, using substitutions from mapping.\n",
+      "     |      The substitutions are identified by braces ('{' and '}').\n",
+      "     |  \n",
+      "     |  index(...)\n",
+      "     |      S.index(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in S where substring sub is found, \n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the substring is not found.\n",
+      "     |  \n",
+      "     |  isalnum(self, /)\n",
+      "     |      Return True if the string is an alpha-numeric string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is alpha-numeric if all characters in the string are alpha-numeric and\n",
+      "     |      there is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isalpha(self, /)\n",
+      "     |      Return True if the string is an alphabetic string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is alphabetic if all characters in the string are alphabetic and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isascii(self, /)\n",
+      "     |      Return True if all characters in the string are ASCII, False otherwise.\n",
+      "     |      \n",
+      "     |      ASCII characters have code points in the range U+0000-U+007F.\n",
+      "     |      Empty string is ASCII too.\n",
+      "     |  \n",
+      "     |  isdecimal(self, /)\n",
+      "     |      Return True if the string is a decimal string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is a decimal string if all characters in the string are decimal and\n",
+      "     |      there is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isdigit(self, /)\n",
+      "     |      Return True if the string is a digit string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is a digit string if all characters in the string are digits and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isidentifier(self, /)\n",
+      "     |      Return True if the string is a valid Python identifier, False otherwise.\n",
+      "     |      \n",
+      "     |      Use keyword.iskeyword() to test for reserved identifiers such as \"def\" and\n",
+      "     |      \"class\".\n",
+      "     |  \n",
+      "     |  islower(self, /)\n",
+      "     |      Return True if the string is a lowercase string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is lowercase if all cased characters in the string are lowercase and\n",
+      "     |      there is at least one cased character in the string.\n",
+      "     |  \n",
+      "     |  isnumeric(self, /)\n",
+      "     |      Return True if the string is a numeric string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is numeric if all characters in the string are numeric and there is at\n",
+      "     |      least one character in the string.\n",
+      "     |  \n",
+      "     |  isprintable(self, /)\n",
+      "     |      Return True if the string is printable, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is printable if all of its characters are considered printable in\n",
+      "     |      repr() or if it is empty.\n",
+      "     |  \n",
+      "     |  isspace(self, /)\n",
+      "     |      Return True if the string is a whitespace string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is whitespace if all characters in the string are whitespace and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  istitle(self, /)\n",
+      "     |      Return True if the string is a title-cased string, False otherwise.\n",
+      "     |      \n",
+      "     |      In a title-cased string, upper- and title-case characters may only\n",
+      "     |      follow uncased characters and lowercase characters only cased ones.\n",
+      "     |  \n",
+      "     |  isupper(self, /)\n",
+      "     |      Return True if the string is an uppercase string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is uppercase if all cased characters in the string are uppercase and\n",
+      "     |      there is at least one cased character in the string.\n",
+      "     |  \n",
+      "     |  join(self, iterable, /)\n",
+      "     |      Concatenate any number of strings.\n",
+      "     |      \n",
+      "     |      The string whose method is called is inserted in between each given string.\n",
+      "     |      The result is returned as a new string.\n",
+      "     |      \n",
+      "     |      Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n",
+      "     |  \n",
+      "     |  ljust(self, width, fillchar=' ', /)\n",
+      "     |      Return a left-justified string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  lower(self, /)\n",
+      "     |      Return a copy of the string converted to lowercase.\n",
+      "     |  \n",
+      "     |  lstrip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with leading whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  partition(self, sep, /)\n",
+      "     |      Partition the string into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator in the string.  If the separator is found,\n",
+      "     |      returns a 3-tuple containing the part before the separator, the separator\n",
+      "     |      itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing the original string\n",
+      "     |      and two empty strings.\n",
+      "     |  \n",
+      "     |  replace(self, old, new, count=-1, /)\n",
+      "     |      Return a copy with all occurrences of substring old replaced by new.\n",
+      "     |      \n",
+      "     |        count\n",
+      "     |          Maximum number of occurrences to replace.\n",
+      "     |          -1 (the default value) means replace all occurrences.\n",
+      "     |      \n",
+      "     |      If the optional argument count is given, only the first count occurrences are\n",
+      "     |      replaced.\n",
+      "     |  \n",
+      "     |  rfind(...)\n",
+      "     |      S.rfind(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  rindex(...)\n",
+      "     |      S.rindex(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the substring is not found.\n",
+      "     |  \n",
+      "     |  rjust(self, width, fillchar=' ', /)\n",
+      "     |      Return a right-justified string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  rpartition(self, sep, /)\n",
+      "     |      Partition the string into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator in the string, starting at the end. If\n",
+      "     |      the separator is found, returns a 3-tuple containing the part before the\n",
+      "     |      separator, the separator itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing two empty strings\n",
+      "     |      and the original string.\n",
+      "     |  \n",
+      "     |  rsplit(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the words in the string, using sep as the delimiter string.\n",
+      "     |      \n",
+      "     |        sep\n",
+      "     |          The delimiter according which to split the string.\n",
+      "     |          None (the default value) means split according to any whitespace,\n",
+      "     |          and discard empty strings from the result.\n",
+      "     |        maxsplit\n",
+      "     |          Maximum number of splits to do.\n",
+      "     |          -1 (the default value) means no limit.\n",
+      "     |      \n",
+      "     |      Splits are done starting at the end of the string and working to the front.\n",
+      "     |  \n",
+      "     |  rstrip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with trailing whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  split(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the words in the string, using sep as the delimiter string.\n",
+      "     |      \n",
+      "     |      sep\n",
+      "     |        The delimiter according which to split the string.\n",
+      "     |        None (the default value) means split according to any whitespace,\n",
+      "     |        and discard empty strings from the result.\n",
+      "     |      maxsplit\n",
+      "     |        Maximum number of splits to do.\n",
+      "     |        -1 (the default value) means no limit.\n",
+      "     |  \n",
+      "     |  splitlines(self, /, keepends=False)\n",
+      "     |      Return a list of the lines in the string, breaking at line boundaries.\n",
+      "     |      \n",
+      "     |      Line breaks are not included in the resulting list unless keepends is given and\n",
+      "     |      true.\n",
+      "     |  \n",
+      "     |  startswith(...)\n",
+      "     |      S.startswith(prefix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if S starts with the specified prefix, False otherwise.\n",
+      "     |      With optional start, test S beginning at that position.\n",
+      "     |      With optional end, stop comparing S at that position.\n",
+      "     |      prefix can also be a tuple of strings to try.\n",
+      "     |  \n",
+      "     |  strip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with leading and trailing whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  swapcase(self, /)\n",
+      "     |      Convert uppercase characters to lowercase and lowercase characters to uppercase.\n",
+      "     |  \n",
+      "     |  title(self, /)\n",
+      "     |      Return a version of the string where each word is titlecased.\n",
+      "     |      \n",
+      "     |      More specifically, words start with uppercased characters and all remaining\n",
+      "     |      cased characters have lower case.\n",
+      "     |  \n",
+      "     |  translate(self, table, /)\n",
+      "     |      Replace each character in the string using the given translation table.\n",
+      "     |      \n",
+      "     |        table\n",
+      "     |          Translation table, which must be a mapping of Unicode ordinals to\n",
+      "     |          Unicode ordinals, strings, or None.\n",
+      "     |      \n",
+      "     |      The table must implement lookup/indexing via __getitem__, for instance a\n",
+      "     |      dictionary or list.  If this operation raises LookupError, the character is\n",
+      "     |      left untouched.  Characters mapped to None are deleted.\n",
+      "     |  \n",
+      "     |  upper(self, /)\n",
+      "     |      Return a copy of the string converted to uppercase.\n",
+      "     |  \n",
+      "     |  zfill(self, width, /)\n",
+      "     |      Pad a numeric string with zeros on the left, to fill a field of the given width.\n",
+      "     |      \n",
+      "     |      The string is never truncated.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.str:\n",
+      "     |  \n",
+      "     |  maketrans(x, y=None, z=None, /)\n",
+      "     |      Return a translation table usable for str.translate().\n",
+      "     |      \n",
+      "     |      If there is only one argument, it must be a dictionary mapping Unicode\n",
+      "     |      ordinals (integers) or characters to Unicode ordinals, strings or None.\n",
+      "     |      Character keys will be then converted to ordinals.\n",
+      "     |      If there are two arguments, they must be strings of equal length, and\n",
+      "     |      in the resulting dictionary, each character in x will be mapped to the\n",
+      "     |      character at the same position in y. If there is a third argument, it\n",
+      "     |      must be a string, whose characters will be mapped to None in the result.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class str_(builtins.str, character)\n",
+      "     |  str(object='') -> str\n",
+      "     |  str(bytes_or_buffer[, encoding[, errors]]) -> str\n",
+      "     |  \n",
+      "     |  Create a new string object from the given object. If encoding or\n",
+      "     |  errors is specified, then the object must expose a data buffer\n",
+      "     |  that will be decoded using the given encoding and error handler.\n",
+      "     |  Otherwise, returns the result of object.__str__() (if defined)\n",
+      "     |  or repr(object).\n",
+      "     |  encoding defaults to sys.getdefaultencoding().\n",
+      "     |  errors defaults to 'strict'.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      str_\n",
+      "     |      builtins.str\n",
+      "     |      character\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.str:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __format__(self, format_spec, /)\n",
+      "     |      Return a formatted version of the string as described by format_spec.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __sizeof__(self, /)\n",
+      "     |      Return the size of the string in memory, in bytes.\n",
+      "     |  \n",
+      "     |  capitalize(self, /)\n",
+      "     |      Return a capitalized version of the string.\n",
+      "     |      \n",
+      "     |      More specifically, make the first character have upper case and the rest lower\n",
+      "     |      case.\n",
+      "     |  \n",
+      "     |  casefold(self, /)\n",
+      "     |      Return a version of the string suitable for caseless comparisons.\n",
+      "     |  \n",
+      "     |  center(self, width, fillchar=' ', /)\n",
+      "     |      Return a centered string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  count(...)\n",
+      "     |      S.count(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the number of non-overlapping occurrences of substring sub in\n",
+      "     |      string S[start:end].  Optional arguments start and end are\n",
+      "     |      interpreted as in slice notation.\n",
+      "     |  \n",
+      "     |  encode(self, /, encoding='utf-8', errors='strict')\n",
+      "     |      Encode the string using the codec registered for encoding.\n",
+      "     |      \n",
+      "     |      encoding\n",
+      "     |        The encoding in which to encode the string.\n",
+      "     |      errors\n",
+      "     |        The error handling scheme to use for encoding errors.\n",
+      "     |        The default is 'strict' meaning that encoding errors raise a\n",
+      "     |        UnicodeEncodeError.  Other possible values are 'ignore', 'replace' and\n",
+      "     |        'xmlcharrefreplace' as well as any other name registered with\n",
+      "     |        codecs.register_error that can handle UnicodeEncodeErrors.\n",
+      "     |  \n",
+      "     |  endswith(...)\n",
+      "     |      S.endswith(suffix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if S ends with the specified suffix, False otherwise.\n",
+      "     |      With optional start, test S beginning at that position.\n",
+      "     |      With optional end, stop comparing S at that position.\n",
+      "     |      suffix can also be a tuple of strings to try.\n",
+      "     |  \n",
+      "     |  expandtabs(self, /, tabsize=8)\n",
+      "     |      Return a copy where all tab characters are expanded using spaces.\n",
+      "     |      \n",
+      "     |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
+      "     |  \n",
+      "     |  find(...)\n",
+      "     |      S.find(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  format(...)\n",
+      "     |      S.format(*args, **kwargs) -> str\n",
+      "     |      \n",
+      "     |      Return a formatted version of S, using substitutions from args and kwargs.\n",
+      "     |      The substitutions are identified by braces ('{' and '}').\n",
+      "     |  \n",
+      "     |  format_map(...)\n",
+      "     |      S.format_map(mapping) -> str\n",
+      "     |      \n",
+      "     |      Return a formatted version of S, using substitutions from mapping.\n",
+      "     |      The substitutions are identified by braces ('{' and '}').\n",
+      "     |  \n",
+      "     |  index(...)\n",
+      "     |      S.index(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in S where substring sub is found, \n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the substring is not found.\n",
+      "     |  \n",
+      "     |  isalnum(self, /)\n",
+      "     |      Return True if the string is an alpha-numeric string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is alpha-numeric if all characters in the string are alpha-numeric and\n",
+      "     |      there is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isalpha(self, /)\n",
+      "     |      Return True if the string is an alphabetic string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is alphabetic if all characters in the string are alphabetic and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isascii(self, /)\n",
+      "     |      Return True if all characters in the string are ASCII, False otherwise.\n",
+      "     |      \n",
+      "     |      ASCII characters have code points in the range U+0000-U+007F.\n",
+      "     |      Empty string is ASCII too.\n",
+      "     |  \n",
+      "     |  isdecimal(self, /)\n",
+      "     |      Return True if the string is a decimal string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is a decimal string if all characters in the string are decimal and\n",
+      "     |      there is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isdigit(self, /)\n",
+      "     |      Return True if the string is a digit string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is a digit string if all characters in the string are digits and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isidentifier(self, /)\n",
+      "     |      Return True if the string is a valid Python identifier, False otherwise.\n",
+      "     |      \n",
+      "     |      Use keyword.iskeyword() to test for reserved identifiers such as \"def\" and\n",
+      "     |      \"class\".\n",
+      "     |  \n",
+      "     |  islower(self, /)\n",
+      "     |      Return True if the string is a lowercase string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is lowercase if all cased characters in the string are lowercase and\n",
+      "     |      there is at least one cased character in the string.\n",
+      "     |  \n",
+      "     |  isnumeric(self, /)\n",
+      "     |      Return True if the string is a numeric string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is numeric if all characters in the string are numeric and there is at\n",
+      "     |      least one character in the string.\n",
+      "     |  \n",
+      "     |  isprintable(self, /)\n",
+      "     |      Return True if the string is printable, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is printable if all of its characters are considered printable in\n",
+      "     |      repr() or if it is empty.\n",
+      "     |  \n",
+      "     |  isspace(self, /)\n",
+      "     |      Return True if the string is a whitespace string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is whitespace if all characters in the string are whitespace and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  istitle(self, /)\n",
+      "     |      Return True if the string is a title-cased string, False otherwise.\n",
+      "     |      \n",
+      "     |      In a title-cased string, upper- and title-case characters may only\n",
+      "     |      follow uncased characters and lowercase characters only cased ones.\n",
+      "     |  \n",
+      "     |  isupper(self, /)\n",
+      "     |      Return True if the string is an uppercase string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is uppercase if all cased characters in the string are uppercase and\n",
+      "     |      there is at least one cased character in the string.\n",
+      "     |  \n",
+      "     |  join(self, iterable, /)\n",
+      "     |      Concatenate any number of strings.\n",
+      "     |      \n",
+      "     |      The string whose method is called is inserted in between each given string.\n",
+      "     |      The result is returned as a new string.\n",
+      "     |      \n",
+      "     |      Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n",
+      "     |  \n",
+      "     |  ljust(self, width, fillchar=' ', /)\n",
+      "     |      Return a left-justified string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  lower(self, /)\n",
+      "     |      Return a copy of the string converted to lowercase.\n",
+      "     |  \n",
+      "     |  lstrip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with leading whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  partition(self, sep, /)\n",
+      "     |      Partition the string into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator in the string.  If the separator is found,\n",
+      "     |      returns a 3-tuple containing the part before the separator, the separator\n",
+      "     |      itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing the original string\n",
+      "     |      and two empty strings.\n",
+      "     |  \n",
+      "     |  replace(self, old, new, count=-1, /)\n",
+      "     |      Return a copy with all occurrences of substring old replaced by new.\n",
+      "     |      \n",
+      "     |        count\n",
+      "     |          Maximum number of occurrences to replace.\n",
+      "     |          -1 (the default value) means replace all occurrences.\n",
+      "     |      \n",
+      "     |      If the optional argument count is given, only the first count occurrences are\n",
+      "     |      replaced.\n",
+      "     |  \n",
+      "     |  rfind(...)\n",
+      "     |      S.rfind(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  rindex(...)\n",
+      "     |      S.rindex(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the substring is not found.\n",
+      "     |  \n",
+      "     |  rjust(self, width, fillchar=' ', /)\n",
+      "     |      Return a right-justified string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  rpartition(self, sep, /)\n",
+      "     |      Partition the string into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator in the string, starting at the end. If\n",
+      "     |      the separator is found, returns a 3-tuple containing the part before the\n",
+      "     |      separator, the separator itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing two empty strings\n",
+      "     |      and the original string.\n",
+      "     |  \n",
+      "     |  rsplit(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the words in the string, using sep as the delimiter string.\n",
+      "     |      \n",
+      "     |        sep\n",
+      "     |          The delimiter according which to split the string.\n",
+      "     |          None (the default value) means split according to any whitespace,\n",
+      "     |          and discard empty strings from the result.\n",
+      "     |        maxsplit\n",
+      "     |          Maximum number of splits to do.\n",
+      "     |          -1 (the default value) means no limit.\n",
+      "     |      \n",
+      "     |      Splits are done starting at the end of the string and working to the front.\n",
+      "     |  \n",
+      "     |  rstrip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with trailing whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  split(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the words in the string, using sep as the delimiter string.\n",
+      "     |      \n",
+      "     |      sep\n",
+      "     |        The delimiter according which to split the string.\n",
+      "     |        None (the default value) means split according to any whitespace,\n",
+      "     |        and discard empty strings from the result.\n",
+      "     |      maxsplit\n",
+      "     |        Maximum number of splits to do.\n",
+      "     |        -1 (the default value) means no limit.\n",
+      "     |  \n",
+      "     |  splitlines(self, /, keepends=False)\n",
+      "     |      Return a list of the lines in the string, breaking at line boundaries.\n",
+      "     |      \n",
+      "     |      Line breaks are not included in the resulting list unless keepends is given and\n",
+      "     |      true.\n",
+      "     |  \n",
+      "     |  startswith(...)\n",
+      "     |      S.startswith(prefix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if S starts with the specified prefix, False otherwise.\n",
+      "     |      With optional start, test S beginning at that position.\n",
+      "     |      With optional end, stop comparing S at that position.\n",
+      "     |      prefix can also be a tuple of strings to try.\n",
+      "     |  \n",
+      "     |  strip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with leading and trailing whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  swapcase(self, /)\n",
+      "     |      Convert uppercase characters to lowercase and lowercase characters to uppercase.\n",
+      "     |  \n",
+      "     |  title(self, /)\n",
+      "     |      Return a version of the string where each word is titlecased.\n",
+      "     |      \n",
+      "     |      More specifically, words start with uppercased characters and all remaining\n",
+      "     |      cased characters have lower case.\n",
+      "     |  \n",
+      "     |  translate(self, table, /)\n",
+      "     |      Replace each character in the string using the given translation table.\n",
+      "     |      \n",
+      "     |        table\n",
+      "     |          Translation table, which must be a mapping of Unicode ordinals to\n",
+      "     |          Unicode ordinals, strings, or None.\n",
+      "     |      \n",
+      "     |      The table must implement lookup/indexing via __getitem__, for instance a\n",
+      "     |      dictionary or list.  If this operation raises LookupError, the character is\n",
+      "     |      left untouched.  Characters mapped to None are deleted.\n",
+      "     |  \n",
+      "     |  upper(self, /)\n",
+      "     |      Return a copy of the string converted to uppercase.\n",
+      "     |  \n",
+      "     |  zfill(self, width, /)\n",
+      "     |      Pad a numeric string with zeros on the left, to fill a field of the given width.\n",
+      "     |      \n",
+      "     |      The string is never truncated.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.str:\n",
+      "     |  \n",
+      "     |  maketrans(x, y=None, z=None, /)\n",
+      "     |      Return a translation table usable for str.translate().\n",
+      "     |      \n",
+      "     |      If there is only one argument, it must be a dictionary mapping Unicode\n",
+      "     |      ordinals (integers) or characters to Unicode ordinals, strings or None.\n",
+      "     |      Character keys will be then converted to ordinals.\n",
+      "     |      If there are two arguments, they must be strings of equal length, and\n",
+      "     |      in the resulting dictionary, each character in x will be mapped to the\n",
+      "     |      character at the same position in y. If there is a third argument, it\n",
+      "     |      must be a string, whose characters will be mapped to None in the result.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    string_ = class bytes_(builtins.bytes, character)\n",
+      "     |  bytes(iterable_of_ints) -> bytes\n",
+      "     |  bytes(string, encoding[, errors]) -> bytes\n",
+      "     |  bytes(bytes_or_buffer) -> immutable copy of bytes_or_buffer\n",
+      "     |  bytes(int) -> bytes object of size given by the parameter initialized with null bytes\n",
+      "     |  bytes() -> empty bytes object\n",
+      "     |  \n",
+      "     |  Construct an immutable array of bytes from:\n",
+      "     |    - an iterable yielding integers in range(256)\n",
+      "     |    - a text string encoded using the specified encoding\n",
+      "     |    - any object implementing the buffer API.\n",
+      "     |    - an integer\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      bytes_\n",
+      "     |      builtins.bytes\n",
+      "     |      character\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  capitalize(...)\n",
+      "     |      B.capitalize() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with only its first character capitalized (ASCII)\n",
+      "     |      and the rest lower-cased.\n",
+      "     |  \n",
+      "     |  center(...)\n",
+      "     |      B.center(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B centered in a string of length width.  Padding is\n",
+      "     |      done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  count(...)\n",
+      "     |      B.count(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the number of non-overlapping occurrences of subsection sub in\n",
+      "     |      bytes B[start:end].  Optional arguments start and end are interpreted\n",
+      "     |      as in slice notation.\n",
+      "     |  \n",
+      "     |  decode(self, /, encoding='utf-8', errors='strict')\n",
+      "     |      Decode the bytes using the codec registered for encoding.\n",
+      "     |      \n",
+      "     |      encoding\n",
+      "     |        The encoding with which to decode the bytes.\n",
+      "     |      errors\n",
+      "     |        The error handling scheme to use for the handling of decoding errors.\n",
+      "     |        The default is 'strict' meaning that decoding errors raise a\n",
+      "     |        UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n",
+      "     |        as well as any other name registered with codecs.register_error that\n",
+      "     |        can handle UnicodeDecodeErrors.\n",
+      "     |  \n",
+      "     |  endswith(...)\n",
+      "     |      B.endswith(suffix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if B ends with the specified suffix, False otherwise.\n",
+      "     |      With optional start, test B beginning at that position.\n",
+      "     |      With optional end, stop comparing B at that position.\n",
+      "     |      suffix can also be a tuple of bytes to try.\n",
+      "     |  \n",
+      "     |  expandtabs(...)\n",
+      "     |      B.expandtabs(tabsize=8) -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B where all tab characters are expanded using spaces.\n",
+      "     |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
+      "     |  \n",
+      "     |  find(...)\n",
+      "     |      B.find(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  hex(...)\n",
+      "     |      B.hex() -> string\n",
+      "     |      \n",
+      "     |      Create a string of hexadecimal numbers from a bytes object.\n",
+      "     |      Example: b'\\xb9\\x01\\xef'.hex() -> 'b901ef'.\n",
+      "     |  \n",
+      "     |  index(...)\n",
+      "     |      B.index(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the subsection is not found.\n",
+      "     |  \n",
+      "     |  isalnum(...)\n",
+      "     |      B.isalnum() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are alphanumeric\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isalpha(...)\n",
+      "     |      B.isalpha() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are alphabetic\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isascii(...)\n",
+      "     |      B.isascii() -> bool\n",
+      "     |      \n",
+      "     |      Return True if B is empty or all characters in B are ASCII,\n",
+      "     |      False otherwise.\n",
+      "     |  \n",
+      "     |  isdigit(...)\n",
+      "     |      B.isdigit() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are digits\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  islower(...)\n",
+      "     |      B.islower() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all cased characters in B are lowercase and there is\n",
+      "     |      at least one cased character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  isspace(...)\n",
+      "     |      B.isspace() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all characters in B are whitespace\n",
+      "     |      and there is at least one character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  istitle(...)\n",
+      "     |      B.istitle() -> bool\n",
+      "     |      \n",
+      "     |      Return True if B is a titlecased string and there is at least one\n",
+      "     |      character in B, i.e. uppercase characters may only follow uncased\n",
+      "     |      characters and lowercase characters only cased ones. Return False\n",
+      "     |      otherwise.\n",
+      "     |  \n",
+      "     |  isupper(...)\n",
+      "     |      B.isupper() -> bool\n",
+      "     |      \n",
+      "     |      Return True if all cased characters in B are uppercase and there is\n",
+      "     |      at least one cased character in B, False otherwise.\n",
+      "     |  \n",
+      "     |  join(self, iterable_of_bytes, /)\n",
+      "     |      Concatenate any number of bytes objects.\n",
+      "     |      \n",
+      "     |      The bytes whose method is called is inserted in between each pair.\n",
+      "     |      \n",
+      "     |      The result is returned as a new bytes object.\n",
+      "     |      \n",
+      "     |      Example: b'.'.join([b'ab', b'pq', b'rs']) -> b'ab.pq.rs'.\n",
+      "     |  \n",
+      "     |  ljust(...)\n",
+      "     |      B.ljust(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B left justified in a string of length width. Padding is\n",
+      "     |      done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  lower(...)\n",
+      "     |      B.lower() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with all ASCII characters converted to lowercase.\n",
+      "     |  \n",
+      "     |  lstrip(self, bytes=None, /)\n",
+      "     |      Strip leading bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip leading  ASCII whitespace.\n",
+      "     |  \n",
+      "     |  partition(self, sep, /)\n",
+      "     |      Partition the bytes into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator sep in the bytes. If the separator is found,\n",
+      "     |      returns a 3-tuple containing the part before the separator, the separator\n",
+      "     |      itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing the original bytes\n",
+      "     |      object and two empty bytes objects.\n",
+      "     |  \n",
+      "     |  replace(self, old, new, count=-1, /)\n",
+      "     |      Return a copy with all occurrences of substring old replaced by new.\n",
+      "     |      \n",
+      "     |        count\n",
+      "     |          Maximum number of occurrences to replace.\n",
+      "     |          -1 (the default value) means replace all occurrences.\n",
+      "     |      \n",
+      "     |      If the optional argument count is given, only the first count occurrences are\n",
+      "     |      replaced.\n",
+      "     |  \n",
+      "     |  rfind(...)\n",
+      "     |      B.rfind(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  rindex(...)\n",
+      "     |      B.rindex(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in B where subsection sub is found,\n",
+      "     |      such that sub is contained within B[start,end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raise ValueError when the subsection is not found.\n",
+      "     |  \n",
+      "     |  rjust(...)\n",
+      "     |      B.rjust(width[, fillchar]) -> copy of B\n",
+      "     |      \n",
+      "     |      Return B right justified in a string of length width. Padding is\n",
+      "     |      done using the specified fill character (default is a space)\n",
+      "     |  \n",
+      "     |  rpartition(self, sep, /)\n",
+      "     |      Partition the bytes into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator sep in the bytes, starting at the end. If\n",
+      "     |      the separator is found, returns a 3-tuple containing the part before the\n",
+      "     |      separator, the separator itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing two empty bytes\n",
+      "     |      objects and the original bytes object.\n",
+      "     |  \n",
+      "     |  rsplit(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the sections in the bytes, using sep as the delimiter.\n",
+      "     |      \n",
+      "     |        sep\n",
+      "     |          The delimiter according which to split the bytes.\n",
+      "     |          None (the default value) means split on ASCII whitespace characters\n",
+      "     |          (space, tab, return, newline, formfeed, vertical tab).\n",
+      "     |        maxsplit\n",
+      "     |          Maximum number of splits to do.\n",
+      "     |          -1 (the default value) means no limit.\n",
+      "     |      \n",
+      "     |      Splitting is done starting at the end of the bytes and working to the front.\n",
+      "     |  \n",
+      "     |  rstrip(self, bytes=None, /)\n",
+      "     |      Strip trailing bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip trailing ASCII whitespace.\n",
+      "     |  \n",
+      "     |  split(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the sections in the bytes, using sep as the delimiter.\n",
+      "     |      \n",
+      "     |      sep\n",
+      "     |        The delimiter according which to split the bytes.\n",
+      "     |        None (the default value) means split on ASCII whitespace characters\n",
+      "     |        (space, tab, return, newline, formfeed, vertical tab).\n",
+      "     |      maxsplit\n",
+      "     |        Maximum number of splits to do.\n",
+      "     |        -1 (the default value) means no limit.\n",
+      "     |  \n",
+      "     |  splitlines(self, /, keepends=False)\n",
+      "     |      Return a list of the lines in the bytes, breaking at line boundaries.\n",
+      "     |      \n",
+      "     |      Line breaks are not included in the resulting list unless keepends is given and\n",
+      "     |      true.\n",
+      "     |  \n",
+      "     |  startswith(...)\n",
+      "     |      B.startswith(prefix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if B starts with the specified prefix, False otherwise.\n",
+      "     |      With optional start, test B beginning at that position.\n",
+      "     |      With optional end, stop comparing B at that position.\n",
+      "     |      prefix can also be a tuple of bytes to try.\n",
+      "     |  \n",
+      "     |  strip(self, bytes=None, /)\n",
+      "     |      Strip leading and trailing bytes contained in the argument.\n",
+      "     |      \n",
+      "     |      If the argument is omitted or None, strip leading and trailing ASCII whitespace.\n",
+      "     |  \n",
+      "     |  swapcase(...)\n",
+      "     |      B.swapcase() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with uppercase ASCII characters converted\n",
+      "     |      to lowercase ASCII and vice versa.\n",
+      "     |  \n",
+      "     |  title(...)\n",
+      "     |      B.title() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a titlecased version of B, i.e. ASCII words start with uppercase\n",
+      "     |      characters, all remaining cased characters have lowercase.\n",
+      "     |  \n",
+      "     |  translate(self, table, /, delete=b'')\n",
+      "     |      Return a copy with each character mapped by the given translation table.\n",
+      "     |      \n",
+      "     |        table\n",
+      "     |          Translation table, which must be a bytes object of length 256.\n",
+      "     |      \n",
+      "     |      All characters occurring in the optional argument delete are removed.\n",
+      "     |      The remaining characters are mapped through the given translation table.\n",
+      "     |  \n",
+      "     |  upper(...)\n",
+      "     |      B.upper() -> copy of B\n",
+      "     |      \n",
+      "     |      Return a copy of B with all ASCII characters converted to uppercase.\n",
+      "     |  \n",
+      "     |  zfill(...)\n",
+      "     |      B.zfill(width) -> copy of B\n",
+      "     |      \n",
+      "     |      Pad a numeric string B with zeros on the left, to fill a field\n",
+      "     |      of the specified width.  B is never truncated.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Class methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  fromhex(string, /) from builtins.type\n",
+      "     |      Create a bytes object from a string of hexadecimal numbers.\n",
+      "     |      \n",
+      "     |      Spaces between two numbers are accepted.\n",
+      "     |      Example: bytes.fromhex('B9 01EF') -> b'\\\\xb9\\\\x01\\\\xef'.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.bytes:\n",
+      "     |  \n",
+      "     |  maketrans(frm, to, /)\n",
+      "     |      Return a translation table useable for the bytes or bytearray translate method.\n",
+      "     |      \n",
+      "     |      The returned table will be one where each byte in frm is mapped to the byte at\n",
+      "     |      the same position in to.\n",
+      "     |      \n",
+      "     |      The bytes objects frm and to must be of the same length.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class timedelta64(signedinteger)\n",
+      "     |  Abstract base class of all signed integer scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      timedelta64\n",
+      "     |      signedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    ubyte = class uint8(unsignedinteger)\n",
+      "     |  Unsigned integer type, compatible with C ``unsigned char``.\n",
+      "     |  Character code: ``'B'``.\n",
+      "     |  Canonical name: ``np.ubyte``.\n",
+      "     |  Alias *on this platform*: ``np.uint8``: 8-bit unsigned integer (0 to 255).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint8\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class ufunc(builtins.object)\n",
+      "     |  Functions that operate element by element on whole arrays.\n",
+      "     |  \n",
+      "     |  To see the documentation for a specific ufunc, use `info`.  For\n",
+      "     |  example, ``np.info(np.sin)``.  Because ufuncs are written in C\n",
+      "     |  (for speed) and linked into Python with NumPy's ufunc facility,\n",
+      "     |  Python's help() function finds this page whenever help() is called\n",
+      "     |  on a ufunc.\n",
+      "     |  \n",
+      "     |  A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.\n",
+      "     |  \n",
+      "     |  Calling ufuncs:\n",
+      "     |  ===============\n",
+      "     |  \n",
+      "     |  op(*x[, out], where=True, **kwargs)\n",
+      "     |  Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.\n",
+      "     |  \n",
+      "     |  The broadcasting rules are:\n",
+      "     |  \n",
+      "     |  * Dimensions of length 1 may be prepended to either array.\n",
+      "     |  * Arrays may be repeated along dimensions of length 1.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  *x : array_like\n",
+      "     |      Input arrays.\n",
+      "     |  out : ndarray, None, or tuple of ndarray and None, optional\n",
+      "     |      Alternate array object(s) in which to put the result; if provided, it\n",
+      "     |      must have a shape that the inputs broadcast to. A tuple of arrays\n",
+      "     |      (possible only as a keyword argument) must have length equal to the\n",
+      "     |      number of outputs; use None for uninitialized outputs to be\n",
+      "     |      allocated by the ufunc.\n",
+      "     |  where : array_like, optional\n",
+      "     |      This condition is broadcast over the input. At locations where the\n",
+      "     |      condition is True, the `out` array will be set to the ufunc result.\n",
+      "     |      Elsewhere, the `out` array will retain its original value.\n",
+      "     |      Note that if an uninitialized `out` array is created via the default\n",
+      "     |      ``out=None``, locations within it where the condition is False will\n",
+      "     |      remain uninitialized.\n",
+      "     |  **kwargs\n",
+      "     |      For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.\n",
+      "     |  \n",
+      "     |  Returns\n",
+      "     |  -------\n",
+      "     |  r : ndarray or tuple of ndarray\n",
+      "     |      `r` will have the shape that the arrays in `x` broadcast to; if `out` is\n",
+      "     |      provided, it will be returned. If not, `r` will be allocated and\n",
+      "     |      may contain uninitialized values. If the function has more than one\n",
+      "     |      output, then the result will be a tuple of arrays.\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __call__(self, /, *args, **kwargs)\n",
+      "     |      Call self as a function.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  accumulate(...)\n",
+      "     |      accumulate(array, axis=0, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Accumulate the result of applying the operator to all elements.\n",
+      "     |      \n",
+      "     |      For a one-dimensional array, accumulate produces results equivalent to::\n",
+      "     |      \n",
+      "     |        r = np.empty(len(A))\n",
+      "     |        t = op.identity        # op = the ufunc being applied to A's  elements\n",
+      "     |        for i in range(len(A)):\n",
+      "     |            t = op(t, A[i])\n",
+      "     |            r[i] = t\n",
+      "     |        return r\n",
+      "     |      \n",
+      "     |      For example, add.accumulate() is equivalent to np.cumsum().\n",
+      "     |      \n",
+      "     |      For a multi-dimensional array, accumulate is applied along only one\n",
+      "     |      axis (axis zero by default; see Examples below) so repeated use is\n",
+      "     |      necessary if one wants to accumulate over multiple axes.\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      array : array_like\n",
+      "     |          The array to act on.\n",
+      "     |      axis : int, optional\n",
+      "     |          The axis along which to apply the accumulation; default is zero.\n",
+      "     |      dtype : data-type code, optional\n",
+      "     |          The data-type used to represent the intermediate results. Defaults\n",
+      "     |          to the data-type of the output array if such is provided, or the\n",
+      "     |          the data-type of the input array if no output array is provided.\n",
+      "     |      out : ndarray, None, or tuple of ndarray and None, optional\n",
+      "     |          A location into which the result is stored. If not provided or None,\n",
+      "     |          a freshly-allocated array is returned. For consistency with\n",
+      "     |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
+      "     |          1-element tuple.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.13.0\n",
+      "     |             Tuples are allowed for keyword argument.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      r : ndarray\n",
+      "     |          The accumulated values. If `out` was supplied, `r` is a reference to\n",
+      "     |          `out`.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      1-D array examples:\n",
+      "     |      \n",
+      "     |      >>> np.add.accumulate([2, 3, 5])\n",
+      "     |      array([ 2,  5, 10])\n",
+      "     |      >>> np.multiply.accumulate([2, 3, 5])\n",
+      "     |      array([ 2,  6, 30])\n",
+      "     |      \n",
+      "     |      2-D array examples:\n",
+      "     |      \n",
+      "     |      >>> I = np.eye(2)\n",
+      "     |      >>> I\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [0.,  1.]])\n",
+      "     |      \n",
+      "     |      Accumulate along axis 0 (rows), down columns:\n",
+      "     |      \n",
+      "     |      >>> np.add.accumulate(I, 0)\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [1.,  1.]])\n",
+      "     |      >>> np.add.accumulate(I) # no axis specified = axis zero\n",
+      "     |      array([[1.,  0.],\n",
+      "     |             [1.,  1.]])\n",
+      "     |      \n",
+      "     |      Accumulate along axis 1 (columns), through rows:\n",
+      "     |      \n",
+      "     |      >>> np.add.accumulate(I, 1)\n",
+      "     |      array([[1.,  1.],\n",
+      "     |             [0.,  1.]])\n",
+      "     |  \n",
+      "     |  at(...)\n",
+      "     |      at(a, indices, b=None)\n",
+      "     |      \n",
+      "     |      Performs unbuffered in place operation on operand 'a' for elements\n",
+      "     |      specified by 'indices'. For addition ufunc, this method is equivalent to\n",
+      "     |      ``a[indices] += b``, except that results are accumulated for elements that\n",
+      "     |      are indexed more than once. For example, ``a[[0,0]] += 1`` will only\n",
+      "     |      increment the first element once because of buffering, whereas\n",
+      "     |      ``add.at(a, [0,0], 1)`` will increment the first element twice.\n",
+      "     |      \n",
+      "     |      .. versionadded:: 1.8.0\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      a : array_like\n",
+      "     |          The array to perform in place operation on.\n",
+      "     |      indices : array_like or tuple\n",
+      "     |          Array like index object or slice object for indexing into first\n",
+      "     |          operand. If first operand has multiple dimensions, indices can be a\n",
+      "     |          tuple of array like index objects or slice objects.\n",
+      "     |      b : array_like\n",
+      "     |          Second operand for ufuncs requiring two operands. Operand must be\n",
+      "     |          broadcastable over first operand after indexing or slicing.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      Set items 0 and 1 to their negative values:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([1, 2, 3, 4])\n",
+      "     |      >>> np.negative.at(a, [0, 1])\n",
+      "     |      >>> a\n",
+      "     |      array([-1, -2,  3,  4])\n",
+      "     |      \n",
+      "     |      Increment items 0 and 1, and increment item 2 twice:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([1, 2, 3, 4])\n",
+      "     |      >>> np.add.at(a, [0, 1, 2, 2], 1)\n",
+      "     |      >>> a\n",
+      "     |      array([2, 3, 5, 4])\n",
+      "     |      \n",
+      "     |      Add items 0 and 1 in first array to second array,\n",
+      "     |      and store results in first array:\n",
+      "     |      \n",
+      "     |      >>> a = np.array([1, 2, 3, 4])\n",
+      "     |      >>> b = np.array([1, 2])\n",
+      "     |      >>> np.add.at(a, [0, 1], b)\n",
+      "     |      >>> a\n",
+      "     |      array([2, 4, 3, 4])\n",
+      "     |  \n",
+      "     |  outer(...)\n",
+      "     |      outer(A, B, **kwargs)\n",
+      "     |      \n",
+      "     |      Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.\n",
+      "     |      \n",
+      "     |      Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of\n",
+      "     |      ``op.outer(A, B)`` is an array of dimension M + N such that:\n",
+      "     |      \n",
+      "     |      .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =\n",
+      "     |         op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])\n",
+      "     |      \n",
+      "     |      For `A` and `B` one-dimensional, this is equivalent to::\n",
+      "     |      \n",
+      "     |        r = empty(len(A),len(B))\n",
+      "     |        for i in range(len(A)):\n",
+      "     |            for j in range(len(B)):\n",
+      "     |                r[i,j] = op(A[i], B[j]) # op = ufunc in question\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      A : array_like\n",
+      "     |          First array\n",
+      "     |      B : array_like\n",
+      "     |          Second array\n",
+      "     |      kwargs : any\n",
+      "     |          Arguments to pass on to the ufunc. Typically `dtype` or `out`.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      r : ndarray\n",
+      "     |          Output array\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.outer : A less powerful version of ``np.multiply.outer``\n",
+      "     |                    that `ravel`\\ s all inputs to 1D. This exists\n",
+      "     |                    primarily for compatibility with old code.\n",
+      "     |      \n",
+      "     |      tensordot : ``np.tensordot(a, b, axes=((), ()))`` and\n",
+      "     |                  ``np.multiply.outer(a, b)`` behave same for all\n",
+      "     |                  dimensions of a and b.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.multiply.outer([1, 2, 3], [4, 5, 6])\n",
+      "     |      array([[ 4,  5,  6],\n",
+      "     |             [ 8, 10, 12],\n",
+      "     |             [12, 15, 18]])\n",
+      "     |      \n",
+      "     |      A multi-dimensional example:\n",
+      "     |      \n",
+      "     |      >>> A = np.array([[1, 2, 3], [4, 5, 6]])\n",
+      "     |      >>> A.shape\n",
+      "     |      (2, 3)\n",
+      "     |      >>> B = np.array([[1, 2, 3, 4]])\n",
+      "     |      >>> B.shape\n",
+      "     |      (1, 4)\n",
+      "     |      >>> C = np.multiply.outer(A, B)\n",
+      "     |      >>> C.shape; C\n",
+      "     |      (2, 3, 1, 4)\n",
+      "     |      array([[[[ 1,  2,  3,  4]],\n",
+      "     |              [[ 2,  4,  6,  8]],\n",
+      "     |              [[ 3,  6,  9, 12]]],\n",
+      "     |             [[[ 4,  8, 12, 16]],\n",
+      "     |              [[ 5, 10, 15, 20]],\n",
+      "     |              [[ 6, 12, 18, 24]]]])\n",
+      "     |  \n",
+      "     |  reduce(...)\n",
+      "     |      reduce(a, axis=0, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      "     |      \n",
+      "     |      Reduces `a`'s dimension by one, by applying ufunc along one axis.\n",
+      "     |      \n",
+      "     |      Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then\n",
+      "     |      :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =\n",
+      "     |      the result of iterating `j` over :math:`range(N_i)`, cumulatively applying\n",
+      "     |      ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.\n",
+      "     |      For a one-dimensional array, reduce produces results equivalent to:\n",
+      "     |      ::\n",
+      "     |      \n",
+      "     |       r = op.identity # op = ufunc\n",
+      "     |       for i in range(len(A)):\n",
+      "     |         r = op(r, A[i])\n",
+      "     |       return r\n",
+      "     |      \n",
+      "     |      For example, add.reduce() is equivalent to sum().\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      a : array_like\n",
+      "     |          The array to act on.\n",
+      "     |      axis : None or int or tuple of ints, optional\n",
+      "     |          Axis or axes along which a reduction is performed.\n",
+      "     |          The default (`axis` = 0) is perform a reduction over the first\n",
+      "     |          dimension of the input array. `axis` may be negative, in\n",
+      "     |          which case it counts from the last to the first axis.\n",
+      "     |      \n",
+      "     |          .. versionadded:: 1.7.0\n",
+      "     |      \n",
+      "     |          If this is None, a reduction is performed over all the axes.\n",
+      "     |          If this is a tuple of ints, a reduction is performed on multiple\n",
+      "     |          axes, instead of a single axis or all the axes as before.\n",
+      "     |      \n",
+      "     |          For operations which are either not commutative or not associative,\n",
+      "     |          doing a reduction over multiple axes is not well-defined. The\n",
+      "     |          ufuncs do not currently raise an exception in this case, but will\n",
+      "     |          likely do so in the future.\n",
+      "     |      dtype : data-type code, optional\n",
+      "     |          The type used to represent the intermediate results. Defaults\n",
+      "     |          to the data-type of the output array if this is provided, or\n",
+      "     |          the data-type of the input array if no output array is provided.\n",
+      "     |      out : ndarray, None, or tuple of ndarray and None, optional\n",
+      "     |          A location into which the result is stored. If not provided or None,\n",
+      "     |          a freshly-allocated array is returned. For consistency with\n",
+      "     |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
+      "     |          1-element tuple.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.13.0\n",
+      "     |             Tuples are allowed for keyword argument.\n",
+      "     |      keepdims : bool, optional\n",
+      "     |          If this is set to True, the axes which are reduced are left\n",
+      "     |          in the result as dimensions with size one. With this option,\n",
+      "     |          the result will broadcast correctly against the original `arr`.\n",
+      "     |      \n",
+      "     |          .. versionadded:: 1.7.0\n",
+      "     |      initial : scalar, optional\n",
+      "     |          The value with which to start the reduction.\n",
+      "     |          If the ufunc has no identity or the dtype is object, this defaults\n",
+      "     |          to None - otherwise it defaults to ufunc.identity.\n",
+      "     |          If ``None`` is given, the first element of the reduction is used,\n",
+      "     |          and an error is thrown if the reduction is empty.\n",
+      "     |      \n",
+      "     |          .. versionadded:: 1.15.0\n",
+      "     |      \n",
+      "     |      where : array_like of bool, optional\n",
+      "     |          A boolean array which is broadcasted to match the dimensions\n",
+      "     |          of `a`, and selects elements to include in the reduction. Note\n",
+      "     |          that for ufuncs like ``minimum`` that do not have an identity\n",
+      "     |          defined, one has to pass in also ``initial``.\n",
+      "     |      \n",
+      "     |          .. versionadded:: 1.17.0\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      r : ndarray\n",
+      "     |          The reduced array. If `out` was supplied, `r` is a reference to it.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.multiply.reduce([2,3,5])\n",
+      "     |      30\n",
+      "     |      \n",
+      "     |      A multi-dimensional array example:\n",
+      "     |      \n",
+      "     |      >>> X = np.arange(8).reshape((2,2,2))\n",
+      "     |      >>> X\n",
+      "     |      array([[[0, 1],\n",
+      "     |              [2, 3]],\n",
+      "     |             [[4, 5],\n",
+      "     |              [6, 7]]])\n",
+      "     |      >>> np.add.reduce(X, 0)\n",
+      "     |      array([[ 4,  6],\n",
+      "     |             [ 8, 10]])\n",
+      "     |      >>> np.add.reduce(X) # confirm: default axis value is 0\n",
+      "     |      array([[ 4,  6],\n",
+      "     |             [ 8, 10]])\n",
+      "     |      >>> np.add.reduce(X, 1)\n",
+      "     |      array([[ 2,  4],\n",
+      "     |             [10, 12]])\n",
+      "     |      >>> np.add.reduce(X, 2)\n",
+      "     |      array([[ 1,  5],\n",
+      "     |             [ 9, 13]])\n",
+      "     |      \n",
+      "     |      You can use the ``initial`` keyword argument to initialize the reduction\n",
+      "     |      with a different value, and ``where`` to select specific elements to include:\n",
+      "     |      \n",
+      "     |      >>> np.add.reduce([10], initial=5)\n",
+      "     |      15\n",
+      "     |      >>> np.add.reduce(np.ones((2, 2, 2)), axis=(0, 2), initial=10)\n",
+      "     |      array([14., 14.])\n",
+      "     |      >>> a = np.array([10., np.nan, 10])\n",
+      "     |      >>> np.add.reduce(a, where=~np.isnan(a))\n",
+      "     |      20.0\n",
+      "     |      \n",
+      "     |      Allows reductions of empty arrays where they would normally fail, i.e.\n",
+      "     |      for ufuncs without an identity.\n",
+      "     |      \n",
+      "     |      >>> np.minimum.reduce([], initial=np.inf)\n",
+      "     |      inf\n",
+      "     |      >>> np.minimum.reduce([[1., 2.], [3., 4.]], initial=10., where=[True, False])\n",
+      "     |      array([ 1., 10.])\n",
+      "     |      >>> np.minimum.reduce([])\n",
+      "     |      Traceback (most recent call last):\n",
+      "     |          ...\n",
+      "     |      ValueError: zero-size array to reduction operation minimum which has no identity\n",
+      "     |  \n",
+      "     |  reduceat(...)\n",
+      "     |      reduceat(a, indices, axis=0, dtype=None, out=None)\n",
+      "     |      \n",
+      "     |      Performs a (local) reduce with specified slices over a single axis.\n",
+      "     |      \n",
+      "     |      For i in ``range(len(indices))``, `reduceat` computes\n",
+      "     |      ``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th\n",
+      "     |      generalized \"row\" parallel to `axis` in the final result (i.e., in a\n",
+      "     |      2-D array, for example, if `axis = 0`, it becomes the i-th row, but if\n",
+      "     |      `axis = 1`, it becomes the i-th column).  There are three exceptions to this:\n",
+      "     |      \n",
+      "     |      * when ``i = len(indices) - 1`` (so for the last index),\n",
+      "     |        ``indices[i+1] = a.shape[axis]``.\n",
+      "     |      * if ``indices[i] >= indices[i + 1]``, the i-th generalized \"row\" is\n",
+      "     |        simply ``a[indices[i]]``.\n",
+      "     |      * if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised.\n",
+      "     |      \n",
+      "     |      The shape of the output depends on the size of `indices`, and may be\n",
+      "     |      larger than `a` (this happens if ``len(indices) > a.shape[axis]``).\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      a : array_like\n",
+      "     |          The array to act on.\n",
+      "     |      indices : array_like\n",
+      "     |          Paired indices, comma separated (not colon), specifying slices to\n",
+      "     |          reduce.\n",
+      "     |      axis : int, optional\n",
+      "     |          The axis along which to apply the reduceat.\n",
+      "     |      dtype : data-type code, optional\n",
+      "     |          The type used to represent the intermediate results. Defaults\n",
+      "     |          to the data type of the output array if this is provided, or\n",
+      "     |          the data type of the input array if no output array is provided.\n",
+      "     |      out : ndarray, None, or tuple of ndarray and None, optional\n",
+      "     |          A location into which the result is stored. If not provided or None,\n",
+      "     |          a freshly-allocated array is returned. For consistency with\n",
+      "     |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
+      "     |          1-element tuple.\n",
+      "     |      \n",
+      "     |          .. versionchanged:: 1.13.0\n",
+      "     |             Tuples are allowed for keyword argument.\n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      r : ndarray\n",
+      "     |          The reduced values. If `out` was supplied, `r` is a reference to\n",
+      "     |          `out`.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      A descriptive example:\n",
+      "     |      \n",
+      "     |      If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as\n",
+      "     |      ``ufunc.reduceat(a, indices)[::2]`` where `indices` is\n",
+      "     |      ``range(len(array) - 1)`` with a zero placed\n",
+      "     |      in every other element:\n",
+      "     |      ``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.\n",
+      "     |      \n",
+      "     |      Don't be fooled by this attribute's name: `reduceat(a)` is not\n",
+      "     |      necessarily smaller than `a`.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      To take the running sum of four successive values:\n",
+      "     |      \n",
+      "     |      >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]\n",
+      "     |      array([ 6, 10, 14, 18])\n",
+      "     |      \n",
+      "     |      A 2-D example:\n",
+      "     |      \n",
+      "     |      >>> x = np.linspace(0, 15, 16).reshape(4,4)\n",
+      "     |      >>> x\n",
+      "     |      array([[ 0.,   1.,   2.,   3.],\n",
+      "     |             [ 4.,   5.,   6.,   7.],\n",
+      "     |             [ 8.,   9.,  10.,  11.],\n",
+      "     |             [12.,  13.,  14.,  15.]])\n",
+      "     |      \n",
+      "     |      ::\n",
+      "     |      \n",
+      "     |       # reduce such that the result has the following five rows:\n",
+      "     |       # [row1 + row2 + row3]\n",
+      "     |       # [row4]\n",
+      "     |       # [row2]\n",
+      "     |       # [row3]\n",
+      "     |       # [row1 + row2 + row3 + row4]\n",
+      "     |      \n",
+      "     |      >>> np.add.reduceat(x, [0, 3, 1, 2, 0])\n",
+      "     |      array([[12.,  15.,  18.,  21.],\n",
+      "     |             [12.,  13.,  14.,  15.],\n",
+      "     |             [ 4.,   5.,   6.,   7.],\n",
+      "     |             [ 8.,   9.,  10.,  11.],\n",
+      "     |             [24.,  28.,  32.,  36.]])\n",
+      "     |      \n",
+      "     |      ::\n",
+      "     |      \n",
+      "     |       # reduce such that result has the following two columns:\n",
+      "     |       # [col1 * col2 * col3, col4]\n",
+      "     |      \n",
+      "     |      >>> np.multiply.reduceat(x, [0, 3], 1)\n",
+      "     |      array([[   0.,     3.],\n",
+      "     |             [ 120.,     7.],\n",
+      "     |             [ 720.,    11.],\n",
+      "     |             [2184.,    15.]])\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  identity\n",
+      "     |      The identity value.\n",
+      "     |      \n",
+      "     |      Data attribute containing the identity element for the ufunc, if it has one.\n",
+      "     |      If it does not, the attribute value is None.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.add.identity\n",
+      "     |      0\n",
+      "     |      >>> np.multiply.identity\n",
+      "     |      1\n",
+      "     |      >>> np.power.identity\n",
+      "     |      1\n",
+      "     |      >>> print(np.exp.identity)\n",
+      "     |      None\n",
+      "     |  \n",
+      "     |  nargs\n",
+      "     |      The number of arguments.\n",
+      "     |      \n",
+      "     |      Data attribute containing the number of arguments the ufunc takes, including\n",
+      "     |      optional ones.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Typically this value will be one more than what you might expect because all\n",
+      "     |      ufuncs take  the optional \"out\" argument.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.add.nargs\n",
+      "     |      3\n",
+      "     |      >>> np.multiply.nargs\n",
+      "     |      3\n",
+      "     |      >>> np.power.nargs\n",
+      "     |      3\n",
+      "     |      >>> np.exp.nargs\n",
+      "     |      2\n",
+      "     |  \n",
+      "     |  nin\n",
+      "     |      The number of inputs.\n",
+      "     |      \n",
+      "     |      Data attribute containing the number of arguments the ufunc treats as input.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.add.nin\n",
+      "     |      2\n",
+      "     |      >>> np.multiply.nin\n",
+      "     |      2\n",
+      "     |      >>> np.power.nin\n",
+      "     |      2\n",
+      "     |      >>> np.exp.nin\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  nout\n",
+      "     |      The number of outputs.\n",
+      "     |      \n",
+      "     |      Data attribute containing the number of arguments the ufunc treats as output.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Since all ufuncs can take output arguments, this will always be (at least) 1.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.add.nout\n",
+      "     |      1\n",
+      "     |      >>> np.multiply.nout\n",
+      "     |      1\n",
+      "     |      >>> np.power.nout\n",
+      "     |      1\n",
+      "     |      >>> np.exp.nout\n",
+      "     |      1\n",
+      "     |  \n",
+      "     |  ntypes\n",
+      "     |      The number of types.\n",
+      "     |      \n",
+      "     |      The number of numerical NumPy types - of which there are 18 total - on which\n",
+      "     |      the ufunc can operate.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ufunc.types\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.add.ntypes\n",
+      "     |      18\n",
+      "     |      >>> np.multiply.ntypes\n",
+      "     |      18\n",
+      "     |      >>> np.power.ntypes\n",
+      "     |      17\n",
+      "     |      >>> np.exp.ntypes\n",
+      "     |      7\n",
+      "     |      >>> np.remainder.ntypes\n",
+      "     |      14\n",
+      "     |  \n",
+      "     |  signature\n",
+      "     |      Definition of the core elements a generalized ufunc operates on.\n",
+      "     |      \n",
+      "     |      The signature determines how the dimensions of each input/output array\n",
+      "     |      are split into core and loop dimensions:\n",
+      "     |      \n",
+      "     |      1. Each dimension in the signature is matched to a dimension of the\n",
+      "     |         corresponding passed-in array, starting from the end of the shape tuple.\n",
+      "     |      2. Core dimensions assigned to the same label in the signature must have\n",
+      "     |         exactly matching sizes, no broadcasting is performed.\n",
+      "     |      3. The core dimensions are removed from all inputs and the remaining\n",
+      "     |         dimensions are broadcast together, defining the loop dimensions.\n",
+      "     |      \n",
+      "     |      Notes\n",
+      "     |      -----\n",
+      "     |      Generalized ufuncs are used internally in many linalg functions, and in\n",
+      "     |      the testing suite; the examples below are taken from these.\n",
+      "     |      For ufuncs that operate on scalars, the signature is None, which is\n",
+      "     |      equivalent to '()' for every argument.\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.core.umath_tests.matrix_multiply.signature\n",
+      "     |      '(m,n),(n,p)->(m,p)'\n",
+      "     |      >>> np.linalg._umath_linalg.det.signature\n",
+      "     |      '(m,m)->()'\n",
+      "     |      >>> np.add.signature is None\n",
+      "     |      True  # equivalent to '(),()->()'\n",
+      "     |  \n",
+      "     |  types\n",
+      "     |      Returns a list with types grouped input->output.\n",
+      "     |      \n",
+      "     |      Data attribute listing the data-type \"Domain-Range\" groupings the ufunc can\n",
+      "     |      deliver. The data-types are given using the character codes.\n",
+      "     |      \n",
+      "     |      See Also\n",
+      "     |      --------\n",
+      "     |      numpy.ufunc.ntypes\n",
+      "     |      \n",
+      "     |      Examples\n",
+      "     |      --------\n",
+      "     |      >>> np.add.types\n",
+      "     |      ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n",
+      "     |      'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n",
+      "     |      'GG->G', 'OO->O']\n",
+      "     |      \n",
+      "     |      >>> np.multiply.types\n",
+      "     |      ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n",
+      "     |      'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n",
+      "     |      'GG->G', 'OO->O']\n",
+      "     |      \n",
+      "     |      >>> np.power.types\n",
+      "     |      ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n",
+      "     |      'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',\n",
+      "     |      'OO->O']\n",
+      "     |      \n",
+      "     |      >>> np.exp.types\n",
+      "     |      ['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']\n",
+      "     |      \n",
+      "     |      >>> np.remainder.types\n",
+      "     |      ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n",
+      "     |      'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']\n",
+      "    \n",
+      "    uint = class uint32(unsignedinteger)\n",
+      "     |  Unsigned integer type, compatible with C ``unsigned long``.\n",
+      "     |  Character code: ``'L'``.\n",
+      "     |  Canonical name: ``np.uint``.\n",
+      "     |  Alias *on this platform*: ``np.uint32``: 32-bit unsigned integer (0 to 4294967295).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint32\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    uint0 = class uint64(unsignedinteger)\n",
+      "     |  Signed integer type, compatible with C ``unsigned long long``.\n",
+      "     |  Character code: ``'Q'``.\n",
+      "     |  Canonical name: ``np.ulonglong``.\n",
+      "     |  Alias *on this platform*: ``np.uint64``: 64-bit unsigned integer (0 to 18446744073709551615).\n",
+      "     |  Alias *on this platform*: ``np.uintp``: Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint64\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class uint16(unsignedinteger)\n",
+      "     |  Unsigned integer type, compatible with C ``unsigned short``.\n",
+      "     |  Character code: ``'H'``.\n",
+      "     |  Canonical name: ``np.ushort``.\n",
+      "     |  Alias *on this platform*: ``np.uint16``: 16-bit unsigned integer (0 to 65535).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint16\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class uint32(unsignedinteger)\n",
+      "     |  Unsigned integer type, compatible with C ``unsigned long``.\n",
+      "     |  Character code: ``'L'``.\n",
+      "     |  Canonical name: ``np.uint``.\n",
+      "     |  Alias *on this platform*: ``np.uint32``: 32-bit unsigned integer (0 to 4294967295).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint32\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class uint64(unsignedinteger)\n",
+      "     |  Signed integer type, compatible with C ``unsigned long long``.\n",
+      "     |  Character code: ``'Q'``.\n",
+      "     |  Canonical name: ``np.ulonglong``.\n",
+      "     |  Alias *on this platform*: ``np.uint64``: 64-bit unsigned integer (0 to 18446744073709551615).\n",
+      "     |  Alias *on this platform*: ``np.uintp``: Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint64\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class uint8(unsignedinteger)\n",
+      "     |  Unsigned integer type, compatible with C ``unsigned char``.\n",
+      "     |  Character code: ``'B'``.\n",
+      "     |  Canonical name: ``np.ubyte``.\n",
+      "     |  Alias *on this platform*: ``np.uint8``: 8-bit unsigned integer (0 to 255).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint8\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class uintc(unsignedinteger)\n",
+      "     |  Unsigned integer type, compatible with C ``unsigned int``.\n",
+      "     |  Character code: ``'I'``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uintc\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    uintp = class uint64(unsignedinteger)\n",
+      "     |  Signed integer type, compatible with C ``unsigned long long``.\n",
+      "     |  Character code: ``'Q'``.\n",
+      "     |  Canonical name: ``np.ulonglong``.\n",
+      "     |  Alias *on this platform*: ``np.uint64``: 64-bit unsigned integer (0 to 18446744073709551615).\n",
+      "     |  Alias *on this platform*: ``np.uintp``: Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint64\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    ulonglong = class uint64(unsignedinteger)\n",
+      "     |  Signed integer type, compatible with C ``unsigned long long``.\n",
+      "     |  Character code: ``'Q'``.\n",
+      "     |  Canonical name: ``np.ulonglong``.\n",
+      "     |  Alias *on this platform*: ``np.uint64``: 64-bit unsigned integer (0 to 18446744073709551615).\n",
+      "     |  Alias *on this platform*: ``np.uintp``: Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint64\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    unicode_ = class str_(builtins.str, character)\n",
+      "     |  str(object='') -> str\n",
+      "     |  str(bytes_or_buffer[, encoding[, errors]]) -> str\n",
+      "     |  \n",
+      "     |  Create a new string object from the given object. If encoding or\n",
+      "     |  errors is specified, then the object must expose a data buffer\n",
+      "     |  that will be decoded using the given encoding and error handler.\n",
+      "     |  Otherwise, returns the result of object.__str__() (if defined)\n",
+      "     |  or repr(object).\n",
+      "     |  encoding defaults to sys.getdefaultencoding().\n",
+      "     |  errors defaults to 'strict'.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      str_\n",
+      "     |      builtins.str\n",
+      "     |      character\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from builtins.str:\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __contains__(self, key, /)\n",
+      "     |      Return key in self.\n",
+      "     |  \n",
+      "     |  __format__(self, format_spec, /)\n",
+      "     |      Return a formatted version of the string as described by format_spec.\n",
+      "     |  \n",
+      "     |  __getattribute__(self, name, /)\n",
+      "     |      Return getattr(self, name).\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __getnewargs__(...)\n",
+      "     |  \n",
+      "     |  __iter__(self, /)\n",
+      "     |      Implement iter(self).\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __sizeof__(self, /)\n",
+      "     |      Return the size of the string in memory, in bytes.\n",
+      "     |  \n",
+      "     |  capitalize(self, /)\n",
+      "     |      Return a capitalized version of the string.\n",
+      "     |      \n",
+      "     |      More specifically, make the first character have upper case and the rest lower\n",
+      "     |      case.\n",
+      "     |  \n",
+      "     |  casefold(self, /)\n",
+      "     |      Return a version of the string suitable for caseless comparisons.\n",
+      "     |  \n",
+      "     |  center(self, width, fillchar=' ', /)\n",
+      "     |      Return a centered string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  count(...)\n",
+      "     |      S.count(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the number of non-overlapping occurrences of substring sub in\n",
+      "     |      string S[start:end].  Optional arguments start and end are\n",
+      "     |      interpreted as in slice notation.\n",
+      "     |  \n",
+      "     |  encode(self, /, encoding='utf-8', errors='strict')\n",
+      "     |      Encode the string using the codec registered for encoding.\n",
+      "     |      \n",
+      "     |      encoding\n",
+      "     |        The encoding in which to encode the string.\n",
+      "     |      errors\n",
+      "     |        The error handling scheme to use for encoding errors.\n",
+      "     |        The default is 'strict' meaning that encoding errors raise a\n",
+      "     |        UnicodeEncodeError.  Other possible values are 'ignore', 'replace' and\n",
+      "     |        'xmlcharrefreplace' as well as any other name registered with\n",
+      "     |        codecs.register_error that can handle UnicodeEncodeErrors.\n",
+      "     |  \n",
+      "     |  endswith(...)\n",
+      "     |      S.endswith(suffix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if S ends with the specified suffix, False otherwise.\n",
+      "     |      With optional start, test S beginning at that position.\n",
+      "     |      With optional end, stop comparing S at that position.\n",
+      "     |      suffix can also be a tuple of strings to try.\n",
+      "     |  \n",
+      "     |  expandtabs(self, /, tabsize=8)\n",
+      "     |      Return a copy where all tab characters are expanded using spaces.\n",
+      "     |      \n",
+      "     |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
+      "     |  \n",
+      "     |  find(...)\n",
+      "     |      S.find(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  format(...)\n",
+      "     |      S.format(*args, **kwargs) -> str\n",
+      "     |      \n",
+      "     |      Return a formatted version of S, using substitutions from args and kwargs.\n",
+      "     |      The substitutions are identified by braces ('{' and '}').\n",
+      "     |  \n",
+      "     |  format_map(...)\n",
+      "     |      S.format_map(mapping) -> str\n",
+      "     |      \n",
+      "     |      Return a formatted version of S, using substitutions from mapping.\n",
+      "     |      The substitutions are identified by braces ('{' and '}').\n",
+      "     |  \n",
+      "     |  index(...)\n",
+      "     |      S.index(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the lowest index in S where substring sub is found, \n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the substring is not found.\n",
+      "     |  \n",
+      "     |  isalnum(self, /)\n",
+      "     |      Return True if the string is an alpha-numeric string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is alpha-numeric if all characters in the string are alpha-numeric and\n",
+      "     |      there is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isalpha(self, /)\n",
+      "     |      Return True if the string is an alphabetic string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is alphabetic if all characters in the string are alphabetic and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isascii(self, /)\n",
+      "     |      Return True if all characters in the string are ASCII, False otherwise.\n",
+      "     |      \n",
+      "     |      ASCII characters have code points in the range U+0000-U+007F.\n",
+      "     |      Empty string is ASCII too.\n",
+      "     |  \n",
+      "     |  isdecimal(self, /)\n",
+      "     |      Return True if the string is a decimal string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is a decimal string if all characters in the string are decimal and\n",
+      "     |      there is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isdigit(self, /)\n",
+      "     |      Return True if the string is a digit string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is a digit string if all characters in the string are digits and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  isidentifier(self, /)\n",
+      "     |      Return True if the string is a valid Python identifier, False otherwise.\n",
+      "     |      \n",
+      "     |      Use keyword.iskeyword() to test for reserved identifiers such as \"def\" and\n",
+      "     |      \"class\".\n",
+      "     |  \n",
+      "     |  islower(self, /)\n",
+      "     |      Return True if the string is a lowercase string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is lowercase if all cased characters in the string are lowercase and\n",
+      "     |      there is at least one cased character in the string.\n",
+      "     |  \n",
+      "     |  isnumeric(self, /)\n",
+      "     |      Return True if the string is a numeric string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is numeric if all characters in the string are numeric and there is at\n",
+      "     |      least one character in the string.\n",
+      "     |  \n",
+      "     |  isprintable(self, /)\n",
+      "     |      Return True if the string is printable, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is printable if all of its characters are considered printable in\n",
+      "     |      repr() or if it is empty.\n",
+      "     |  \n",
+      "     |  isspace(self, /)\n",
+      "     |      Return True if the string is a whitespace string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is whitespace if all characters in the string are whitespace and there\n",
+      "     |      is at least one character in the string.\n",
+      "     |  \n",
+      "     |  istitle(self, /)\n",
+      "     |      Return True if the string is a title-cased string, False otherwise.\n",
+      "     |      \n",
+      "     |      In a title-cased string, upper- and title-case characters may only\n",
+      "     |      follow uncased characters and lowercase characters only cased ones.\n",
+      "     |  \n",
+      "     |  isupper(self, /)\n",
+      "     |      Return True if the string is an uppercase string, False otherwise.\n",
+      "     |      \n",
+      "     |      A string is uppercase if all cased characters in the string are uppercase and\n",
+      "     |      there is at least one cased character in the string.\n",
+      "     |  \n",
+      "     |  join(self, iterable, /)\n",
+      "     |      Concatenate any number of strings.\n",
+      "     |      \n",
+      "     |      The string whose method is called is inserted in between each given string.\n",
+      "     |      The result is returned as a new string.\n",
+      "     |      \n",
+      "     |      Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n",
+      "     |  \n",
+      "     |  ljust(self, width, fillchar=' ', /)\n",
+      "     |      Return a left-justified string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  lower(self, /)\n",
+      "     |      Return a copy of the string converted to lowercase.\n",
+      "     |  \n",
+      "     |  lstrip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with leading whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  partition(self, sep, /)\n",
+      "     |      Partition the string into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator in the string.  If the separator is found,\n",
+      "     |      returns a 3-tuple containing the part before the separator, the separator\n",
+      "     |      itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing the original string\n",
+      "     |      and two empty strings.\n",
+      "     |  \n",
+      "     |  replace(self, old, new, count=-1, /)\n",
+      "     |      Return a copy with all occurrences of substring old replaced by new.\n",
+      "     |      \n",
+      "     |        count\n",
+      "     |          Maximum number of occurrences to replace.\n",
+      "     |          -1 (the default value) means replace all occurrences.\n",
+      "     |      \n",
+      "     |      If the optional argument count is given, only the first count occurrences are\n",
+      "     |      replaced.\n",
+      "     |  \n",
+      "     |  rfind(...)\n",
+      "     |      S.rfind(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Return -1 on failure.\n",
+      "     |  \n",
+      "     |  rindex(...)\n",
+      "     |      S.rindex(sub[, start[, end]]) -> int\n",
+      "     |      \n",
+      "     |      Return the highest index in S where substring sub is found,\n",
+      "     |      such that sub is contained within S[start:end].  Optional\n",
+      "     |      arguments start and end are interpreted as in slice notation.\n",
+      "     |      \n",
+      "     |      Raises ValueError when the substring is not found.\n",
+      "     |  \n",
+      "     |  rjust(self, width, fillchar=' ', /)\n",
+      "     |      Return a right-justified string of length width.\n",
+      "     |      \n",
+      "     |      Padding is done using the specified fill character (default is a space).\n",
+      "     |  \n",
+      "     |  rpartition(self, sep, /)\n",
+      "     |      Partition the string into three parts using the given separator.\n",
+      "     |      \n",
+      "     |      This will search for the separator in the string, starting at the end. If\n",
+      "     |      the separator is found, returns a 3-tuple containing the part before the\n",
+      "     |      separator, the separator itself, and the part after it.\n",
+      "     |      \n",
+      "     |      If the separator is not found, returns a 3-tuple containing two empty strings\n",
+      "     |      and the original string.\n",
+      "     |  \n",
+      "     |  rsplit(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the words in the string, using sep as the delimiter string.\n",
+      "     |      \n",
+      "     |        sep\n",
+      "     |          The delimiter according which to split the string.\n",
+      "     |          None (the default value) means split according to any whitespace,\n",
+      "     |          and discard empty strings from the result.\n",
+      "     |        maxsplit\n",
+      "     |          Maximum number of splits to do.\n",
+      "     |          -1 (the default value) means no limit.\n",
+      "     |      \n",
+      "     |      Splits are done starting at the end of the string and working to the front.\n",
+      "     |  \n",
+      "     |  rstrip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with trailing whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  split(self, /, sep=None, maxsplit=-1)\n",
+      "     |      Return a list of the words in the string, using sep as the delimiter string.\n",
+      "     |      \n",
+      "     |      sep\n",
+      "     |        The delimiter according which to split the string.\n",
+      "     |        None (the default value) means split according to any whitespace,\n",
+      "     |        and discard empty strings from the result.\n",
+      "     |      maxsplit\n",
+      "     |        Maximum number of splits to do.\n",
+      "     |        -1 (the default value) means no limit.\n",
+      "     |  \n",
+      "     |  splitlines(self, /, keepends=False)\n",
+      "     |      Return a list of the lines in the string, breaking at line boundaries.\n",
+      "     |      \n",
+      "     |      Line breaks are not included in the resulting list unless keepends is given and\n",
+      "     |      true.\n",
+      "     |  \n",
+      "     |  startswith(...)\n",
+      "     |      S.startswith(prefix[, start[, end]]) -> bool\n",
+      "     |      \n",
+      "     |      Return True if S starts with the specified prefix, False otherwise.\n",
+      "     |      With optional start, test S beginning at that position.\n",
+      "     |      With optional end, stop comparing S at that position.\n",
+      "     |      prefix can also be a tuple of strings to try.\n",
+      "     |  \n",
+      "     |  strip(self, chars=None, /)\n",
+      "     |      Return a copy of the string with leading and trailing whitespace removed.\n",
+      "     |      \n",
+      "     |      If chars is given and not None, remove characters in chars instead.\n",
+      "     |  \n",
+      "     |  swapcase(self, /)\n",
+      "     |      Convert uppercase characters to lowercase and lowercase characters to uppercase.\n",
+      "     |  \n",
+      "     |  title(self, /)\n",
+      "     |      Return a version of the string where each word is titlecased.\n",
+      "     |      \n",
+      "     |      More specifically, words start with uppercased characters and all remaining\n",
+      "     |      cased characters have lower case.\n",
+      "     |  \n",
+      "     |  translate(self, table, /)\n",
+      "     |      Replace each character in the string using the given translation table.\n",
+      "     |      \n",
+      "     |        table\n",
+      "     |          Translation table, which must be a mapping of Unicode ordinals to\n",
+      "     |          Unicode ordinals, strings, or None.\n",
+      "     |      \n",
+      "     |      The table must implement lookup/indexing via __getitem__, for instance a\n",
+      "     |      dictionary or list.  If this operation raises LookupError, the character is\n",
+      "     |      left untouched.  Characters mapped to None are deleted.\n",
+      "     |  \n",
+      "     |  upper(self, /)\n",
+      "     |      Return a copy of the string converted to uppercase.\n",
+      "     |  \n",
+      "     |  zfill(self, width, /)\n",
+      "     |      Pad a numeric string with zeros on the left, to fill a field of the given width.\n",
+      "     |      \n",
+      "     |      The string is never truncated.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods inherited from builtins.str:\n",
+      "     |  \n",
+      "     |  maketrans(x, y=None, z=None, /)\n",
+      "     |      Return a translation table usable for str.translate().\n",
+      "     |      \n",
+      "     |      If there is only one argument, it must be a dictionary mapping Unicode\n",
+      "     |      ordinals (integers) or characters to Unicode ordinals, strings or None.\n",
+      "     |      Character keys will be then converted to ordinals.\n",
+      "     |      If there are two arguments, they must be strings of equal length, and\n",
+      "     |      in the resulting dictionary, each character in x will be mapped to the\n",
+      "     |      character at the same position in y. If there is a third argument, it\n",
+      "     |      must be a string, whose characters will be mapped to None in the result.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class unsignedinteger(integer)\n",
+      "     |  Abstract base class of all unsigned integer scalar types.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes inherited from generic:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "    \n",
+      "    ushort = class uint16(unsignedinteger)\n",
+      "     |  Unsigned integer type, compatible with C ``unsigned short``.\n",
+      "     |  Character code: ``'H'``.\n",
+      "     |  Canonical name: ``np.ushort``.\n",
+      "     |  Alias *on this platform*: ``np.uint16``: 16-bit unsigned integer (0 to 65535).\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      uint16\n",
+      "     |      unsignedinteger\n",
+      "     |      integer\n",
+      "     |      number\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __index__(self, /)\n",
+      "     |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from integer:\n",
+      "     |  \n",
+      "     |  __round__(...)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from integer:\n",
+      "     |  \n",
+      "     |  denominator\n",
+      "     |      denominator of value (1)\n",
+      "     |  \n",
+      "     |  numerator\n",
+      "     |      numerator of value (the value itself)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      get array data-descriptor\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    class vectorize(builtins.object)\n",
+      "     |  vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None)\n",
+      "     |  \n",
+      "     |  vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False,\n",
+      "     |            signature=None)\n",
+      "     |  \n",
+      "     |  Generalized function class.\n",
+      "     |  \n",
+      "     |  Define a vectorized function which takes a nested sequence of objects or\n",
+      "     |  numpy arrays as inputs and returns a single numpy array or a tuple of numpy\n",
+      "     |  arrays. The vectorized function evaluates `pyfunc` over successive tuples\n",
+      "     |  of the input arrays like the python map function, except it uses the\n",
+      "     |  broadcasting rules of numpy.\n",
+      "     |  \n",
+      "     |  The data type of the output of `vectorized` is determined by calling\n",
+      "     |  the function with the first element of the input.  This can be avoided\n",
+      "     |  by specifying the `otypes` argument.\n",
+      "     |  \n",
+      "     |  Parameters\n",
+      "     |  ----------\n",
+      "     |  pyfunc : callable\n",
+      "     |      A python function or method.\n",
+      "     |  otypes : str or list of dtypes, optional\n",
+      "     |      The output data type. It must be specified as either a string of\n",
+      "     |      typecode characters or a list of data type specifiers. There should\n",
+      "     |      be one data type specifier for each output.\n",
+      "     |  doc : str, optional\n",
+      "     |      The docstring for the function. If None, the docstring will be the\n",
+      "     |      ``pyfunc.__doc__``.\n",
+      "     |  excluded : set, optional\n",
+      "     |      Set of strings or integers representing the positional or keyword\n",
+      "     |      arguments for which the function will not be vectorized.  These will be\n",
+      "     |      passed directly to `pyfunc` unmodified.\n",
+      "     |  \n",
+      "     |      .. versionadded:: 1.7.0\n",
+      "     |  \n",
+      "     |  cache : bool, optional\n",
+      "     |     If `True`, then cache the first function call that determines the number\n",
+      "     |     of outputs if `otypes` is not provided.\n",
+      "     |  \n",
+      "     |      .. versionadded:: 1.7.0\n",
+      "     |  \n",
+      "     |  signature : string, optional\n",
+      "     |      Generalized universal function signature, e.g., ``(m,n),(n)->(m)`` for\n",
+      "     |      vectorized matrix-vector multiplication. If provided, ``pyfunc`` will\n",
+      "     |      be called with (and expected to return) arrays with shapes given by the\n",
+      "     |      size of corresponding core dimensions. By default, ``pyfunc`` is\n",
+      "     |      assumed to take scalars as input and output.\n",
+      "     |  \n",
+      "     |      .. versionadded:: 1.12.0\n",
+      "     |  \n",
+      "     |  Returns\n",
+      "     |  -------\n",
+      "     |  vectorized : callable\n",
+      "     |      Vectorized function.\n",
+      "     |  \n",
+      "     |  See Also\n",
+      "     |  --------\n",
+      "     |  frompyfunc : Takes an arbitrary Python function and returns a ufunc\n",
+      "     |  \n",
+      "     |  Notes\n",
+      "     |  -----\n",
+      "     |  The `vectorize` function is provided primarily for convenience, not for\n",
+      "     |  performance. The implementation is essentially a for loop.\n",
+      "     |  \n",
+      "     |  If `otypes` is not specified, then a call to the function with the\n",
+      "     |  first argument will be used to determine the number of outputs.  The\n",
+      "     |  results of this call will be cached if `cache` is `True` to prevent\n",
+      "     |  calling the function twice.  However, to implement the cache, the\n",
+      "     |  original function must be wrapped which will slow down subsequent\n",
+      "     |  calls, so only do this if your function is expensive.\n",
+      "     |  \n",
+      "     |  The new keyword argument interface and `excluded` argument support\n",
+      "     |  further degrades performance.\n",
+      "     |  \n",
+      "     |  References\n",
+      "     |  ----------\n",
+      "     |  .. [1] NumPy Reference, section `Generalized Universal Function API\n",
+      "     |         <https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html>`_.\n",
+      "     |  \n",
+      "     |  Examples\n",
+      "     |  --------\n",
+      "     |  >>> def myfunc(a, b):\n",
+      "     |  ...     \"Return a-b if a>b, otherwise return a+b\"\n",
+      "     |  ...     if a > b:\n",
+      "     |  ...         return a - b\n",
+      "     |  ...     else:\n",
+      "     |  ...         return a + b\n",
+      "     |  \n",
+      "     |  >>> vfunc = np.vectorize(myfunc)\n",
+      "     |  >>> vfunc([1, 2, 3, 4], 2)\n",
+      "     |  array([3, 4, 1, 2])\n",
+      "     |  \n",
+      "     |  The docstring is taken from the input function to `vectorize` unless it\n",
+      "     |  is specified:\n",
+      "     |  \n",
+      "     |  >>> vfunc.__doc__\n",
+      "     |  'Return a-b if a>b, otherwise return a+b'\n",
+      "     |  >>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')\n",
+      "     |  >>> vfunc.__doc__\n",
+      "     |  'Vectorized `myfunc`'\n",
+      "     |  \n",
+      "     |  The output type is determined by evaluating the first element of the input,\n",
+      "     |  unless it is specified:\n",
+      "     |  \n",
+      "     |  >>> out = vfunc([1, 2, 3, 4], 2)\n",
+      "     |  >>> type(out[0])\n",
+      "     |  <class 'numpy.int64'>\n",
+      "     |  >>> vfunc = np.vectorize(myfunc, otypes=[float])\n",
+      "     |  >>> out = vfunc([1, 2, 3, 4], 2)\n",
+      "     |  >>> type(out[0])\n",
+      "     |  <class 'numpy.float64'>\n",
+      "     |  \n",
+      "     |  The `excluded` argument can be used to prevent vectorizing over certain\n",
+      "     |  arguments.  This can be useful for array-like arguments of a fixed length\n",
+      "     |  such as the coefficients for a polynomial as in `polyval`:\n",
+      "     |  \n",
+      "     |  >>> def mypolyval(p, x):\n",
+      "     |  ...     _p = list(p)\n",
+      "     |  ...     res = _p.pop(0)\n",
+      "     |  ...     while _p:\n",
+      "     |  ...         res = res*x + _p.pop(0)\n",
+      "     |  ...     return res\n",
+      "     |  >>> vpolyval = np.vectorize(mypolyval, excluded=['p'])\n",
+      "     |  >>> vpolyval(p=[1, 2, 3], x=[0, 1])\n",
+      "     |  array([3, 6])\n",
+      "     |  \n",
+      "     |  Positional arguments may also be excluded by specifying their position:\n",
+      "     |  \n",
+      "     |  >>> vpolyval.excluded.add(0)\n",
+      "     |  >>> vpolyval([1, 2, 3], x=[0, 1])\n",
+      "     |  array([3, 6])\n",
+      "     |  \n",
+      "     |  The `signature` argument allows for vectorizing functions that act on\n",
+      "     |  non-scalar arrays of fixed length. For example, you can use it for a\n",
+      "     |  vectorized calculation of Pearson correlation coefficient and its p-value:\n",
+      "     |  \n",
+      "     |  >>> import scipy.stats\n",
+      "     |  >>> pearsonr = np.vectorize(scipy.stats.pearsonr,\n",
+      "     |  ...                 signature='(n),(n)->(),()')\n",
+      "     |  >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])\n",
+      "     |  (array([ 1., -1.]), array([ 0.,  0.]))\n",
+      "     |  \n",
+      "     |  Or for a vectorized convolution:\n",
+      "     |  \n",
+      "     |  >>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')\n",
+      "     |  >>> convolve(np.eye(4), [1, 2, 1])\n",
+      "     |  array([[1., 2., 1., 0., 0., 0.],\n",
+      "     |         [0., 1., 2., 1., 0., 0.],\n",
+      "     |         [0., 0., 1., 2., 1., 0.],\n",
+      "     |         [0., 0., 0., 1., 2., 1.]])\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __call__(self, *args, **kwargs)\n",
+      "     |      Return arrays with the results of `pyfunc` broadcast (vectorized) over\n",
+      "     |      `args` and `kwargs` not in `excluded`.\n",
+      "     |  \n",
+      "     |  __init__(self, pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None)\n",
+      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "    \n",
+      "    class void(flexible)\n",
+      "     |  Abstract base class of all scalar types without predefined length.\n",
+      "     |  The actual size of these types depends on the specific `np.dtype`\n",
+      "     |  instantiation.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      void\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      dtype object\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "    \n",
+      "    void0 = class void(flexible)\n",
+      "     |  Abstract base class of all scalar types without predefined length.\n",
+      "     |  The actual size of these types depends on the specific `np.dtype`\n",
+      "     |  instantiation.\n",
+      "     |  \n",
+      "     |  Method resolution order:\n",
+      "     |      void\n",
+      "     |      flexible\n",
+      "     |      generic\n",
+      "     |      builtins.object\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __delitem__(self, key, /)\n",
+      "     |      Delete self[key].\n",
+      "     |  \n",
+      "     |  __eq__(self, value, /)\n",
+      "     |      Return self==value.\n",
+      "     |  \n",
+      "     |  __ge__(self, value, /)\n",
+      "     |      Return self>=value.\n",
+      "     |  \n",
+      "     |  __getitem__(self, key, /)\n",
+      "     |      Return self[key].\n",
+      "     |  \n",
+      "     |  __gt__(self, value, /)\n",
+      "     |      Return self>value.\n",
+      "     |  \n",
+      "     |  __hash__(self, /)\n",
+      "     |      Return hash(self).\n",
+      "     |  \n",
+      "     |  __le__(self, value, /)\n",
+      "     |      Return self<=value.\n",
+      "     |  \n",
+      "     |  __len__(self, /)\n",
+      "     |      Return len(self).\n",
+      "     |  \n",
+      "     |  __lt__(self, value, /)\n",
+      "     |      Return self<value.\n",
+      "     |  \n",
+      "     |  __ne__(self, value, /)\n",
+      "     |      Return self!=value.\n",
+      "     |  \n",
+      "     |  __repr__(self, /)\n",
+      "     |      Return repr(self).\n",
+      "     |  \n",
+      "     |  __setitem__(self, key, value, /)\n",
+      "     |      Set self[key] to value.\n",
+      "     |  \n",
+      "     |  __str__(self, /)\n",
+      "     |      Return str(self).\n",
+      "     |  \n",
+      "     |  getfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setfield(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Static methods defined here:\n",
+      "     |  \n",
+      "     |  __new__(*args, **kwargs) from builtins.type\n",
+      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  base\n",
+      "     |      base object\n",
+      "     |  \n",
+      "     |  dtype\n",
+      "     |      dtype object\n",
+      "     |  \n",
+      "     |  flags\n",
+      "     |      integer value of flags\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Methods inherited from generic:\n",
+      "     |  \n",
+      "     |  __abs__(self, /)\n",
+      "     |      abs(self)\n",
+      "     |  \n",
+      "     |  __add__(self, value, /)\n",
+      "     |      Return self+value.\n",
+      "     |  \n",
+      "     |  __and__(self, value, /)\n",
+      "     |      Return self&value.\n",
+      "     |  \n",
+      "     |  __array__(...)\n",
+      "     |      sc.__array__(dtype) return 0-dim array from scalar with specified dtype\n",
+      "     |  \n",
+      "     |  __array_wrap__(...)\n",
+      "     |      sc.__array_wrap__(obj) return scalar from array\n",
+      "     |  \n",
+      "     |  __bool__(self, /)\n",
+      "     |      self != 0\n",
+      "     |  \n",
+      "     |  __copy__(...)\n",
+      "     |  \n",
+      "     |  __deepcopy__(...)\n",
+      "     |  \n",
+      "     |  __divmod__(self, value, /)\n",
+      "     |      Return divmod(self, value).\n",
+      "     |  \n",
+      "     |  __float__(self, /)\n",
+      "     |      float(self)\n",
+      "     |  \n",
+      "     |  __floordiv__(self, value, /)\n",
+      "     |      Return self//value.\n",
+      "     |  \n",
+      "     |  __format__(...)\n",
+      "     |      NumPy array scalar formatter\n",
+      "     |  \n",
+      "     |  __int__(self, /)\n",
+      "     |      int(self)\n",
+      "     |  \n",
+      "     |  __invert__(self, /)\n",
+      "     |      ~self\n",
+      "     |  \n",
+      "     |  __lshift__(self, value, /)\n",
+      "     |      Return self<<value.\n",
+      "     |  \n",
+      "     |  __mod__(self, value, /)\n",
+      "     |      Return self%value.\n",
+      "     |  \n",
+      "     |  __mul__(self, value, /)\n",
+      "     |      Return self*value.\n",
+      "     |  \n",
+      "     |  __neg__(self, /)\n",
+      "     |      -self\n",
+      "     |  \n",
+      "     |  __or__(self, value, /)\n",
+      "     |      Return self|value.\n",
+      "     |  \n",
+      "     |  __pos__(self, /)\n",
+      "     |      +self\n",
+      "     |  \n",
+      "     |  __pow__(self, value, mod=None, /)\n",
+      "     |      Return pow(self, value, mod).\n",
+      "     |  \n",
+      "     |  __radd__(self, value, /)\n",
+      "     |      Return value+self.\n",
+      "     |  \n",
+      "     |  __rand__(self, value, /)\n",
+      "     |      Return value&self.\n",
+      "     |  \n",
+      "     |  __rdivmod__(self, value, /)\n",
+      "     |      Return divmod(value, self).\n",
+      "     |  \n",
+      "     |  __reduce__(...)\n",
+      "     |      Helper for pickle.\n",
+      "     |  \n",
+      "     |  __rfloordiv__(self, value, /)\n",
+      "     |      Return value//self.\n",
+      "     |  \n",
+      "     |  __rlshift__(self, value, /)\n",
+      "     |      Return value<<self.\n",
+      "     |  \n",
+      "     |  __rmod__(self, value, /)\n",
+      "     |      Return value%self.\n",
+      "     |  \n",
+      "     |  __rmul__(self, value, /)\n",
+      "     |      Return value*self.\n",
+      "     |  \n",
+      "     |  __ror__(self, value, /)\n",
+      "     |      Return value|self.\n",
+      "     |  \n",
+      "     |  __rpow__(self, value, mod=None, /)\n",
+      "     |      Return pow(value, self, mod).\n",
+      "     |  \n",
+      "     |  __rrshift__(self, value, /)\n",
+      "     |      Return value>>self.\n",
+      "     |  \n",
+      "     |  __rshift__(self, value, /)\n",
+      "     |      Return self>>value.\n",
+      "     |  \n",
+      "     |  __rsub__(self, value, /)\n",
+      "     |      Return value-self.\n",
+      "     |  \n",
+      "     |  __rtruediv__(self, value, /)\n",
+      "     |      Return value/self.\n",
+      "     |  \n",
+      "     |  __rxor__(self, value, /)\n",
+      "     |      Return value^self.\n",
+      "     |  \n",
+      "     |  __setstate__(...)\n",
+      "     |  \n",
+      "     |  __sizeof__(...)\n",
+      "     |      Size of object in memory, in bytes.\n",
+      "     |  \n",
+      "     |  __sub__(self, value, /)\n",
+      "     |      Return self-value.\n",
+      "     |  \n",
+      "     |  __truediv__(self, value, /)\n",
+      "     |      Return self/value.\n",
+      "     |  \n",
+      "     |  __xor__(self, value, /)\n",
+      "     |      Return self^value.\n",
+      "     |  \n",
+      "     |  all(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  any(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmax(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argmin(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  argsort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  astype(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  byteswap(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  choose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  clip(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  compress(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  conj(...)\n",
+      "     |  \n",
+      "     |  conjugate(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  copy(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumprod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  cumsum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  diagonal(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dump(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  dumps(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  fill(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  flatten(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  item(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  itemset(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  max(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  mean(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  min(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  newbyteorder(...)\n",
+      "     |      newbyteorder(new_order='S')\n",
+      "     |      \n",
+      "     |      Return a new `dtype` with a different byte order.\n",
+      "     |      \n",
+      "     |      Changes are also made in all fields and sub-arrays of the data type.\n",
+      "     |      \n",
+      "     |      The `new_order` code can be any from the following:\n",
+      "     |      \n",
+      "     |      * 'S' - swap dtype from current to opposite endian\n",
+      "     |      * {'<', 'L'} - little endian\n",
+      "     |      * {'>', 'B'} - big endian\n",
+      "     |      * {'=', 'N'} - native order\n",
+      "     |      * {'|', 'I'} - ignore (no change to byte order)\n",
+      "     |      \n",
+      "     |      Parameters\n",
+      "     |      ----------\n",
+      "     |      new_order : str, optional\n",
+      "     |          Byte order to force; a value from the byte order specifications\n",
+      "     |          above.  The default value ('S') results in swapping the current\n",
+      "     |          byte order. The code does a case-insensitive check on the first\n",
+      "     |          letter of `new_order` for the alternatives above.  For example,\n",
+      "     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
+      "     |      \n",
+      "     |      \n",
+      "     |      Returns\n",
+      "     |      -------\n",
+      "     |      new_dtype : dtype\n",
+      "     |          New `dtype` object with the given change to the byte order.\n",
+      "     |  \n",
+      "     |  nonzero(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  prod(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ptp(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  put(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ravel(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  repeat(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  reshape(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  resize(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  round(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  searchsorted(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  setflags(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class so as to\n",
+      "     |      provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sort(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  squeeze(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  std(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  sum(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  swapaxes(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  take(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tobytes(...)\n",
+      "     |  \n",
+      "     |  tofile(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tolist(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  tostring(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  trace(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  transpose(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  var(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  view(...)\n",
+      "     |      Not implemented (virtual attribute)\n",
+      "     |      \n",
+      "     |      Class generic exists solely to derive numpy scalars from, and possesses,\n",
+      "     |      albeit unimplemented, all the attributes of the ndarray class\n",
+      "     |      so as to provide a uniform API.\n",
+      "     |      \n",
+      "     |      See also the corresponding attribute of the derived class of interest.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors inherited from generic:\n",
+      "     |  \n",
+      "     |  T\n",
+      "     |      transpose\n",
+      "     |  \n",
+      "     |  __array_interface__\n",
+      "     |      Array protocol: Python side\n",
+      "     |  \n",
+      "     |  __array_priority__\n",
+      "     |      Array priority.\n",
+      "     |  \n",
+      "     |  __array_struct__\n",
+      "     |      Array protocol: struct\n",
+      "     |  \n",
+      "     |  data\n",
+      "     |      pointer to start of data\n",
+      "     |  \n",
+      "     |  flat\n",
+      "     |      a 1-d view of scalar\n",
+      "     |  \n",
+      "     |  imag\n",
+      "     |      imaginary part of scalar\n",
+      "     |  \n",
+      "     |  itemsize\n",
+      "     |      length of one element in bytes\n",
+      "     |  \n",
+      "     |  nbytes\n",
+      "     |      length of item in bytes\n",
+      "     |  \n",
+      "     |  ndim\n",
+      "     |      number of array dimensions\n",
+      "     |  \n",
+      "     |  real\n",
+      "     |      real part of scalar\n",
+      "     |  \n",
+      "     |  shape\n",
+      "     |      tuple of array dimensions\n",
+      "     |  \n",
+      "     |  size\n",
+      "     |      number of elements in the gentype\n",
+      "     |  \n",
+      "     |  strides\n",
+      "     |      tuple of bytes steps in each dimension\n",
+      "\n",
+      "FUNCTIONS\n",
+      "    _add_newdoc_ufunc(...)\n",
+      "        scipy._add_newdoc_ufunc is deprecated and will be removed in SciPy 2.0.0, use numpy._add_newdoc_ufunc instead\n",
+      "    \n",
+      "    absolute(...)\n",
+      "        scipy.absolute is deprecated and will be removed in SciPy 2.0.0, use numpy.absolute instead\n",
+      "    \n",
+      "    add(...)\n",
+      "        scipy.add is deprecated and will be removed in SciPy 2.0.0, use numpy.add instead\n",
+      "    \n",
+      "    add_docstring(...)\n",
+      "        scipy.add_docstring is deprecated and will be removed in SciPy 2.0.0, use numpy.add_docstring instead\n",
+      "    \n",
+      "    add_newdoc(place, obj, doc, warn_on_python=True)\n",
+      "        scipy.add_newdoc is deprecated and will be removed in SciPy 2.0.0, use numpy.add_newdoc instead\n",
+      "    \n",
+      "    add_newdoc_ufunc = _add_newdoc_ufunc(...)\n",
+      "        scipy.add_newdoc_ufunc is deprecated and will be removed in SciPy 2.0.0, use numpy.add_newdoc_ufunc instead\n",
+      "    \n",
+      "    alen(a)\n",
+      "        scipy.alen is deprecated and will be removed in SciPy 2.0.0, use numpy.alen instead\n",
+      "    \n",
+      "    all(a, axis=None, out=None, keepdims=<no value>)\n",
+      "        scipy.all is deprecated and will be removed in SciPy 2.0.0, use numpy.all instead\n",
+      "    \n",
+      "    allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)\n",
+      "        scipy.allclose is deprecated and will be removed in SciPy 2.0.0, use numpy.allclose instead\n",
+      "    \n",
+      "    alltrue(*args, **kwargs)\n",
+      "        scipy.alltrue is deprecated and will be removed in SciPy 2.0.0, use numpy.alltrue instead\n",
+      "    \n",
+      "    amax(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)\n",
+      "        scipy.amax is deprecated and will be removed in SciPy 2.0.0, use numpy.amax instead\n",
+      "    \n",
+      "    amin(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)\n",
+      "        scipy.amin is deprecated and will be removed in SciPy 2.0.0, use numpy.amin instead\n",
+      "    \n",
+      "    angle(z, deg=False)\n",
+      "        scipy.angle is deprecated and will be removed in SciPy 2.0.0, use numpy.angle instead\n",
+      "    \n",
+      "    any(a, axis=None, out=None, keepdims=<no value>)\n",
+      "        scipy.any is deprecated and will be removed in SciPy 2.0.0, use numpy.any instead\n",
+      "    \n",
+      "    append(arr, values, axis=None)\n",
+      "        scipy.append is deprecated and will be removed in SciPy 2.0.0, use numpy.append instead\n",
+      "    \n",
+      "    apply_along_axis(func1d, axis, arr, *args, **kwargs)\n",
+      "        scipy.apply_along_axis is deprecated and will be removed in SciPy 2.0.0, use numpy.apply_along_axis instead\n",
+      "    \n",
+      "    apply_over_axes(func, a, axes)\n",
+      "        scipy.apply_over_axes is deprecated and will be removed in SciPy 2.0.0, use numpy.apply_over_axes instead\n",
+      "    \n",
+      "    arange(...)\n",
+      "        scipy.arange is deprecated and will be removed in SciPy 2.0.0, use numpy.arange instead\n",
+      "    \n",
+      "    arccos(x)\n",
+      "        scipy.arccos is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.arccos instead\n",
+      "    \n",
+      "    arccosh(...)\n",
+      "        scipy.arccosh is deprecated and will be removed in SciPy 2.0.0, use numpy.arccosh instead\n",
+      "    \n",
+      "    arcsin(x)\n",
+      "        scipy.arcsin is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.arcsin instead\n",
+      "    \n",
+      "    arcsinh(...)\n",
+      "        scipy.arcsinh is deprecated and will be removed in SciPy 2.0.0, use numpy.arcsinh instead\n",
+      "    \n",
+      "    arctan(...)\n",
+      "        scipy.arctan is deprecated and will be removed in SciPy 2.0.0, use numpy.arctan instead\n",
+      "    \n",
+      "    arctan2(...)\n",
+      "        scipy.arctan2 is deprecated and will be removed in SciPy 2.0.0, use numpy.arctan2 instead\n",
+      "    \n",
+      "    arctanh(x)\n",
+      "        scipy.arctanh is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.arctanh instead\n",
+      "    \n",
+      "    argmax(a, axis=None, out=None)\n",
+      "        scipy.argmax is deprecated and will be removed in SciPy 2.0.0, use numpy.argmax instead\n",
+      "    \n",
+      "    argmin(a, axis=None, out=None)\n",
+      "        scipy.argmin is deprecated and will be removed in SciPy 2.0.0, use numpy.argmin instead\n",
+      "    \n",
+      "    argpartition(a, kth, axis=-1, kind='introselect', order=None)\n",
+      "        scipy.argpartition is deprecated and will be removed in SciPy 2.0.0, use numpy.argpartition instead\n",
+      "    \n",
+      "    argsort(a, axis=-1, kind=None, order=None)\n",
+      "        scipy.argsort is deprecated and will be removed in SciPy 2.0.0, use numpy.argsort instead\n",
+      "    \n",
+      "    argwhere(a)\n",
+      "        scipy.argwhere is deprecated and will be removed in SciPy 2.0.0, use numpy.argwhere instead\n",
+      "    \n",
+      "    around(a, decimals=0, out=None)\n",
+      "        scipy.around is deprecated and will be removed in SciPy 2.0.0, use numpy.around instead\n",
+      "    \n",
+      "    array(...)\n",
+      "        scipy.array is deprecated and will be removed in SciPy 2.0.0, use numpy.array instead\n",
+      "    \n",
+      "    array2string(a, max_line_width=None, precision=None, suppress_small=None, separator=' ', prefix='', style=<no value>, formatter=None, threshold=None, edgeitems=None, sign=None, floatmode=None, suffix='', *, legacy=None)\n",
+      "        scipy.array2string is deprecated and will be removed in SciPy 2.0.0, use numpy.array2string instead\n",
+      "    \n",
+      "    array_equal(a1, a2, equal_nan=False)\n",
+      "        scipy.array_equal is deprecated and will be removed in SciPy 2.0.0, use numpy.array_equal instead\n",
+      "    \n",
+      "    array_equiv(a1, a2)\n",
+      "        scipy.array_equiv is deprecated and will be removed in SciPy 2.0.0, use numpy.array_equiv instead\n",
+      "    \n",
+      "    array_repr(arr, max_line_width=None, precision=None, suppress_small=None)\n",
+      "        scipy.array_repr is deprecated and will be removed in SciPy 2.0.0, use numpy.array_repr instead\n",
+      "    \n",
+      "    array_split(ary, indices_or_sections, axis=0)\n",
+      "        scipy.array_split is deprecated and will be removed in SciPy 2.0.0, use numpy.array_split instead\n",
+      "    \n",
+      "    array_str(a, max_line_width=None, precision=None, suppress_small=None)\n",
+      "        scipy.array_str is deprecated and will be removed in SciPy 2.0.0, use numpy.array_str instead\n",
+      "    \n",
+      "    asanyarray(a, dtype=None, order=None)\n",
+      "        scipy.asanyarray is deprecated and will be removed in SciPy 2.0.0, use numpy.asanyarray instead\n",
+      "    \n",
+      "    asarray(a, dtype=None, order=None)\n",
+      "        scipy.asarray is deprecated and will be removed in SciPy 2.0.0, use numpy.asarray instead\n",
+      "    \n",
+      "    asarray_chkfinite(a, dtype=None, order=None)\n",
+      "        scipy.asarray_chkfinite is deprecated and will be removed in SciPy 2.0.0, use numpy.asarray_chkfinite instead\n",
+      "    \n",
+      "    ascontiguousarray(a, dtype=None)\n",
+      "        scipy.ascontiguousarray is deprecated and will be removed in SciPy 2.0.0, use numpy.ascontiguousarray instead\n",
+      "    \n",
+      "    asfarray(a, dtype=<class 'numpy.float64'>)\n",
+      "        scipy.asfarray is deprecated and will be removed in SciPy 2.0.0, use numpy.asfarray instead\n",
+      "    \n",
+      "    asfortranarray(a, dtype=None)\n",
+      "        scipy.asfortranarray is deprecated and will be removed in SciPy 2.0.0, use numpy.asfortranarray instead\n",
+      "    \n",
+      "    asmatrix(data, dtype=None)\n",
+      "        scipy.asmatrix is deprecated and will be removed in SciPy 2.0.0, use numpy.asmatrix instead\n",
+      "    \n",
+      "    asscalar(a)\n",
+      "        scipy.asscalar is deprecated and will be removed in SciPy 2.0.0, use numpy.asscalar instead\n",
+      "    \n",
+      "    atleast_1d(*arys)\n",
+      "        scipy.atleast_1d is deprecated and will be removed in SciPy 2.0.0, use numpy.atleast_1d instead\n",
+      "    \n",
+      "    atleast_2d(*arys)\n",
+      "        scipy.atleast_2d is deprecated and will be removed in SciPy 2.0.0, use numpy.atleast_2d instead\n",
+      "    \n",
+      "    atleast_3d(*arys)\n",
+      "        scipy.atleast_3d is deprecated and will be removed in SciPy 2.0.0, use numpy.atleast_3d instead\n",
+      "    \n",
+      "    average(a, axis=None, weights=None, returned=False)\n",
+      "        scipy.average is deprecated and will be removed in SciPy 2.0.0, use numpy.average instead\n",
+      "    \n",
+      "    bartlett(M)\n",
+      "        scipy.bartlett is deprecated and will be removed in SciPy 2.0.0, use numpy.bartlett instead\n",
+      "    \n",
+      "    base_repr(number, base=2, padding=0)\n",
+      "        scipy.base_repr is deprecated and will be removed in SciPy 2.0.0, use numpy.base_repr instead\n",
+      "    \n",
+      "    binary_repr(num, width=None)\n",
+      "        scipy.binary_repr is deprecated and will be removed in SciPy 2.0.0, use numpy.binary_repr instead\n",
+      "    \n",
+      "    bincount(...)\n",
+      "        scipy.bincount is deprecated and will be removed in SciPy 2.0.0, use numpy.bincount instead\n",
+      "    \n",
+      "    bitwise_and(...)\n",
+      "        scipy.bitwise_and is deprecated and will be removed in SciPy 2.0.0, use numpy.bitwise_and instead\n",
+      "    \n",
+      "    bitwise_not = invert(...)\n",
+      "        scipy.bitwise_not is deprecated and will be removed in SciPy 2.0.0, use numpy.bitwise_not instead\n",
+      "    \n",
+      "    bitwise_or(...)\n",
+      "        scipy.bitwise_or is deprecated and will be removed in SciPy 2.0.0, use numpy.bitwise_or instead\n",
+      "    \n",
+      "    bitwise_xor(...)\n",
+      "        scipy.bitwise_xor is deprecated and will be removed in SciPy 2.0.0, use numpy.bitwise_xor instead\n",
+      "    \n",
+      "    blackman(M)\n",
+      "        scipy.blackman is deprecated and will be removed in SciPy 2.0.0, use numpy.blackman instead\n",
+      "    \n",
+      "    block(arrays)\n",
+      "        scipy.block is deprecated and will be removed in SciPy 2.0.0, use numpy.block instead\n",
+      "    \n",
+      "    bmat(obj, ldict=None, gdict=None)\n",
+      "        scipy.bmat is deprecated and will be removed in SciPy 2.0.0, use numpy.bmat instead\n",
+      "    \n",
+      "    broadcast_arrays(*args, subok=False)\n",
+      "        scipy.broadcast_arrays is deprecated and will be removed in SciPy 2.0.0, use numpy.broadcast_arrays instead\n",
+      "    \n",
+      "    broadcast_to(array, shape, subok=False)\n",
+      "        scipy.broadcast_to is deprecated and will be removed in SciPy 2.0.0, use numpy.broadcast_to instead\n",
+      "    \n",
+      "    busday_count(...)\n",
+      "        scipy.busday_count is deprecated and will be removed in SciPy 2.0.0, use numpy.busday_count instead\n",
+      "    \n",
+      "    busday_offset(...)\n",
+      "        scipy.busday_offset is deprecated and will be removed in SciPy 2.0.0, use numpy.busday_offset instead\n",
+      "    \n",
+      "    byte_bounds(a)\n",
+      "        scipy.byte_bounds is deprecated and will be removed in SciPy 2.0.0, use numpy.byte_bounds instead\n",
+      "    \n",
+      "    can_cast(...)\n",
+      "        scipy.can_cast is deprecated and will be removed in SciPy 2.0.0, use numpy.can_cast instead\n",
+      "    \n",
+      "    cbrt(...)\n",
+      "        scipy.cbrt is deprecated and will be removed in SciPy 2.0.0, use numpy.cbrt instead\n",
+      "    \n",
+      "    ceil(...)\n",
+      "        scipy.ceil is deprecated and will be removed in SciPy 2.0.0, use numpy.ceil instead\n",
+      "    \n",
+      "    choose(a, choices, out=None, mode='raise')\n",
+      "        scipy.choose is deprecated and will be removed in SciPy 2.0.0, use numpy.choose instead\n",
+      "    \n",
+      "    clip(a, a_min, a_max, out=None, **kwargs)\n",
+      "        scipy.clip is deprecated and will be removed in SciPy 2.0.0, use numpy.clip instead\n",
+      "    \n",
+      "    column_stack(tup)\n",
+      "        scipy.column_stack is deprecated and will be removed in SciPy 2.0.0, use numpy.column_stack instead\n",
+      "    \n",
+      "    common_type(*arrays)\n",
+      "        scipy.common_type is deprecated and will be removed in SciPy 2.0.0, use numpy.common_type instead\n",
+      "    \n",
+      "    compare_chararrays(...)\n",
+      "        scipy.compare_chararrays is deprecated and will be removed in SciPy 2.0.0, use numpy.compare_chararrays instead\n",
+      "    \n",
+      "    compress(condition, a, axis=None, out=None)\n",
+      "        scipy.compress is deprecated and will be removed in SciPy 2.0.0, use numpy.compress instead\n",
+      "    \n",
+      "    concatenate(...)\n",
+      "        scipy.concatenate is deprecated and will be removed in SciPy 2.0.0, use numpy.concatenate instead\n",
+      "    \n",
+      "    conj = conjugate(...)\n",
+      "        scipy.conj is deprecated and will be removed in SciPy 2.0.0, use numpy.conj instead\n",
+      "    \n",
+      "    conjugate(...)\n",
+      "        scipy.conjugate is deprecated and will be removed in SciPy 2.0.0, use numpy.conjugate instead\n",
+      "    \n",
+      "    convolve(a, v, mode='full')\n",
+      "        scipy.convolve is deprecated and will be removed in SciPy 2.0.0, use numpy.convolve instead\n",
+      "    \n",
+      "    copy(a, order='K', subok=False)\n",
+      "        scipy.copy is deprecated and will be removed in SciPy 2.0.0, use numpy.copy instead\n",
+      "    \n",
+      "    copysign(...)\n",
+      "        scipy.copysign is deprecated and will be removed in SciPy 2.0.0, use numpy.copysign instead\n",
+      "    \n",
+      "    copyto(...)\n",
+      "        scipy.copyto is deprecated and will be removed in SciPy 2.0.0, use numpy.copyto instead\n",
+      "    \n",
+      "    corrcoef(x, y=None, rowvar=True, bias=<no value>, ddof=<no value>)\n",
+      "        scipy.corrcoef is deprecated and will be removed in SciPy 2.0.0, use numpy.corrcoef instead\n",
+      "    \n",
+      "    correlate(a, v, mode='valid')\n",
+      "        scipy.correlate is deprecated and will be removed in SciPy 2.0.0, use numpy.correlate instead\n",
+      "    \n",
+      "    cos(...)\n",
+      "        scipy.cos is deprecated and will be removed in SciPy 2.0.0, use numpy.cos instead\n",
+      "    \n",
+      "    cosh(...)\n",
+      "        scipy.cosh is deprecated and will be removed in SciPy 2.0.0, use numpy.cosh instead\n",
+      "    \n",
+      "    count_nonzero(a, axis=None, *, keepdims=False)\n",
+      "        scipy.count_nonzero is deprecated and will be removed in SciPy 2.0.0, use numpy.count_nonzero instead\n",
+      "    \n",
+      "    cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)\n",
+      "        scipy.cov is deprecated and will be removed in SciPy 2.0.0, use numpy.cov instead\n",
+      "    \n",
+      "    cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None)\n",
+      "        scipy.cross is deprecated and will be removed in SciPy 2.0.0, use numpy.cross instead\n",
+      "    \n",
+      "    cumprod(a, axis=None, dtype=None, out=None)\n",
+      "        scipy.cumprod is deprecated and will be removed in SciPy 2.0.0, use numpy.cumprod instead\n",
+      "    \n",
+      "    cumproduct(*args, **kwargs)\n",
+      "        scipy.cumproduct is deprecated and will be removed in SciPy 2.0.0, use numpy.cumproduct instead\n",
+      "    \n",
+      "    cumsum(a, axis=None, dtype=None, out=None)\n",
+      "        scipy.cumsum is deprecated and will be removed in SciPy 2.0.0, use numpy.cumsum instead\n",
+      "    \n",
+      "    datetime_as_string(...)\n",
+      "        scipy.datetime_as_string is deprecated and will be removed in SciPy 2.0.0, use numpy.datetime_as_string instead\n",
+      "    \n",
+      "    datetime_data(...)\n",
+      "        scipy.datetime_data is deprecated and will be removed in SciPy 2.0.0, use numpy.datetime_data instead\n",
+      "    \n",
+      "    deg2rad(...)\n",
+      "        scipy.deg2rad is deprecated and will be removed in SciPy 2.0.0, use numpy.deg2rad instead\n",
+      "    \n",
+      "    degrees(...)\n",
+      "        scipy.degrees is deprecated and will be removed in SciPy 2.0.0, use numpy.degrees instead\n",
+      "    \n",
+      "    delete(arr, obj, axis=None)\n",
+      "        scipy.delete is deprecated and will be removed in SciPy 2.0.0, use numpy.delete instead\n",
+      "    \n",
+      "    deprecate(*args, **kwargs)\n",
+      "        scipy.deprecate is deprecated and will be removed in SciPy 2.0.0, use numpy.deprecate instead\n",
+      "    \n",
+      "    deprecate_with_doc lambda msg\n",
+      "        scipy.deprecate_with_doc is deprecated and will be removed in SciPy 2.0.0, use numpy.deprecate_with_doc instead\n",
+      "    \n",
+      "    diag(v, k=0)\n",
+      "        scipy.diag is deprecated and will be removed in SciPy 2.0.0, use numpy.diag instead\n",
+      "    \n",
+      "    diag_indices(n, ndim=2)\n",
+      "        scipy.diag_indices is deprecated and will be removed in SciPy 2.0.0, use numpy.diag_indices instead\n",
+      "    \n",
+      "    diag_indices_from(arr)\n",
+      "        scipy.diag_indices_from is deprecated and will be removed in SciPy 2.0.0, use numpy.diag_indices_from instead\n",
+      "    \n",
+      "    diagflat(v, k=0)\n",
+      "        scipy.diagflat is deprecated and will be removed in SciPy 2.0.0, use numpy.diagflat instead\n",
+      "    \n",
+      "    diagonal(a, offset=0, axis1=0, axis2=1)\n",
+      "        scipy.diagonal is deprecated and will be removed in SciPy 2.0.0, use numpy.diagonal instead\n",
+      "    \n",
+      "    diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)\n",
+      "        scipy.diff is deprecated and will be removed in SciPy 2.0.0, use numpy.diff instead\n",
+      "    \n",
+      "    digitize(x, bins, right=False)\n",
+      "        scipy.digitize is deprecated and will be removed in SciPy 2.0.0, use numpy.digitize instead\n",
+      "    \n",
+      "    disp(mesg, device=None, linefeed=True)\n",
+      "        scipy.disp is deprecated and will be removed in SciPy 2.0.0, use numpy.disp instead\n",
+      "    \n",
+      "    divide = true_divide(...)\n",
+      "        scipy.divide is deprecated and will be removed in SciPy 2.0.0, use numpy.divide instead\n",
+      "    \n",
+      "    divmod(...)\n",
+      "        scipy.divmod is deprecated and will be removed in SciPy 2.0.0, use numpy.divmod instead\n",
+      "    \n",
+      "    dot(...)\n",
+      "        scipy.dot is deprecated and will be removed in SciPy 2.0.0, use numpy.dot instead\n",
+      "    \n",
+      "    dsplit(ary, indices_or_sections)\n",
+      "        scipy.dsplit is deprecated and will be removed in SciPy 2.0.0, use numpy.dsplit instead\n",
+      "    \n",
+      "    dstack(tup)\n",
+      "        scipy.dstack is deprecated and will be removed in SciPy 2.0.0, use numpy.dstack instead\n",
+      "    \n",
+      "    ediff1d(ary, to_end=None, to_begin=None)\n",
+      "        scipy.ediff1d is deprecated and will be removed in SciPy 2.0.0, use numpy.ediff1d instead\n",
+      "    \n",
+      "    einsum(*operands, out=None, optimize=False, **kwargs)\n",
+      "        scipy.einsum is deprecated and will be removed in SciPy 2.0.0, use numpy.einsum instead\n",
+      "    \n",
+      "    einsum_path(*operands, optimize='greedy', einsum_call=False)\n",
+      "        scipy.einsum_path is deprecated and will be removed in SciPy 2.0.0, use numpy.einsum_path instead\n",
+      "    \n",
+      "    empty(...)\n",
+      "        scipy.empty is deprecated and will be removed in SciPy 2.0.0, use numpy.empty instead\n",
+      "    \n",
+      "    empty_like(...)\n",
+      "        scipy.empty_like is deprecated and will be removed in SciPy 2.0.0, use numpy.empty_like instead\n",
+      "    \n",
+      "    equal(...)\n",
+      "        scipy.equal is deprecated and will be removed in SciPy 2.0.0, use numpy.equal instead\n",
+      "    \n",
+      "    exp(...)\n",
+      "        scipy.exp is deprecated and will be removed in SciPy 2.0.0, use numpy.exp instead\n",
+      "    \n",
+      "    exp2(...)\n",
+      "        scipy.exp2 is deprecated and will be removed in SciPy 2.0.0, use numpy.exp2 instead\n",
+      "    \n",
+      "    expand_dims(a, axis)\n",
+      "        scipy.expand_dims is deprecated and will be removed in SciPy 2.0.0, use numpy.expand_dims instead\n",
+      "    \n",
+      "    expm1(...)\n",
+      "        scipy.expm1 is deprecated and will be removed in SciPy 2.0.0, use numpy.expm1 instead\n",
+      "    \n",
+      "    extract(condition, arr)\n",
+      "        scipy.extract is deprecated and will be removed in SciPy 2.0.0, use numpy.extract instead\n",
+      "    \n",
+      "    eye(N, M=None, k=0, dtype=<class 'float'>, order='C')\n",
+      "        scipy.eye is deprecated and will be removed in SciPy 2.0.0, use numpy.eye instead\n",
+      "    \n",
+      "    fabs(...)\n",
+      "        scipy.fabs is deprecated and will be removed in SciPy 2.0.0, use numpy.fabs instead\n",
+      "    \n",
+      "    fastCopyAndTranspose = _fastCopyAndTranspose(...)\n",
+      "        scipy.fastCopyAndTranspose is deprecated and will be removed in SciPy 2.0.0, use numpy.fastCopyAndTranspose instead\n",
+      "    \n",
+      "    fill_diagonal(a, val, wrap=False)\n",
+      "        scipy.fill_diagonal is deprecated and will be removed in SciPy 2.0.0, use numpy.fill_diagonal instead\n",
+      "    \n",
+      "    find_common_type(array_types, scalar_types)\n",
+      "        scipy.find_common_type is deprecated and will be removed in SciPy 2.0.0, use numpy.find_common_type instead\n",
+      "    \n",
+      "    fix(x, out=None)\n",
+      "        scipy.fix is deprecated and will be removed in SciPy 2.0.0, use numpy.fix instead\n",
+      "    \n",
+      "    flatnonzero(a)\n",
+      "        scipy.flatnonzero is deprecated and will be removed in SciPy 2.0.0, use numpy.flatnonzero instead\n",
+      "    \n",
+      "    flip(m, axis=None)\n",
+      "        scipy.flip is deprecated and will be removed in SciPy 2.0.0, use numpy.flip instead\n",
+      "    \n",
+      "    fliplr(m)\n",
+      "        scipy.fliplr is deprecated and will be removed in SciPy 2.0.0, use numpy.fliplr instead\n",
+      "    \n",
+      "    flipud(m)\n",
+      "        scipy.flipud is deprecated and will be removed in SciPy 2.0.0, use numpy.flipud instead\n",
+      "    \n",
+      "    float_power(...)\n",
+      "        scipy.float_power is deprecated and will be removed in SciPy 2.0.0, use numpy.float_power instead\n",
+      "    \n",
+      "    floor(...)\n",
+      "        scipy.floor is deprecated and will be removed in SciPy 2.0.0, use numpy.floor instead\n",
+      "    \n",
+      "    floor_divide(...)\n",
+      "        scipy.floor_divide is deprecated and will be removed in SciPy 2.0.0, use numpy.floor_divide instead\n",
+      "    \n",
+      "    fmax(...)\n",
+      "        scipy.fmax is deprecated and will be removed in SciPy 2.0.0, use numpy.fmax instead\n",
+      "    \n",
+      "    fmin(...)\n",
+      "        scipy.fmin is deprecated and will be removed in SciPy 2.0.0, use numpy.fmin instead\n",
+      "    \n",
+      "    fmod(...)\n",
+      "        scipy.fmod is deprecated and will be removed in SciPy 2.0.0, use numpy.fmod instead\n",
+      "    \n",
+      "    format_float_positional(x, precision=None, unique=True, fractional=True, trim='k', sign=False, pad_left=None, pad_right=None)\n",
+      "        scipy.format_float_positional is deprecated and will be removed in SciPy 2.0.0, use numpy.format_float_positional instead\n",
+      "    \n",
+      "    format_float_scientific(x, precision=None, unique=True, trim='k', sign=False, pad_left=None, exp_digits=None)\n",
+      "        scipy.format_float_scientific is deprecated and will be removed in SciPy 2.0.0, use numpy.format_float_scientific instead\n",
+      "    \n",
+      "    frexp(...)\n",
+      "        scipy.frexp is deprecated and will be removed in SciPy 2.0.0, use numpy.frexp instead\n",
+      "    \n",
+      "    frombuffer(...)\n",
+      "        scipy.frombuffer is deprecated and will be removed in SciPy 2.0.0, use numpy.frombuffer instead\n",
+      "    \n",
+      "    fromfile(...)\n",
+      "        scipy.fromfile is deprecated and will be removed in SciPy 2.0.0, use numpy.fromfile instead\n",
+      "    \n",
+      "    fromfunction(function, shape, *, dtype=<class 'float'>, **kwargs)\n",
+      "        scipy.fromfunction is deprecated and will be removed in SciPy 2.0.0, use numpy.fromfunction instead\n",
+      "    \n",
+      "    fromiter(...)\n",
+      "        scipy.fromiter is deprecated and will be removed in SciPy 2.0.0, use numpy.fromiter instead\n",
+      "    \n",
+      "    frompyfunc(...)\n",
+      "        scipy.frompyfunc is deprecated and will be removed in SciPy 2.0.0, use numpy.frompyfunc instead\n",
+      "    \n",
+      "    fromregex(file, regexp, dtype, encoding=None)\n",
+      "        scipy.fromregex is deprecated and will be removed in SciPy 2.0.0, use numpy.fromregex instead\n",
+      "    \n",
+      "    fromstring(...)\n",
+      "        scipy.fromstring is deprecated and will be removed in SciPy 2.0.0, use numpy.fromstring instead\n",
+      "    \n",
+      "    full(shape, fill_value, dtype=None, order='C')\n",
+      "        scipy.full is deprecated and will be removed in SciPy 2.0.0, use numpy.full instead\n",
+      "    \n",
+      "    full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None)\n",
+      "        scipy.full_like is deprecated and will be removed in SciPy 2.0.0, use numpy.full_like instead\n",
+      "    \n",
+      "    fv(rate, nper, pmt, pv, when='end')\n",
+      "        scipy.fv is deprecated and will be removed in SciPy 2.0.0, use numpy.fv instead\n",
+      "    \n",
+      "    gcd(...)\n",
+      "        scipy.gcd is deprecated and will be removed in SciPy 2.0.0, use numpy.gcd instead\n",
+      "    \n",
+      "    genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=\" !#$%&'()*+,-./:;<=>?@[\\\\]^{|}~\", replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding='bytes')\n",
+      "        scipy.genfromtxt is deprecated and will be removed in SciPy 2.0.0, use numpy.genfromtxt instead\n",
+      "    \n",
+      "    geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0)\n",
+      "        scipy.geomspace is deprecated and will be removed in SciPy 2.0.0, use numpy.geomspace instead\n",
+      "    \n",
+      "    get_array_wrap(*args)\n",
+      "        scipy.get_array_wrap is deprecated and will be removed in SciPy 2.0.0, use numpy.get_array_wrap instead\n",
+      "    \n",
+      "    get_include()\n",
+      "        scipy.get_include is deprecated and will be removed in SciPy 2.0.0, use numpy.get_include instead\n",
+      "    \n",
+      "    get_printoptions()\n",
+      "        scipy.get_printoptions is deprecated and will be removed in SciPy 2.0.0, use numpy.get_printoptions instead\n",
+      "    \n",
+      "    getbufsize()\n",
+      "        scipy.getbufsize is deprecated and will be removed in SciPy 2.0.0, use numpy.getbufsize instead\n",
+      "    \n",
+      "    geterr()\n",
+      "        scipy.geterr is deprecated and will be removed in SciPy 2.0.0, use numpy.geterr instead\n",
+      "    \n",
+      "    geterrcall()\n",
+      "        scipy.geterrcall is deprecated and will be removed in SciPy 2.0.0, use numpy.geterrcall instead\n",
+      "    \n",
+      "    geterrobj(...)\n",
+      "        scipy.geterrobj is deprecated and will be removed in SciPy 2.0.0, use numpy.geterrobj instead\n",
+      "    \n",
+      "    gradient(f, *varargs, axis=None, edge_order=1)\n",
+      "        scipy.gradient is deprecated and will be removed in SciPy 2.0.0, use numpy.gradient instead\n",
+      "    \n",
+      "    greater(...)\n",
+      "        scipy.greater is deprecated and will be removed in SciPy 2.0.0, use numpy.greater instead\n",
+      "    \n",
+      "    greater_equal(...)\n",
+      "        scipy.greater_equal is deprecated and will be removed in SciPy 2.0.0, use numpy.greater_equal instead\n",
+      "    \n",
+      "    hamming(M)\n",
+      "        scipy.hamming is deprecated and will be removed in SciPy 2.0.0, use numpy.hamming instead\n",
+      "    \n",
+      "    hanning(M)\n",
+      "        scipy.hanning is deprecated and will be removed in SciPy 2.0.0, use numpy.hanning instead\n",
+      "    \n",
+      "    heaviside(...)\n",
+      "        scipy.heaviside is deprecated and will be removed in SciPy 2.0.0, use numpy.heaviside instead\n",
+      "    \n",
+      "    histogram(a, bins=10, range=None, normed=None, weights=None, density=None)\n",
+      "        scipy.histogram is deprecated and will be removed in SciPy 2.0.0, use numpy.histogram instead\n",
+      "    \n",
+      "    histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None)\n",
+      "        scipy.histogram2d is deprecated and will be removed in SciPy 2.0.0, use numpy.histogram2d instead\n",
+      "    \n",
+      "    histogram_bin_edges(a, bins=10, range=None, weights=None)\n",
+      "        scipy.histogram_bin_edges is deprecated and will be removed in SciPy 2.0.0, use numpy.histogram_bin_edges instead\n",
+      "    \n",
+      "    histogramdd(sample, bins=10, range=None, normed=None, weights=None, density=None)\n",
+      "        scipy.histogramdd is deprecated and will be removed in SciPy 2.0.0, use numpy.histogramdd instead\n",
+      "    \n",
+      "    hsplit(ary, indices_or_sections)\n",
+      "        scipy.hsplit is deprecated and will be removed in SciPy 2.0.0, use numpy.hsplit instead\n",
+      "    \n",
+      "    hstack(tup)\n",
+      "        scipy.hstack is deprecated and will be removed in SciPy 2.0.0, use numpy.hstack instead\n",
+      "    \n",
+      "    hypot(...)\n",
+      "        scipy.hypot is deprecated and will be removed in SciPy 2.0.0, use numpy.hypot instead\n",
+      "    \n",
+      "    i0(x)\n",
+      "        scipy.i0 is deprecated and will be removed in SciPy 2.0.0, use numpy.i0 instead\n",
+      "    \n",
+      "    identity(n, dtype=None)\n",
+      "        scipy.identity is deprecated and will be removed in SciPy 2.0.0, use numpy.identity instead\n",
+      "    \n",
+      "    ifft(a, n=None, axis=-1, norm=None)\n",
+      "        scipy.ifft is deprecated and will be removed in SciPy 2.0.0, use scipy.fft.ifft instead\n",
+      "    \n",
+      "    imag(val)\n",
+      "        scipy.imag is deprecated and will be removed in SciPy 2.0.0, use numpy.imag instead\n",
+      "    \n",
+      "    in1d(ar1, ar2, assume_unique=False, invert=False)\n",
+      "        scipy.in1d is deprecated and will be removed in SciPy 2.0.0, use numpy.in1d instead\n",
+      "    \n",
+      "    indices(dimensions, dtype=<class 'int'>, sparse=False)\n",
+      "        scipy.indices is deprecated and will be removed in SciPy 2.0.0, use numpy.indices instead\n",
+      "    \n",
+      "    info(object=None, maxwidth=76, output=<ipykernel.iostream.OutStream object at 0x0000018C0DD84F88>, toplevel='numpy')\n",
+      "        scipy.info is deprecated and will be removed in SciPy 2.0.0, use numpy.info instead\n",
+      "    \n",
+      "    inner(...)\n",
+      "        scipy.inner is deprecated and will be removed in SciPy 2.0.0, use numpy.inner instead\n",
+      "    \n",
+      "    insert(arr, obj, values, axis=None)\n",
+      "        scipy.insert is deprecated and will be removed in SciPy 2.0.0, use numpy.insert instead\n",
+      "    \n",
+      "    interp(x, xp, fp, left=None, right=None, period=None)\n",
+      "        scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
+      "    \n",
+      "    intersect1d(ar1, ar2, assume_unique=False, return_indices=False)\n",
+      "        scipy.intersect1d is deprecated and will be removed in SciPy 2.0.0, use numpy.intersect1d instead\n",
+      "    \n",
+      "    invert(...)\n",
+      "        scipy.invert is deprecated and will be removed in SciPy 2.0.0, use numpy.invert instead\n",
+      "    \n",
+      "    ipmt(rate, per, nper, pv, fv=0, when='end')\n",
+      "        scipy.ipmt is deprecated and will be removed in SciPy 2.0.0, use numpy.ipmt instead\n",
+      "    \n",
+      "    irr(values)\n",
+      "        scipy.irr is deprecated and will be removed in SciPy 2.0.0, use numpy.irr instead\n",
+      "    \n",
+      "    is_busday(...)\n",
+      "        scipy.is_busday is deprecated and will be removed in SciPy 2.0.0, use numpy.is_busday instead\n",
+      "    \n",
+      "    isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)\n",
+      "        scipy.isclose is deprecated and will be removed in SciPy 2.0.0, use numpy.isclose instead\n",
+      "    \n",
+      "    iscomplex(x)\n",
+      "        scipy.iscomplex is deprecated and will be removed in SciPy 2.0.0, use numpy.iscomplex instead\n",
+      "    \n",
+      "    iscomplexobj(x)\n",
+      "        scipy.iscomplexobj is deprecated and will be removed in SciPy 2.0.0, use numpy.iscomplexobj instead\n",
+      "    \n",
+      "    isfinite(...)\n",
+      "        scipy.isfinite is deprecated and will be removed in SciPy 2.0.0, use numpy.isfinite instead\n",
+      "    \n",
+      "    isfortran(a)\n",
+      "        scipy.isfortran is deprecated and will be removed in SciPy 2.0.0, use numpy.isfortran instead\n",
+      "    \n",
+      "    isin(element, test_elements, assume_unique=False, invert=False)\n",
+      "        scipy.isin is deprecated and will be removed in SciPy 2.0.0, use numpy.isin instead\n",
+      "    \n",
+      "    isinf(...)\n",
+      "        scipy.isinf is deprecated and will be removed in SciPy 2.0.0, use numpy.isinf instead\n",
+      "    \n",
+      "    isnan(...)\n",
+      "        scipy.isnan is deprecated and will be removed in SciPy 2.0.0, use numpy.isnan instead\n",
+      "    \n",
+      "    isnat(...)\n",
+      "        scipy.isnat is deprecated and will be removed in SciPy 2.0.0, use numpy.isnat instead\n",
+      "    \n",
+      "    isneginf(x, out=None)\n",
+      "        scipy.isneginf is deprecated and will be removed in SciPy 2.0.0, use numpy.isneginf instead\n",
+      "    \n",
+      "    isposinf(x, out=None)\n",
+      "        scipy.isposinf is deprecated and will be removed in SciPy 2.0.0, use numpy.isposinf instead\n",
+      "    \n",
+      "    isreal(x)\n",
+      "        scipy.isreal is deprecated and will be removed in SciPy 2.0.0, use numpy.isreal instead\n",
+      "    \n",
+      "    isrealobj(x)\n",
+      "        scipy.isrealobj is deprecated and will be removed in SciPy 2.0.0, use numpy.isrealobj instead\n",
+      "    \n",
+      "    isscalar(element)\n",
+      "        scipy.isscalar is deprecated and will be removed in SciPy 2.0.0, use numpy.isscalar instead\n",
+      "    \n",
+      "    issctype(rep)\n",
+      "        scipy.issctype is deprecated and will be removed in SciPy 2.0.0, use numpy.issctype instead\n",
+      "    \n",
+      "    issubclass_(arg1, arg2)\n",
+      "        scipy.issubclass_ is deprecated and will be removed in SciPy 2.0.0, use numpy.issubclass_ instead\n",
+      "    \n",
+      "    issubdtype(arg1, arg2)\n",
+      "        scipy.issubdtype is deprecated and will be removed in SciPy 2.0.0, use numpy.issubdtype instead\n",
+      "    \n",
+      "    issubsctype(arg1, arg2)\n",
+      "        scipy.issubsctype is deprecated and will be removed in SciPy 2.0.0, use numpy.issubsctype instead\n",
+      "    \n",
+      "    iterable(y)\n",
+      "        scipy.iterable is deprecated and will be removed in SciPy 2.0.0, use numpy.iterable instead\n",
+      "    \n",
+      "    ix_(*args)\n",
+      "        scipy.ix_ is deprecated and will be removed in SciPy 2.0.0, use numpy.ix_ instead\n",
+      "    \n",
+      "    kaiser(M, beta)\n",
+      "        scipy.kaiser is deprecated and will be removed in SciPy 2.0.0, use numpy.kaiser instead\n",
+      "    \n",
+      "    kron(a, b)\n",
+      "        scipy.kron is deprecated and will be removed in SciPy 2.0.0, use numpy.kron instead\n",
+      "    \n",
+      "    lcm(...)\n",
+      "        scipy.lcm is deprecated and will be removed in SciPy 2.0.0, use numpy.lcm instead\n",
+      "    \n",
+      "    ldexp(...)\n",
+      "        scipy.ldexp is deprecated and will be removed in SciPy 2.0.0, use numpy.ldexp instead\n",
+      "    \n",
+      "    left_shift(...)\n",
+      "        scipy.left_shift is deprecated and will be removed in SciPy 2.0.0, use numpy.left_shift instead\n",
+      "    \n",
+      "    less(...)\n",
+      "        scipy.less is deprecated and will be removed in SciPy 2.0.0, use numpy.less instead\n",
+      "    \n",
+      "    less_equal(...)\n",
+      "        scipy.less_equal is deprecated and will be removed in SciPy 2.0.0, use numpy.less_equal instead\n",
+      "    \n",
+      "    lexsort(...)\n",
+      "        scipy.lexsort is deprecated and will be removed in SciPy 2.0.0, use numpy.lexsort instead\n",
+      "    \n",
+      "    linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)\n",
+      "        scipy.linspace is deprecated and will be removed in SciPy 2.0.0, use numpy.linspace instead\n",
+      "    \n",
+      "    load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding='ASCII')\n",
+      "        scipy.load is deprecated and will be removed in SciPy 2.0.0, use numpy.load instead\n",
+      "    \n",
+      "    loads(*args, **kwargs)\n",
+      "        scipy.loads is deprecated and will be removed in SciPy 2.0.0, use numpy.loads instead\n",
+      "    \n",
+      "    loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes', max_rows=None)\n",
+      "        scipy.loadtxt is deprecated and will be removed in SciPy 2.0.0, use numpy.loadtxt instead\n",
+      "    \n",
+      "    log(x)\n",
+      "        scipy.log is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.log instead\n",
+      "    \n",
+      "    log10(x)\n",
+      "        scipy.log10 is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.log10 instead\n",
+      "    \n",
+      "    log1p(...)\n",
+      "        scipy.log1p is deprecated and will be removed in SciPy 2.0.0, use numpy.log1p instead\n",
+      "    \n",
+      "    log2(x)\n",
+      "        scipy.log2 is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.log2 instead\n",
+      "    \n",
+      "    logaddexp(...)\n",
+      "        scipy.logaddexp is deprecated and will be removed in SciPy 2.0.0, use numpy.logaddexp instead\n",
+      "    \n",
+      "    logaddexp2(...)\n",
+      "        scipy.logaddexp2 is deprecated and will be removed in SciPy 2.0.0, use numpy.logaddexp2 instead\n",
+      "    \n",
+      "    logical_and(...)\n",
+      "        scipy.logical_and is deprecated and will be removed in SciPy 2.0.0, use numpy.logical_and instead\n",
+      "    \n",
+      "    logical_not(...)\n",
+      "        scipy.logical_not is deprecated and will be removed in SciPy 2.0.0, use numpy.logical_not instead\n",
+      "    \n",
+      "    logical_or(...)\n",
+      "        scipy.logical_or is deprecated and will be removed in SciPy 2.0.0, use numpy.logical_or instead\n",
+      "    \n",
+      "    logical_xor(...)\n",
+      "        scipy.logical_xor is deprecated and will be removed in SciPy 2.0.0, use numpy.logical_xor instead\n",
+      "    \n",
+      "    logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)\n",
+      "        scipy.logspace is deprecated and will be removed in SciPy 2.0.0, use numpy.logspace instead\n",
+      "    \n",
+      "    lookfor(what, module=None, import_modules=True, regenerate=False, output=None)\n",
+      "        scipy.lookfor is deprecated and will be removed in SciPy 2.0.0, use numpy.lookfor instead\n",
+      "    \n",
+      "    mafromtxt(fname, **kwargs)\n",
+      "        scipy.mafromtxt is deprecated and will be removed in SciPy 2.0.0, use numpy.mafromtxt instead\n",
+      "    \n",
+      "    mask_indices(n, mask_func, k=0)\n",
+      "        scipy.mask_indices is deprecated and will be removed in SciPy 2.0.0, use numpy.mask_indices instead\n",
+      "    \n",
+      "    mat = asmatrix(data, dtype=None)\n",
+      "        scipy.mat is deprecated and will be removed in SciPy 2.0.0, use numpy.mat instead\n",
+      "    \n",
+      "    matmul(...)\n",
+      "        scipy.matmul is deprecated and will be removed in SciPy 2.0.0, use numpy.matmul instead\n",
+      "    \n",
+      "    maximum(...)\n",
+      "        scipy.maximum is deprecated and will be removed in SciPy 2.0.0, use numpy.maximum instead\n",
+      "    \n",
+      "    maximum_sctype(t)\n",
+      "        scipy.maximum_sctype is deprecated and will be removed in SciPy 2.0.0, use numpy.maximum_sctype instead\n",
+      "    \n",
+      "    may_share_memory(...)\n",
+      "        scipy.may_share_memory is deprecated and will be removed in SciPy 2.0.0, use numpy.may_share_memory instead\n",
+      "    \n",
+      "    mean(a, axis=None, dtype=None, out=None, keepdims=<no value>)\n",
+      "        scipy.mean is deprecated and will be removed in SciPy 2.0.0, use numpy.mean instead\n",
+      "    \n",
+      "    median(a, axis=None, out=None, overwrite_input=False, keepdims=False)\n",
+      "        scipy.median is deprecated and will be removed in SciPy 2.0.0, use numpy.median instead\n",
+      "    \n",
+      "    meshgrid(*xi, copy=True, sparse=False, indexing='xy')\n",
+      "        scipy.meshgrid is deprecated and will be removed in SciPy 2.0.0, use numpy.meshgrid instead\n",
+      "    \n",
+      "    min_scalar_type(...)\n",
+      "        scipy.min_scalar_type is deprecated and will be removed in SciPy 2.0.0, use numpy.min_scalar_type instead\n",
+      "    \n",
+      "    minimum(...)\n",
+      "        scipy.minimum is deprecated and will be removed in SciPy 2.0.0, use numpy.minimum instead\n",
+      "    \n",
+      "    mintypecode(typechars, typeset='GDFgdf', default='d')\n",
+      "        scipy.mintypecode is deprecated and will be removed in SciPy 2.0.0, use numpy.mintypecode instead\n",
+      "    \n",
+      "    mirr(values, finance_rate, reinvest_rate)\n",
+      "        scipy.mirr is deprecated and will be removed in SciPy 2.0.0, use numpy.mirr instead\n",
+      "    \n",
+      "    mod = remainder(...)\n",
+      "        scipy.mod is deprecated and will be removed in SciPy 2.0.0, use numpy.mod instead\n",
+      "    \n",
+      "    modf(...)\n",
+      "        scipy.modf is deprecated and will be removed in SciPy 2.0.0, use numpy.modf instead\n",
+      "    \n",
+      "    moveaxis(a, source, destination)\n",
+      "        scipy.moveaxis is deprecated and will be removed in SciPy 2.0.0, use numpy.moveaxis instead\n",
+      "    \n",
+      "    msort(a)\n",
+      "        scipy.msort is deprecated and will be removed in SciPy 2.0.0, use numpy.msort instead\n",
+      "    \n",
+      "    multiply(...)\n",
+      "        scipy.multiply is deprecated and will be removed in SciPy 2.0.0, use numpy.multiply instead\n",
+      "    \n",
+      "    nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)\n",
+      "        scipy.nan_to_num is deprecated and will be removed in SciPy 2.0.0, use numpy.nan_to_num instead\n",
+      "    \n",
+      "    nanargmax(a, axis=None)\n",
+      "        scipy.nanargmax is deprecated and will be removed in SciPy 2.0.0, use numpy.nanargmax instead\n",
+      "    \n",
+      "    nanargmin(a, axis=None)\n",
+      "        scipy.nanargmin is deprecated and will be removed in SciPy 2.0.0, use numpy.nanargmin instead\n",
+      "    \n",
+      "    nancumprod(a, axis=None, dtype=None, out=None)\n",
+      "        scipy.nancumprod is deprecated and will be removed in SciPy 2.0.0, use numpy.nancumprod instead\n",
+      "    \n",
+      "    nancumsum(a, axis=None, dtype=None, out=None)\n",
+      "        scipy.nancumsum is deprecated and will be removed in SciPy 2.0.0, use numpy.nancumsum instead\n",
+      "    \n",
+      "    nanmax(a, axis=None, out=None, keepdims=<no value>)\n",
+      "        scipy.nanmax is deprecated and will be removed in SciPy 2.0.0, use numpy.nanmax instead\n",
+      "    \n",
+      "    nanmean(a, axis=None, dtype=None, out=None, keepdims=<no value>)\n",
+      "        scipy.nanmean is deprecated and will be removed in SciPy 2.0.0, use numpy.nanmean instead\n",
+      "    \n",
+      "    nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=<no value>)\n",
+      "        scipy.nanmedian is deprecated and will be removed in SciPy 2.0.0, use numpy.nanmedian instead\n",
+      "    \n",
+      "    nanmin(a, axis=None, out=None, keepdims=<no value>)\n",
+      "        scipy.nanmin is deprecated and will be removed in SciPy 2.0.0, use numpy.nanmin instead\n",
+      "    \n",
+      "    nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=<no value>)\n",
+      "        scipy.nanpercentile is deprecated and will be removed in SciPy 2.0.0, use numpy.nanpercentile instead\n",
+      "    \n",
+      "    nanprod(a, axis=None, dtype=None, out=None, keepdims=<no value>)\n",
+      "        scipy.nanprod is deprecated and will be removed in SciPy 2.0.0, use numpy.nanprod instead\n",
+      "    \n",
+      "    nanquantile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=<no value>)\n",
+      "        scipy.nanquantile is deprecated and will be removed in SciPy 2.0.0, use numpy.nanquantile instead\n",
+      "    \n",
+      "    nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)\n",
+      "        scipy.nanstd is deprecated and will be removed in SciPy 2.0.0, use numpy.nanstd instead\n",
+      "    \n",
+      "    nansum(a, axis=None, dtype=None, out=None, keepdims=<no value>)\n",
+      "        scipy.nansum is deprecated and will be removed in SciPy 2.0.0, use numpy.nansum instead\n",
+      "    \n",
+      "    nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)\n",
+      "        scipy.nanvar is deprecated and will be removed in SciPy 2.0.0, use numpy.nanvar instead\n",
+      "    \n",
+      "    ndfromtxt(fname, **kwargs)\n",
+      "        scipy.ndfromtxt is deprecated and will be removed in SciPy 2.0.0, use numpy.ndfromtxt instead\n",
+      "    \n",
+      "    ndim(a)\n",
+      "        scipy.ndim is deprecated and will be removed in SciPy 2.0.0, use numpy.ndim instead\n",
+      "    \n",
+      "    negative(...)\n",
+      "        scipy.negative is deprecated and will be removed in SciPy 2.0.0, use numpy.negative instead\n",
+      "    \n",
+      "    nested_iters(...)\n",
+      "        scipy.nested_iters is deprecated and will be removed in SciPy 2.0.0, use numpy.nested_iters instead\n",
+      "    \n",
+      "    nextafter(...)\n",
+      "        scipy.nextafter is deprecated and will be removed in SciPy 2.0.0, use numpy.nextafter instead\n",
+      "    \n",
+      "    nonzero(a)\n",
+      "        scipy.nonzero is deprecated and will be removed in SciPy 2.0.0, use numpy.nonzero instead\n",
+      "    \n",
+      "    not_equal(...)\n",
+      "        scipy.not_equal is deprecated and will be removed in SciPy 2.0.0, use numpy.not_equal instead\n",
+      "    \n",
+      "    nper(rate, pmt, pv, fv=0, when='end')\n",
+      "        scipy.nper is deprecated and will be removed in SciPy 2.0.0, use numpy.nper instead\n",
+      "    \n",
+      "    npv(rate, values)\n",
+      "        scipy.npv is deprecated and will be removed in SciPy 2.0.0, use numpy.npv instead\n",
+      "    \n",
+      "    obj2sctype(rep, default=None)\n",
+      "        scipy.obj2sctype is deprecated and will be removed in SciPy 2.0.0, use numpy.obj2sctype instead\n",
+      "    \n",
+      "    ones(shape, dtype=None, order='C')\n",
+      "        scipy.ones is deprecated and will be removed in SciPy 2.0.0, use numpy.ones instead\n",
+      "    \n",
+      "    ones_like(a, dtype=None, order='K', subok=True, shape=None)\n",
+      "        scipy.ones_like is deprecated and will be removed in SciPy 2.0.0, use numpy.ones_like instead\n",
+      "    \n",
+      "    outer(a, b, out=None)\n",
+      "        scipy.outer is deprecated and will be removed in SciPy 2.0.0, use numpy.outer instead\n",
+      "    \n",
+      "    packbits(...)\n",
+      "        scipy.packbits is deprecated and will be removed in SciPy 2.0.0, use numpy.packbits instead\n",
+      "    \n",
+      "    pad(array, pad_width, mode='constant', **kwargs)\n",
+      "        scipy.pad is deprecated and will be removed in SciPy 2.0.0, use numpy.pad instead\n",
+      "    \n",
+      "    partition(a, kth, axis=-1, kind='introselect', order=None)\n",
+      "        scipy.partition is deprecated and will be removed in SciPy 2.0.0, use numpy.partition instead\n",
+      "    \n",
+      "    percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
+      "        scipy.percentile is deprecated and will be removed in SciPy 2.0.0, use numpy.percentile instead\n",
+      "    \n",
+      "    piecewise(x, condlist, funclist, *args, **kw)\n",
+      "        scipy.piecewise is deprecated and will be removed in SciPy 2.0.0, use numpy.piecewise instead\n",
+      "    \n",
+      "    place(arr, mask, vals)\n",
+      "        scipy.place is deprecated and will be removed in SciPy 2.0.0, use numpy.place instead\n",
+      "    \n",
+      "    pmt(rate, nper, pv, fv=0, when='end')\n",
+      "        scipy.pmt is deprecated and will be removed in SciPy 2.0.0, use numpy.pmt instead\n",
+      "    \n",
+      "    poly(seq_of_zeros)\n",
+      "        scipy.poly is deprecated and will be removed in SciPy 2.0.0, use numpy.poly instead\n",
+      "    \n",
+      "    polyadd(a1, a2)\n",
+      "        scipy.polyadd is deprecated and will be removed in SciPy 2.0.0, use numpy.polyadd instead\n",
+      "    \n",
+      "    polyder(p, m=1)\n",
+      "        scipy.polyder is deprecated and will be removed in SciPy 2.0.0, use numpy.polyder instead\n",
+      "    \n",
+      "    polydiv(u, v)\n",
+      "        scipy.polydiv is deprecated and will be removed in SciPy 2.0.0, use numpy.polydiv instead\n",
+      "    \n",
+      "    polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)\n",
+      "        scipy.polyfit is deprecated and will be removed in SciPy 2.0.0, use numpy.polyfit instead\n",
+      "    \n",
+      "    polyint(p, m=1, k=None)\n",
+      "        scipy.polyint is deprecated and will be removed in SciPy 2.0.0, use numpy.polyint instead\n",
+      "    \n",
+      "    polymul(a1, a2)\n",
+      "        scipy.polymul is deprecated and will be removed in SciPy 2.0.0, use numpy.polymul instead\n",
+      "    \n",
+      "    polysub(a1, a2)\n",
+      "        scipy.polysub is deprecated and will be removed in SciPy 2.0.0, use numpy.polysub instead\n",
+      "    \n",
+      "    polyval(p, x)\n",
+      "        scipy.polyval is deprecated and will be removed in SciPy 2.0.0, use numpy.polyval instead\n",
+      "    \n",
+      "    positive(...)\n",
+      "        scipy.positive is deprecated and will be removed in SciPy 2.0.0, use numpy.positive instead\n",
+      "    \n",
+      "    power(x, p)\n",
+      "        scipy.power is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.power instead\n",
+      "    \n",
+      "    ppmt(rate, per, nper, pv, fv=0, when='end')\n",
+      "        scipy.ppmt is deprecated and will be removed in SciPy 2.0.0, use numpy.ppmt instead\n",
+      "    \n",
+      "    printoptions(*args, **kwargs)\n",
+      "        scipy.printoptions is deprecated and will be removed in SciPy 2.0.0, use numpy.printoptions instead\n",
+      "    \n",
+      "    prod(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)\n",
+      "        scipy.prod is deprecated and will be removed in SciPy 2.0.0, use numpy.prod instead\n",
+      "    \n",
+      "    product(*args, **kwargs)\n",
+      "        scipy.product is deprecated and will be removed in SciPy 2.0.0, use numpy.product instead\n",
+      "    \n",
+      "    promote_types(...)\n",
+      "        scipy.promote_types is deprecated and will be removed in SciPy 2.0.0, use numpy.promote_types instead\n",
+      "    \n",
+      "    ptp(a, axis=None, out=None, keepdims=<no value>)\n",
+      "        scipy.ptp is deprecated and will be removed in SciPy 2.0.0, use numpy.ptp instead\n",
+      "    \n",
+      "    put(a, ind, v, mode='raise')\n",
+      "        scipy.put is deprecated and will be removed in SciPy 2.0.0, use numpy.put instead\n",
+      "    \n",
+      "    put_along_axis(arr, indices, values, axis)\n",
+      "        scipy.put_along_axis is deprecated and will be removed in SciPy 2.0.0, use numpy.put_along_axis instead\n",
+      "    \n",
+      "    putmask(...)\n",
+      "        scipy.putmask is deprecated and will be removed in SciPy 2.0.0, use numpy.putmask instead\n",
+      "    \n",
+      "    pv(rate, nper, pmt, fv=0, when='end')\n",
+      "        scipy.pv is deprecated and will be removed in SciPy 2.0.0, use numpy.pv instead\n",
+      "    \n",
+      "    quantile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
+      "        scipy.quantile is deprecated and will be removed in SciPy 2.0.0, use numpy.quantile instead\n",
+      "    \n",
+      "    rad2deg(...)\n",
+      "        scipy.rad2deg is deprecated and will be removed in SciPy 2.0.0, use numpy.rad2deg instead\n",
+      "    \n",
+      "    radians(...)\n",
+      "        scipy.radians is deprecated and will be removed in SciPy 2.0.0, use numpy.radians instead\n",
+      "    \n",
+      "    rand(...)\n",
+      "        scipy.rand is deprecated and will be removed in SciPy 2.0.0, use numpy.random.rand instead\n",
+      "    \n",
+      "    randn(...)\n",
+      "        scipy.randn is deprecated and will be removed in SciPy 2.0.0, use numpy.random.randn instead\n",
+      "    \n",
+      "    rate(nper, pmt, pv, fv, when='end', guess=None, tol=None, maxiter=100)\n",
+      "        scipy.rate is deprecated and will be removed in SciPy 2.0.0, use numpy.rate instead\n",
+      "    \n",
+      "    ravel(a, order='C')\n",
+      "        scipy.ravel is deprecated and will be removed in SciPy 2.0.0, use numpy.ravel instead\n",
+      "    \n",
+      "    ravel_multi_index(...)\n",
+      "        scipy.ravel_multi_index is deprecated and will be removed in SciPy 2.0.0, use numpy.ravel_multi_index instead\n",
+      "    \n",
+      "    real(val)\n",
+      "        scipy.real is deprecated and will be removed in SciPy 2.0.0, use numpy.real instead\n",
+      "    \n",
+      "    real_if_close(a, tol=100)\n",
+      "        scipy.real_if_close is deprecated and will be removed in SciPy 2.0.0, use numpy.real_if_close instead\n",
+      "    \n",
+      "    recfromcsv(fname, **kwargs)\n",
+      "        scipy.recfromcsv is deprecated and will be removed in SciPy 2.0.0, use numpy.recfromcsv instead\n",
+      "    \n",
+      "    recfromtxt(fname, **kwargs)\n",
+      "        scipy.recfromtxt is deprecated and will be removed in SciPy 2.0.0, use numpy.recfromtxt instead\n",
+      "    \n",
+      "    reciprocal(...)\n",
+      "        scipy.reciprocal is deprecated and will be removed in SciPy 2.0.0, use numpy.reciprocal instead\n",
+      "    \n",
+      "    remainder(...)\n",
+      "        scipy.remainder is deprecated and will be removed in SciPy 2.0.0, use numpy.remainder instead\n",
+      "    \n",
+      "    repeat(a, repeats, axis=None)\n",
+      "        scipy.repeat is deprecated and will be removed in SciPy 2.0.0, use numpy.repeat instead\n",
+      "    \n",
+      "    require(a, dtype=None, requirements=None)\n",
+      "        scipy.require is deprecated and will be removed in SciPy 2.0.0, use numpy.require instead\n",
+      "    \n",
+      "    reshape(a, newshape, order='C')\n",
+      "        scipy.reshape is deprecated and will be removed in SciPy 2.0.0, use numpy.reshape instead\n",
+      "    \n",
+      "    resize(a, new_shape)\n",
+      "        scipy.resize is deprecated and will be removed in SciPy 2.0.0, use numpy.resize instead\n",
+      "    \n",
+      "    result_type(...)\n",
+      "        scipy.result_type is deprecated and will be removed in SciPy 2.0.0, use numpy.result_type instead\n",
+      "    \n",
+      "    right_shift(...)\n",
+      "        scipy.right_shift is deprecated and will be removed in SciPy 2.0.0, use numpy.right_shift instead\n",
+      "    \n",
+      "    rint(...)\n",
+      "        scipy.rint is deprecated and will be removed in SciPy 2.0.0, use numpy.rint instead\n",
+      "    \n",
+      "    roll(a, shift, axis=None)\n",
+      "        scipy.roll is deprecated and will be removed in SciPy 2.0.0, use numpy.roll instead\n",
+      "    \n",
+      "    rollaxis(a, axis, start=0)\n",
+      "        scipy.rollaxis is deprecated and will be removed in SciPy 2.0.0, use numpy.rollaxis instead\n",
+      "    \n",
+      "    roots(p)\n",
+      "        scipy.roots is deprecated and will be removed in SciPy 2.0.0, use numpy.roots instead\n",
+      "    \n",
+      "    rot90(m, k=1, axes=(0, 1))\n",
+      "        scipy.rot90 is deprecated and will be removed in SciPy 2.0.0, use numpy.rot90 instead\n",
+      "    \n",
+      "    round_(a, decimals=0, out=None)\n",
+      "        scipy.round_ is deprecated and will be removed in SciPy 2.0.0, use numpy.round_ instead\n",
+      "    \n",
+      "    row_stack = vstack(tup)\n",
+      "        scipy.row_stack is deprecated and will be removed in SciPy 2.0.0, use numpy.row_stack instead\n",
+      "    \n",
+      "    safe_eval(source)\n",
+      "        scipy.safe_eval is deprecated and will be removed in SciPy 2.0.0, use numpy.safe_eval instead\n",
+      "    \n",
+      "    save(file, arr, allow_pickle=True, fix_imports=True)\n",
+      "        scipy.save is deprecated and will be removed in SciPy 2.0.0, use numpy.save instead\n",
+      "    \n",
+      "    savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\n', header='', footer='', comments='# ', encoding=None)\n",
+      "        scipy.savetxt is deprecated and will be removed in SciPy 2.0.0, use numpy.savetxt instead\n",
+      "    \n",
+      "    savez(file, *args, **kwds)\n",
+      "        scipy.savez is deprecated and will be removed in SciPy 2.0.0, use numpy.savez instead\n",
+      "    \n",
+      "    savez_compressed(file, *args, **kwds)\n",
+      "        scipy.savez_compressed is deprecated and will be removed in SciPy 2.0.0, use numpy.savez_compressed instead\n",
+      "    \n",
+      "    sctype2char(sctype)\n",
+      "        scipy.sctype2char is deprecated and will be removed in SciPy 2.0.0, use numpy.sctype2char instead\n",
+      "    \n",
+      "    searchsorted(a, v, side='left', sorter=None)\n",
+      "        scipy.searchsorted is deprecated and will be removed in SciPy 2.0.0, use numpy.searchsorted instead\n",
+      "    \n",
+      "    select(condlist, choicelist, default=0)\n",
+      "        scipy.select is deprecated and will be removed in SciPy 2.0.0, use numpy.select instead\n",
+      "    \n",
+      "    set_numeric_ops(...)\n",
+      "        scipy.set_numeric_ops is deprecated and will be removed in SciPy 2.0.0, use numpy.set_numeric_ops instead\n",
+      "    \n",
+      "    set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None, sign=None, floatmode=None, *, legacy=None)\n",
+      "        scipy.set_printoptions is deprecated and will be removed in SciPy 2.0.0, use numpy.set_printoptions instead\n",
+      "    \n",
+      "    set_string_function(f, repr=True)\n",
+      "        scipy.set_string_function is deprecated and will be removed in SciPy 2.0.0, use numpy.set_string_function instead\n",
+      "    \n",
+      "    setbufsize(size)\n",
+      "        scipy.setbufsize is deprecated and will be removed in SciPy 2.0.0, use numpy.setbufsize instead\n",
+      "    \n",
+      "    setdiff1d(ar1, ar2, assume_unique=False)\n",
+      "        scipy.setdiff1d is deprecated and will be removed in SciPy 2.0.0, use numpy.setdiff1d instead\n",
+      "    \n",
+      "    seterr(all=None, divide=None, over=None, under=None, invalid=None)\n",
+      "        scipy.seterr is deprecated and will be removed in SciPy 2.0.0, use numpy.seterr instead\n",
+      "    \n",
+      "    seterrcall(func)\n",
+      "        scipy.seterrcall is deprecated and will be removed in SciPy 2.0.0, use numpy.seterrcall instead\n",
+      "    \n",
+      "    seterrobj(...)\n",
+      "        scipy.seterrobj is deprecated and will be removed in SciPy 2.0.0, use numpy.seterrobj instead\n",
+      "    \n",
+      "    setxor1d(ar1, ar2, assume_unique=False)\n",
+      "        scipy.setxor1d is deprecated and will be removed in SciPy 2.0.0, use numpy.setxor1d instead\n",
+      "    \n",
+      "    shape(a)\n",
+      "        scipy.shape is deprecated and will be removed in SciPy 2.0.0, use numpy.shape instead\n",
+      "    \n",
+      "    shares_memory(...)\n",
+      "        scipy.shares_memory is deprecated and will be removed in SciPy 2.0.0, use numpy.shares_memory instead\n",
+      "    \n",
+      "    show_config = show()\n",
+      "    \n",
+      "    sign(...)\n",
+      "        scipy.sign is deprecated and will be removed in SciPy 2.0.0, use numpy.sign instead\n",
+      "    \n",
+      "    signbit(...)\n",
+      "        scipy.signbit is deprecated and will be removed in SciPy 2.0.0, use numpy.signbit instead\n",
+      "    \n",
+      "    sin(...)\n",
+      "        scipy.sin is deprecated and will be removed in SciPy 2.0.0, use numpy.sin instead\n",
+      "    \n",
+      "    sinc(x)\n",
+      "        scipy.sinc is deprecated and will be removed in SciPy 2.0.0, use numpy.sinc instead\n",
+      "    \n",
+      "    sinh(...)\n",
+      "        scipy.sinh is deprecated and will be removed in SciPy 2.0.0, use numpy.sinh instead\n",
+      "    \n",
+      "    size(a, axis=None)\n",
+      "        scipy.size is deprecated and will be removed in SciPy 2.0.0, use numpy.size instead\n",
+      "    \n",
+      "    sometrue(*args, **kwargs)\n",
+      "        scipy.sometrue is deprecated and will be removed in SciPy 2.0.0, use numpy.sometrue instead\n",
+      "    \n",
+      "    sort(a, axis=-1, kind=None, order=None)\n",
+      "        scipy.sort is deprecated and will be removed in SciPy 2.0.0, use numpy.sort instead\n",
+      "    \n",
+      "    sort_complex(a)\n",
+      "        scipy.sort_complex is deprecated and will be removed in SciPy 2.0.0, use numpy.sort_complex instead\n",
+      "    \n",
+      "    source(object, output=<ipykernel.iostream.OutStream object at 0x0000018C0DD84F88>)\n",
+      "        scipy.source is deprecated and will be removed in SciPy 2.0.0, use numpy.source instead\n",
+      "    \n",
+      "    spacing(...)\n",
+      "        scipy.spacing is deprecated and will be removed in SciPy 2.0.0, use numpy.spacing instead\n",
+      "    \n",
+      "    split(ary, indices_or_sections, axis=0)\n",
+      "        scipy.split is deprecated and will be removed in SciPy 2.0.0, use numpy.split instead\n",
+      "    \n",
+      "    sqrt(x)\n",
+      "        scipy.sqrt is deprecated and will be removed in SciPy 2.0.0, use numpy.lib.scimath.sqrt instead\n",
+      "    \n",
+      "    square(...)\n",
+      "        scipy.square is deprecated and will be removed in SciPy 2.0.0, use numpy.square instead\n",
+      "    \n",
+      "    squeeze(a, axis=None)\n",
+      "        scipy.squeeze is deprecated and will be removed in SciPy 2.0.0, use numpy.squeeze instead\n",
+      "    \n",
+      "    stack(arrays, axis=0, out=None)\n",
+      "        scipy.stack is deprecated and will be removed in SciPy 2.0.0, use numpy.stack instead\n",
+      "    \n",
+      "    std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)\n",
+      "        scipy.std is deprecated and will be removed in SciPy 2.0.0, use numpy.std instead\n",
+      "    \n",
+      "    subtract(...)\n",
+      "        scipy.subtract is deprecated and will be removed in SciPy 2.0.0, use numpy.subtract instead\n",
+      "    \n",
+      "    sum(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)\n",
+      "        scipy.sum is deprecated and will be removed in SciPy 2.0.0, use numpy.sum instead\n",
+      "    \n",
+      "    swapaxes(a, axis1, axis2)\n",
+      "        scipy.swapaxes is deprecated and will be removed in SciPy 2.0.0, use numpy.swapaxes instead\n",
+      "    \n",
+      "    take(a, indices, axis=None, out=None, mode='raise')\n",
+      "        scipy.take is deprecated and will be removed in SciPy 2.0.0, use numpy.take instead\n",
+      "    \n",
+      "    take_along_axis(arr, indices, axis)\n",
+      "        scipy.take_along_axis is deprecated and will be removed in SciPy 2.0.0, use numpy.take_along_axis instead\n",
+      "    \n",
+      "    tan(...)\n",
+      "        scipy.tan is deprecated and will be removed in SciPy 2.0.0, use numpy.tan instead\n",
+      "    \n",
+      "    tanh(...)\n",
+      "        scipy.tanh is deprecated and will be removed in SciPy 2.0.0, use numpy.tanh instead\n",
+      "    \n",
+      "    tensordot(a, b, axes=2)\n",
+      "        scipy.tensordot is deprecated and will be removed in SciPy 2.0.0, use numpy.tensordot instead\n",
+      "    \n",
+      "    tile(A, reps)\n",
+      "        scipy.tile is deprecated and will be removed in SciPy 2.0.0, use numpy.tile instead\n",
+      "    \n",
+      "    trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)\n",
+      "        scipy.trace is deprecated and will be removed in SciPy 2.0.0, use numpy.trace instead\n",
+      "    \n",
+      "    transpose(a, axes=None)\n",
+      "        scipy.transpose is deprecated and will be removed in SciPy 2.0.0, use numpy.transpose instead\n",
+      "    \n",
+      "    trapz(y, x=None, dx=1.0, axis=-1)\n",
+      "        scipy.trapz is deprecated and will be removed in SciPy 2.0.0, use numpy.trapz instead\n",
+      "    \n",
+      "    tri(N, M=None, k=0, dtype=<class 'float'>)\n",
+      "        scipy.tri is deprecated and will be removed in SciPy 2.0.0, use numpy.tri instead\n",
+      "    \n",
+      "    tril(m, k=0)\n",
+      "        scipy.tril is deprecated and will be removed in SciPy 2.0.0, use numpy.tril instead\n",
+      "    \n",
+      "    tril_indices(n, k=0, m=None)\n",
+      "        scipy.tril_indices is deprecated and will be removed in SciPy 2.0.0, use numpy.tril_indices instead\n",
+      "    \n",
+      "    tril_indices_from(arr, k=0)\n",
+      "        scipy.tril_indices_from is deprecated and will be removed in SciPy 2.0.0, use numpy.tril_indices_from instead\n",
+      "    \n",
+      "    trim_zeros(filt, trim='fb')\n",
+      "        scipy.trim_zeros is deprecated and will be removed in SciPy 2.0.0, use numpy.trim_zeros instead\n",
+      "    \n",
+      "    triu(m, k=0)\n",
+      "        scipy.triu is deprecated and will be removed in SciPy 2.0.0, use numpy.triu instead\n",
+      "    \n",
+      "    triu_indices(n, k=0, m=None)\n",
+      "        scipy.triu_indices is deprecated and will be removed in SciPy 2.0.0, use numpy.triu_indices instead\n",
+      "    \n",
+      "    triu_indices_from(arr, k=0)\n",
+      "        scipy.triu_indices_from is deprecated and will be removed in SciPy 2.0.0, use numpy.triu_indices_from instead\n",
+      "    \n",
+      "    true_divide(...)\n",
+      "        scipy.true_divide is deprecated and will be removed in SciPy 2.0.0, use numpy.true_divide instead\n",
+      "    \n",
+      "    trunc(...)\n",
+      "        scipy.trunc is deprecated and will be removed in SciPy 2.0.0, use numpy.trunc instead\n",
+      "    \n",
+      "    typename(char)\n",
+      "        scipy.typename is deprecated and will be removed in SciPy 2.0.0, use numpy.typename instead\n",
+      "    \n",
+      "    union1d(ar1, ar2)\n",
+      "        scipy.union1d is deprecated and will be removed in SciPy 2.0.0, use numpy.union1d instead\n",
+      "    \n",
+      "    unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)\n",
+      "        scipy.unique is deprecated and will be removed in SciPy 2.0.0, use numpy.unique instead\n",
+      "    \n",
+      "    unpackbits(...)\n",
+      "        scipy.unpackbits is deprecated and will be removed in SciPy 2.0.0, use numpy.unpackbits instead\n",
+      "    \n",
+      "    unravel_index(...)\n",
+      "        scipy.unravel_index is deprecated and will be removed in SciPy 2.0.0, use numpy.unravel_index instead\n",
+      "    \n",
+      "    unwrap(p, discont=3.141592653589793, axis=-1)\n",
+      "        scipy.unwrap is deprecated and will be removed in SciPy 2.0.0, use numpy.unwrap instead\n",
+      "    \n",
+      "    vander(x, N=None, increasing=False)\n",
+      "        scipy.vander is deprecated and will be removed in SciPy 2.0.0, use numpy.vander instead\n",
+      "    \n",
+      "    var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)\n",
+      "        scipy.var is deprecated and will be removed in SciPy 2.0.0, use numpy.var instead\n",
+      "    \n",
+      "    vdot(...)\n",
+      "        scipy.vdot is deprecated and will be removed in SciPy 2.0.0, use numpy.vdot instead\n",
+      "    \n",
+      "    vsplit(ary, indices_or_sections)\n",
+      "        scipy.vsplit is deprecated and will be removed in SciPy 2.0.0, use numpy.vsplit instead\n",
+      "    \n",
+      "    vstack(tup)\n",
+      "        scipy.vstack is deprecated and will be removed in SciPy 2.0.0, use numpy.vstack instead\n",
+      "    \n",
+      "    where(...)\n",
+      "        scipy.where is deprecated and will be removed in SciPy 2.0.0, use numpy.where instead\n",
+      "    \n",
+      "    who(vardict=None)\n",
+      "        scipy.who is deprecated and will be removed in SciPy 2.0.0, use numpy.who instead\n",
+      "    \n",
+      "    zeros(...)\n",
+      "        scipy.zeros is deprecated and will be removed in SciPy 2.0.0, use numpy.zeros instead\n",
+      "    \n",
+      "    zeros_like(a, dtype=None, order='K', subok=True, shape=None)\n",
+      "        scipy.zeros_like is deprecated and will be removed in SciPy 2.0.0, use numpy.zeros_like instead\n",
+      "\n",
+      "DATA\n",
+      "    ALLOW_THREADS = 1\n",
+      "    BUFSIZE = 8192\n",
+      "    CLIP = 0\n",
+      "    ERR_CALL = 3\n",
+      "    ERR_DEFAULT = 521\n",
+      "    ERR_IGNORE = 0\n",
+      "    ERR_LOG = 5\n",
+      "    ERR_PRINT = 4\n",
+      "    ERR_RAISE = 2\n",
+      "    ERR_WARN = 1\n",
+      "    FLOATING_POINT_SUPPORT = 1\n",
+      "    FPE_DIVIDEBYZERO = 1\n",
+      "    FPE_INVALID = 8\n",
+      "    FPE_OVERFLOW = 2\n",
+      "    FPE_UNDERFLOW = 4\n",
+      "    False_ = False\n",
+      "    Inf = inf\n",
+      "    Infinity = inf\n",
+      "    MAXDIMS = 32\n",
+      "    MAY_SHARE_BOUNDS = 0\n",
+      "    MAY_SHARE_EXACT = -1\n",
+      "    NAN = nan\n",
+      "    NINF = -inf\n",
+      "    NZERO = -0.0\n",
+      "    NaN = nan\n",
+      "    PINF = inf\n",
+      "    PZERO = 0.0\n",
+      "    RAISE = 2\n",
+      "    SHIFT_DIVIDEBYZERO = 0\n",
+      "    SHIFT_INVALID = 9\n",
+      "    SHIFT_OVERFLOW = 3\n",
+      "    SHIFT_UNDERFLOW = 6\n",
+      "    ScalarType = (<class 'int'>, <class 'float'>, <class 'complex'>, <clas...\n",
+      "    True_ = True\n",
+      "    UFUNC_BUFSIZE_DEFAULT = 8192\n",
+      "    UFUNC_PYVALS_NAME = 'UFUNC_PYVALS'\n",
+      "    WRAP = 1\n",
+      "    _UFUNC_API = <capsule object NULL>\n",
+      "    __SCIPY_SETUP__ = False\n",
+      "    __all__ = ['test', 'ModuleDeprecationWarning', 'VisibleDeprecationWarn...\n",
+      "    __numpy_version__ = '1.19.1'\n",
+      "    c_ = <numpy.lib.index_tricks.CClass object>\n",
+      "    cast = {<class 'numpy.uint32'>: <function <lambda> at 0...t16'>: <func...\n",
+      "    e = 2.718281828459045\n",
+      "    euler_gamma = 0.5772156649015329\n",
+      "    index_exp = <numpy.lib.index_tricks.IndexExpression object>\n",
+      "    inf = inf\n",
+      "    infty = inf\n",
+      "    little_endian = True\n",
+      "    mgrid = <numpy.lib.index_tricks.MGridClass object>\n",
+      "    nan = nan\n",
+      "    nbytes = {<class 'numpy.bool_'>: 1, <class 'numpy.int8'>:....datetime6...\n",
+      "    newaxis = None\n",
+      "    ogrid = <numpy.lib.index_tricks.OGridClass object>\n",
+      "    pi = 3.141592653589793\n",
+      "    r_ = <numpy.lib.index_tricks.RClass object>\n",
+      "    s_ = <numpy.lib.index_tricks.IndexExpression object>\n",
+      "    sctypeDict = {'?': <class 'numpy.bool_'>, 0: <class 'numpy.bool_'>, 'b...\n",
+      "    sctypeNA = {'Bool': <class 'numpy.bool_'>, <class 'numpy.bo...'>, <cla...\n",
+      "    sctypes = {'complex': [<class 'numpy.complex64'>, <class 'numpy.comple...\n",
+      "    test = <scipy._lib._testutils.PytestTester object>\n",
+      "    tracemalloc_domain = 389047\n",
+      "    typeDict = {'?': <class 'numpy.bool_'>, 0: <class 'numpy.bool_'>, 'byt...\n",
+      "    typeNA = {'Bool': <class 'numpy.bool_'>, <class 'numpy.bo...'>, <class...\n",
+      "    typecodes = {'All': '?bhilqpBHILQPefdgFDGSUVOMm', 'AllFloat': 'efdgFDG...\n",
+      "\n",
+      "VERSION\n",
+      "    1.5.2\n",
+      "\n",
+      "FILE\n",
+      "    c:\\users\\nikhil\\anaconda3\\lib\\site-packages\\scipy\\__init__.py\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "help> quit\n",
+      "\n",
+      "You are now leaving help and returning to the Python interpreter.\n",
+      "If you want to ask for help on a particular object directly from the\n",
+      "interpreter, you can type \"help(object)\".  Executing \"help('string')\"\n",
+      "has the same effect as typing a particular string at the help> prompt.\n"
+     ]
+    }
+   ],
+   "source": [
+    "help()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=========================================\n",
+      "Clustering package (:mod:`scipy.cluster`)\n",
+      "=========================================\n",
+      "\n",
+      ".. currentmodule:: scipy.cluster\n",
+      "\n",
+      ":mod:`scipy.cluster.vq`\n",
+      "\n",
+      "Clustering algorithms are useful in information theory, target detection,\n",
+      "communications, compression, and other areas. The `vq` module only\n",
+      "supports vector quantization and the k-means algorithms.\n",
+      "\n",
+      ":mod:`scipy.cluster.hierarchy`\n",
+      "\n",
+      "The `hierarchy` module provides functions for hierarchical and\n",
+      "agglomerative clustering.  Its features include generating hierarchical\n",
+      "clusters from distance matrices,\n",
+      "calculating statistics on clusters, cutting linkages\n",
+      "to generate flat clusters, and visualizing clusters with dendrograms.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Nikhil\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: scipy.info is deprecated and will be removed in SciPy 2.0.0, use numpy.info instead\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy\n",
+    "scipy.info(cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In file: C:\\Users\\Nikhil\\anaconda3\\lib\\site-packages\\scipy\\cluster\\__init__.py\n",
+      "\n",
+      "\"\"\"\n",
+      "=========================================\n",
+      "Clustering package (:mod:`scipy.cluster`)\n",
+      "=========================================\n",
+      "\n",
+      ".. currentmodule:: scipy.cluster\n",
+      "\n",
+      ":mod:`scipy.cluster.vq`\n",
+      "\n",
+      "Clustering algorithms are useful in information theory, target detection,\n",
+      "communications, compression, and other areas. The `vq` module only\n",
+      "supports vector quantization and the k-means algorithms.\n",
+      "\n",
+      ":mod:`scipy.cluster.hierarchy`\n",
+      "\n",
+      "The `hierarchy` module provides functions for hierarchical and\n",
+      "agglomerative clustering.  Its features include generating hierarchical\n",
+      "clusters from distance matrices,\n",
+      "calculating statistics on clusters, cutting linkages\n",
+      "to generate flat clusters, and visualizing clusters with dendrograms.\n",
+      "\n",
+      "\"\"\"\n",
+      "__all__ = ['vq', 'hierarchy']\n",
+      "\n",
+      "from . import vq, hierarchy\n",
+      "\n",
+      "from scipy._lib._testutils import PytestTester\n",
+      "test = PytestTester(__name__)\n",
+      "del PytestTester\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Nikhil\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: scipy.source is deprecated and will be removed in SciPy 2.0.0, use numpy.source instead\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "scipy.source(cluster)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Special Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Exponential Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import special\n",
+    "\n",
+    "a = special.exp10(2)  # 10 pow(2)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "64.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = special.exp2(6)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Trignometric Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = special.sindg(90)\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7071067811865476\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = special.sindg(45)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = special.cosdg(90)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = special.cosdg(0)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = special.tandg(45)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inf\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = special.tandg(90)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Integration Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. General Intergration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function quad in module scipy.integrate.quadpack:\n",
+      "\n",
+      "quad(func, a, b, args=(), full_output=0, epsabs=1.49e-08, epsrel=1.49e-08, limit=50, points=None, weight=None, wvar=None, wopts=None, maxp1=50, limlst=50)\n",
+      "    Compute a definite integral.\n",
+      "    \n",
+      "    Integrate func from `a` to `b` (possibly infinite interval) using a\n",
+      "    technique from the Fortran library QUADPACK.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    func : {function, scipy.LowLevelCallable}\n",
+      "        A Python function or method to integrate. If `func` takes many\n",
+      "        arguments, it is integrated along the axis corresponding to the\n",
+      "        first argument.\n",
+      "    \n",
+      "        If the user desires improved integration performance, then `f` may\n",
+      "        be a `scipy.LowLevelCallable` with one of the signatures::\n",
+      "    \n",
+      "            double func(double x)\n",
+      "            double func(double x, void *user_data)\n",
+      "            double func(int n, double *xx)\n",
+      "            double func(int n, double *xx, void *user_data)\n",
+      "    \n",
+      "        The ``user_data`` is the data contained in the `scipy.LowLevelCallable`.\n",
+      "        In the call forms with ``xx``,  ``n`` is the length of the ``xx``\n",
+      "        array which contains ``xx[0] == x`` and the rest of the items are\n",
+      "        numbers contained in the ``args`` argument of quad.\n",
+      "    \n",
+      "        In addition, certain ctypes call signatures are supported for\n",
+      "        backward compatibility, but those should not be used in new code.\n",
+      "    a : float\n",
+      "        Lower limit of integration (use -numpy.inf for -infinity).\n",
+      "    b : float\n",
+      "        Upper limit of integration (use numpy.inf for +infinity).\n",
+      "    args : tuple, optional\n",
+      "        Extra arguments to pass to `func`.\n",
+      "    full_output : int, optional\n",
+      "        Non-zero to return a dictionary of integration information.\n",
+      "        If non-zero, warning messages are also suppressed and the\n",
+      "        message is appended to the output tuple.\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    y : float\n",
+      "        The integral of func from `a` to `b`.\n",
+      "    abserr : float\n",
+      "        An estimate of the absolute error in the result.\n",
+      "    infodict : dict\n",
+      "        A dictionary containing additional information.\n",
+      "        Run scipy.integrate.quad_explain() for more information.\n",
+      "    message\n",
+      "        A convergence message.\n",
+      "    explain\n",
+      "        Appended only with 'cos' or 'sin' weighting and infinite\n",
+      "        integration limits, it contains an explanation of the codes in\n",
+      "        infodict['ierlst']\n",
+      "    \n",
+      "    Other Parameters\n",
+      "    ----------------\n",
+      "    epsabs : float or int, optional\n",
+      "        Absolute error tolerance. Default is 1.49e-8. `quad` tries to obtain\n",
+      "        an accuracy of ``abs(i-result) <= max(epsabs, epsrel*abs(i))``\n",
+      "        where ``i`` = integral of `func` from `a` to `b`, and ``result`` is the\n",
+      "        numerical approximation. See `epsrel` below.\n",
+      "    epsrel : float or int, optional\n",
+      "        Relative error tolerance. Default is 1.49e-8.\n",
+      "        If ``epsabs <= 0``, `epsrel` must be greater than both 5e-29\n",
+      "        and ``50 * (machine epsilon)``. See `epsabs` above.\n",
+      "    limit : float or int, optional\n",
+      "        An upper bound on the number of subintervals used in the adaptive\n",
+      "        algorithm.\n",
+      "    points : (sequence of floats,ints), optional\n",
+      "        A sequence of break points in the bounded integration interval\n",
+      "        where local difficulties of the integrand may occur (e.g.,\n",
+      "        singularities, discontinuities). The sequence does not have\n",
+      "        to be sorted. Note that this option cannot be used in conjunction\n",
+      "        with ``weight``.\n",
+      "    weight : float or int, optional\n",
+      "        String indicating weighting function. Full explanation for this\n",
+      "        and the remaining arguments can be found below.\n",
+      "    wvar : optional\n",
+      "        Variables for use with weighting functions.\n",
+      "    wopts : optional\n",
+      "        Optional input for reusing Chebyshev moments.\n",
+      "    maxp1 : float or int, optional\n",
+      "        An upper bound on the number of Chebyshev moments.\n",
+      "    limlst : int, optional\n",
+      "        Upper bound on the number of cycles (>=3) for use with a sinusoidal\n",
+      "        weighting and an infinite end-point.\n",
+      "    \n",
+      "    See Also\n",
+      "    --------\n",
+      "    dblquad : double integral\n",
+      "    tplquad : triple integral\n",
+      "    nquad : n-dimensional integrals (uses `quad` recursively)\n",
+      "    fixed_quad : fixed-order Gaussian quadrature\n",
+      "    quadrature : adaptive Gaussian quadrature\n",
+      "    odeint : ODE integrator\n",
+      "    ode : ODE integrator\n",
+      "    simps : integrator for sampled data\n",
+      "    romb : integrator for sampled data\n",
+      "    scipy.special : for coefficients and roots of orthogonal polynomials\n",
+      "    \n",
+      "    Notes\n",
+      "    -----\n",
+      "    \n",
+      "    **Extra information for quad() inputs and outputs**\n",
+      "    \n",
+      "    If full_output is non-zero, then the third output argument\n",
+      "    (infodict) is a dictionary with entries as tabulated below. For\n",
+      "    infinite limits, the range is transformed to (0,1) and the\n",
+      "    optional outputs are given with respect to this transformed range.\n",
+      "    Let M be the input argument limit and let K be infodict['last'].\n",
+      "    The entries are:\n",
+      "    \n",
+      "    'neval'\n",
+      "        The number of function evaluations.\n",
+      "    'last'\n",
+      "        The number, K, of subintervals produced in the subdivision process.\n",
+      "    'alist'\n",
+      "        A rank-1 array of length M, the first K elements of which are the\n",
+      "        left end points of the subintervals in the partition of the\n",
+      "        integration range.\n",
+      "    'blist'\n",
+      "        A rank-1 array of length M, the first K elements of which are the\n",
+      "        right end points of the subintervals.\n",
+      "    'rlist'\n",
+      "        A rank-1 array of length M, the first K elements of which are the\n",
+      "        integral approximations on the subintervals.\n",
+      "    'elist'\n",
+      "        A rank-1 array of length M, the first K elements of which are the\n",
+      "        moduli of the absolute error estimates on the subintervals.\n",
+      "    'iord'\n",
+      "        A rank-1 integer array of length M, the first L elements of\n",
+      "        which are pointers to the error estimates over the subintervals\n",
+      "        with ``L=K`` if ``K<=M/2+2`` or ``L=M+1-K`` otherwise. Let I be the\n",
+      "        sequence ``infodict['iord']`` and let E be the sequence\n",
+      "        ``infodict['elist']``.  Then ``E[I[1]], ..., E[I[L]]`` forms a\n",
+      "        decreasing sequence.\n",
+      "    \n",
+      "    If the input argument points is provided (i.e., it is not None),\n",
+      "    the following additional outputs are placed in the output\n",
+      "    dictionary. Assume the points sequence is of length P.\n",
+      "    \n",
+      "    'pts'\n",
+      "        A rank-1 array of length P+2 containing the integration limits\n",
+      "        and the break points of the intervals in ascending order.\n",
+      "        This is an array giving the subintervals over which integration\n",
+      "        will occur.\n",
+      "    'level'\n",
+      "        A rank-1 integer array of length M (=limit), containing the\n",
+      "        subdivision levels of the subintervals, i.e., if (aa,bb) is a\n",
+      "        subinterval of ``(pts[1], pts[2])`` where ``pts[0]`` and ``pts[2]``\n",
+      "        are adjacent elements of ``infodict['pts']``, then (aa,bb) has level l\n",
+      "        if ``|bb-aa| = |pts[2]-pts[1]| * 2**(-l)``.\n",
+      "    'ndin'\n",
+      "        A rank-1 integer array of length P+2. After the first integration\n",
+      "        over the intervals (pts[1], pts[2]), the error estimates over some\n",
+      "        of the intervals may have been increased artificially in order to\n",
+      "        put their subdivision forward. This array has ones in slots\n",
+      "        corresponding to the subintervals for which this happens.\n",
+      "    \n",
+      "    **Weighting the integrand**\n",
+      "    \n",
+      "    The input variables, *weight* and *wvar*, are used to weight the\n",
+      "    integrand by a select list of functions. Different integration\n",
+      "    methods are used to compute the integral with these weighting\n",
+      "    functions, and these do not support specifying break points. The\n",
+      "    possible values of weight and the corresponding weighting functions are.\n",
+      "    \n",
+      "    ==========  ===================================   =====================\n",
+      "    ``weight``  Weight function used                  ``wvar``\n",
+      "    ==========  ===================================   =====================\n",
+      "    'cos'       cos(w*x)                              wvar = w\n",
+      "    'sin'       sin(w*x)                              wvar = w\n",
+      "    'alg'       g(x) = ((x-a)**alpha)*((b-x)**beta)   wvar = (alpha, beta)\n",
+      "    'alg-loga'  g(x)*log(x-a)                         wvar = (alpha, beta)\n",
+      "    'alg-logb'  g(x)*log(b-x)                         wvar = (alpha, beta)\n",
+      "    'alg-log'   g(x)*log(x-a)*log(b-x)                wvar = (alpha, beta)\n",
+      "    'cauchy'    1/(x-c)                               wvar = c\n",
+      "    ==========  ===================================   =====================\n",
+      "    \n",
+      "    wvar holds the parameter w, (alpha, beta), or c depending on the weight\n",
+      "    selected. In these expressions, a and b are the integration limits.\n",
+      "    \n",
+      "    For the 'cos' and 'sin' weighting, additional inputs and outputs are\n",
+      "    available.\n",
+      "    \n",
+      "    For finite integration limits, the integration is performed using a\n",
+      "    Clenshaw-Curtis method which uses Chebyshev moments. For repeated\n",
+      "    calculations, these moments are saved in the output dictionary:\n",
+      "    \n",
+      "    'momcom'\n",
+      "        The maximum level of Chebyshev moments that have been computed,\n",
+      "        i.e., if ``M_c`` is ``infodict['momcom']`` then the moments have been\n",
+      "        computed for intervals of length ``|b-a| * 2**(-l)``,\n",
+      "        ``l=0,1,...,M_c``.\n",
+      "    'nnlog'\n",
+      "        A rank-1 integer array of length M(=limit), containing the\n",
+      "        subdivision levels of the subintervals, i.e., an element of this\n",
+      "        array is equal to l if the corresponding subinterval is\n",
+      "        ``|b-a|* 2**(-l)``.\n",
+      "    'chebmo'\n",
+      "        A rank-2 array of shape (25, maxp1) containing the computed\n",
+      "        Chebyshev moments. These can be passed on to an integration\n",
+      "        over the same interval by passing this array as the second\n",
+      "        element of the sequence wopts and passing infodict['momcom'] as\n",
+      "        the first element.\n",
+      "    \n",
+      "    If one of the integration limits is infinite, then a Fourier integral is\n",
+      "    computed (assuming w neq 0). If full_output is 1 and a numerical error\n",
+      "    is encountered, besides the error message attached to the output tuple,\n",
+      "    a dictionary is also appended to the output tuple which translates the\n",
+      "    error codes in the array ``info['ierlst']`` to English messages. The\n",
+      "    output information dictionary contains the following entries instead of\n",
+      "    'last', 'alist', 'blist', 'rlist', and 'elist':\n",
+      "    \n",
+      "    'lst'\n",
+      "        The number of subintervals needed for the integration (call it ``K_f``).\n",
+      "    'rslst'\n",
+      "        A rank-1 array of length M_f=limlst, whose first ``K_f`` elements\n",
+      "        contain the integral contribution over the interval\n",
+      "        ``(a+(k-1)c, a+kc)`` where ``c = (2*floor(|w|) + 1) * pi / |w|``\n",
+      "        and ``k=1,2,...,K_f``.\n",
+      "    'erlst'\n",
+      "        A rank-1 array of length ``M_f`` containing the error estimate\n",
+      "        corresponding to the interval in the same position in\n",
+      "        ``infodict['rslist']``.\n",
+      "    'ierlst'\n",
+      "        A rank-1 integer array of length ``M_f`` containing an error flag\n",
+      "        corresponding to the interval in the same position in\n",
+      "        ``infodict['rslist']``.  See the explanation dictionary (last entry\n",
+      "        in the output tuple) for the meaning of the codes.\n",
+      "    \n",
+      "    Examples\n",
+      "    --------\n",
+      "    Calculate :math:`\\int^4_0 x^2 dx` and compare with an analytic result\n",
+      "    \n",
+      "    >>> from scipy import integrate\n",
+      "    >>> x2 = lambda x: x**2\n",
+      "    >>> integrate.quad(x2, 0, 4)\n",
+      "    (21.333333333333332, 2.3684757858670003e-13)\n",
+      "    >>> print(4**3 / 3.)  # analytical result\n",
+      "    21.3333333333\n",
+      "    \n",
+      "    Calculate :math:`\\int^\\infty_0 e^{-x} dx`\n",
+      "    \n",
+      "    >>> invexp = lambda x: np.exp(-x)\n",
+      "    >>> integrate.quad(invexp, 0, np.inf)\n",
+      "    (1.0, 5.842605999138044e-11)\n",
+      "    \n",
+      "    >>> f = lambda x,a : a*x\n",
+      "    >>> y, err = integrate.quad(f, 0, 1, args=(1,))\n",
+      "    >>> y\n",
+      "    0.5\n",
+      "    >>> y, err = integrate.quad(f, 0, 1, args=(3,))\n",
+      "    >>> y\n",
+      "    1.5\n",
+      "    \n",
+      "    Calculate :math:`\\int^1_0 x^2 + y^2 dx` with ctypes, holding\n",
+      "    y parameter as 1::\n",
+      "    \n",
+      "        testlib.c =>\n",
+      "            double func(int n, double args[n]){\n",
+      "                return args[0]*args[0] + args[1]*args[1];}\n",
+      "        compile to library testlib.*\n",
+      "    \n",
+      "    ::\n",
+      "    \n",
+      "       from scipy import integrate\n",
+      "       import ctypes\n",
+      "       lib = ctypes.CDLL('/home/.../testlib.*') #use absolute path\n",
+      "       lib.func.restype = ctypes.c_double\n",
+      "       lib.func.argtypes = (ctypes.c_int,ctypes.c_double)\n",
+      "       integrate.quad(lib.func,0,1,(1))\n",
+      "       #(1.3333333333333333, 1.4802973661668752e-14)\n",
+      "       print((1.0**3/3.0 + 1.0) - (0.0**3/3.0 + 0.0)) #Analytic result\n",
+      "       # 1.3333333333333333\n",
+      "    \n",
+      "    Be aware that pulse shapes and other sharp features as compared to the\n",
+      "    size of the integration interval may not be integrated correctly using\n",
+      "    this method. A simplified example of this limitation is integrating a\n",
+      "    y-axis reflected step function with many zero values within the integrals\n",
+      "    bounds.\n",
+      "    \n",
+      "    >>> y = lambda x: 1 if x<=0 else 0\n",
+      "    >>> integrate.quad(y, -1, 1)\n",
+      "    (1.0, 1.1102230246251565e-14)\n",
+      "    >>> integrate.quad(y, -1, 100)\n",
+      "    (1.0000000002199108, 1.0189464580163188e-08)\n",
+      "    >>> integrate.quad(y, -1, 10000)\n",
+      "    (0.0, 0.0)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import integrate\n",
+    "help(integrate.quad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3.9086503371292665, 4.3394735994897923e-14)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = scipy.integrate.quad(lambda x: special.exp10(x),0,1)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Double intergration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.0, 4.405142707569776e-14)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e = lambda x,y: x*y**2\n",
+    "f = lambda x: 1\n",
+    "g = lambda x:-1\n",
+    "integrate.dblquad(e,0,2,f,g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fourier Transformations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "array =  [1 2 3 4 5]\n",
+      "\n",
+      " fourier transform =  [15. -0.j         -2.5+3.4409548j  -2.5+0.81229924j -2.5-0.81229924j\n",
+      " -2.5-3.4409548j ]\n",
+      "\n",
+      " inverse fourier transform =  [ 3. -0.j         -0.5-0.68819096j -0.5-0.16245985j -0.5+0.16245985j\n",
+      " -0.5+0.68819096j]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.fftpack import fft,ifft\n",
+    "import numpy as np \n",
+    "arr = np.array([1,2,3,4,5])\n",
+    "fft = fft(arr)      #fourier transform\n",
+    "ifft = ifft(arr)    #inverse fourier transform\n",
+    "print(\"array = \" ,arr)\n",
+    "print(\"\\n fourier transform = \",fft)\n",
+    "print(\"\\n inverse fourier transform = \",ifft)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Algebra "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.14285714  0.21428571]\n",
+      " [ 0.28571429 -0.17857143]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import linalg\n",
+    "a = np.array([[5,6],[8,4]])\n",
+    "b = linalg.inv(a)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Interpolation Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x18c10ccb348>,\n",
+       " <matplotlib.lines.Line2D at 0x18c10cd3508>]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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1h5/kHJ6/9/ZR/umkcoiIbZnZUes9e+7qY+P2Ln5vw9O0Hu/m+jjMnNcP8dOH/jt/8dan+MfvXwhPfgd+8Bk4deLtg2IcXP/JSi/8yg/Ah78IbVfClLkw5Uo+8rVn6Tl6FIC/PrXwzGHtjo1LdWG4N7lz+gLP0Z/BgafhjYPwxqG3X798J7TfwI8eeYBnxq1m/KRTZx326UffWwn3mQvgVz4NU64sAvxKuGwOtEyo7PjuD1VeVT6/5ORZ89HBsXGpngz3UVKPb1FWLlI+Qxz/OZfHEXq6J7FqwzEmHOvmoz9/uCq4D8Kbh+DmVbDwNyvTCO//6NsnGjceJs+A91Tatr0xla+3fIzD2ca+nMbenMG+nE7PW5P4Y4D2GyqvYXBsXBpdhnsv9QvhAb5FmQnH3oQ8CZMuq2zv+gH0dMNb3W8vr3g/XPvrcPQNWPshPvTaIZ4e9yYTJ1XO+9UTt/PV47/O1x/dxUePfgUmT4NLZsLk6fDOX4R3zKoU9M558M8frrx3yczKxctxb08lPHHZXP6g+zf6/BznO4TiNz6l0dPU4T7SQTzsr7KfPA7H3qgE89E3YFxL5Ys2ALv+vNJrPvYGrz76LJ/Jn/Fyy7v43slbAfgG/4m5m16FLcfgrSOV8evrlsPt36jMEvmz34ETb1XOFeMqoT/pssr2hIvhXdfxyMEjHGEyR3Iyr3Epz5x6NwDPHrkIVr8KLf38573oEnj3zf3+OaxcPN8hFKnJNW241+OeItUPaLiz5REWjHuJybzF1P95DDonwqWz4Te+U9n5W78Gex87+wRzboI7N1fW/9eX4NXKV+B/B+hpmcijpxaeCffDtHHkxGSuuPrqSs+5tQ1mXvP2uf7VozDxkkr7xHec1bNm3Di440+47/8+SleNb2LObru4/2AfAodQpOZXt3CPiCXAHwEtwDcz896RPP9AT8o51xCq/sr6gnEv8b74CT9nEm+emASTiiGO067/JPzir1Z6wRMnV16nhz2gMld73HiYeAkf/MPH2Hvk2Fmf9bnjn6K9rZWP3XZL7WJmLhi03nr2sB1CkZpbXcI9IlqA/wr8KrAPeCIiNmXmcyP1GfV4Us7sttYzPeF/d/xfn2lvb2vlf/92rxDu+BcDn6ztijOrn1tydV1C2B62pP7Uq+d+I7AnM38KEBEPAkuBEQv36iDu3X6u6tUTrmcI28OWVEu9wr0d2Fu1vQ/4B9U7RMRdwF0AV1xxBcNVjyA2hCWVRb3CvdZNQc66z0FmrgXWQuX2A8P9gHoFsSEsqQzqFe77gDlV25cD+0f6QwxiSaqtXk9iegKYFxFXRcREYDmwqU6fJUnqpS4998w8ERH/BthMZSrktzNzZz0+S5LUV93muWfmI8Aj9Tq/JKl/PiBbkkrIcJekEhoTT2KKiMPAy42uo5dpwKuNLmIYmqneZqoVmqveZqoVmqvesVjrlZk5vdYbYyLcx6KI6Ozv8VVjUTPV20y1QnPV20y1QnPV20y1gsMyklRKhrsklZDh3r+1jS5gmJqp3maqFZqr3maqFZqr3maq1TF3SSoje+6SVEKGuySVkOFeQ0S0RcT3I2JXRDwfEe9vdE39iYjPRsTOiHg2Ih6IiEmNrqlaRHw7Ig5FxLNVbVMjYktEvFAspzSyxtP6qXVN8XvwTEQ8FBFtjayxWq16q977fERkRExrRG299VdrRPzbiNhd/A5/pVH19dbP78LCiPhxRDwVEZ0RcWMjaxyM4V7bHwF/mZnvAa4Hnm9wPTVFRDvwaaAjM6+hcpO25Y2tqo/7gSW92u4BtmbmPGBrsT0W3E/fWrcA12TmdcBPgFWjXdQA7qdvvUTEHCqPuHxltAsawP30qjUiPkzlCW3XZeYC4A8aUFd/7qfvn+1XgC9l5kLgPxTbY5bh3ktEXAr8I+BbAJl5LDO7G1vVgMYDrRExHriYOtw3/3xk5o+Av+vVvBRYV6yvA5aNalH9qFVrZv4wM08Umz+m8myCMaGfP1uAPwS+QK8H5DRSP7V+Crg3M48W+xwa9cL60U+9CVxarF/GGPu71pvh3te7gcPAn0TE9oj4ZkRMbnRRtWRmF5XezivAAeBIZv6wsVUNyczMPABQLGc0uJ6h+pfAXzS6iIFExMeBrsx8utG1DMEvAR+MiMci4m8i4pcbXdAgPgOsiYi9VP7ejaV/xfVhuPc1HrgB+G+ZuQh4k7EzbHCWYqx6KXAVMBuYHBG/1diqyikivgicAL7b6Fr6ExEXA1+kMmTQDMYDU4CbgJXA+oio9YjOseJTwGczcw7wWYp/3Y9Vhntf+4B9mflYsf19KmE/Fn0EeDEzD2fmcWAD8CsNrmkoDkbELIBiOWb+OV5LRKwAbgP+WY7tL4b8ApX/0T8dES9RGUJ6MiLe1dCq+rcP2JAVjwOnqNyca6xaQeXvGMD/ALyg2kwy8/8BeyNiftF0K/BcA0sayCvATRFxcdHjuZUxevG3l01U/qJQLB9uYC0DioglwL8HPp6ZP290PQPJzB2ZOSMz52bmXCrheUPxOz0WbQRuAYiIXwImMvbuulhtP/ChYv0W4IUG1jK4zPTV6wUsBDqBZ6j8Ak5pdE0D1PolYBfwLPAd4KJG19SrvgeoXA84TiVs7gTeSWWWzAvFcmqj6xyg1j3AXuCp4vX1Rtc5UL293n8JmNboOgf4s50I/Gnxu/skcEuj6xyk3n8IbAOeBh4D3tfoOgd6efsBSSohh2UkqYQMd0kqIcNdkkrIcJekEjLcJamEDHdJKiHDXZJK6P8DCSJgX8twOA8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import interpolate\n",
+    "\n",
+    "x = np.arange(5,20)\n",
+    "y = np.exp(x/3.0)\n",
+    "f = interpolate.interp1d(x,y)\n",
+    "\n",
+    "x1 = np.arange(6,12)\n",
+    "y1 = f(x1)               # use interpolation function returned by 'interp1d'\n",
+    "plt.plot(x,y, 'o' ,x1,y1, '--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}