diff --git a/.travis.yml b/.travis.yml
index 6940d752f9..63c99928f7 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -25,7 +25,8 @@ before_install:
     
 install:
     - echo "install"
-    - conda install --yes python=$TRAVIS_PYTHON_VERSION numpy scipy pandas=$PD_VERSION nose pytz ephem
+    - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then conda install --yes python=$TRAVIS_PYTHON_VERSION numpy scipy pandas=$PD_VERSION nose pytz ephem; fi
+    - if [[ $TRAVIS_PYTHON_VERSION == '3.3' || $TRAVIS_PYTHON_VERSION == '3.4' ]]; then conda install --yes python=$TRAVIS_PYTHON_VERSION numpy=1.8 scipy=0.14 pandas nose pytz ephem; conda install --yes pandas=$PD_VERSION --no-deps; fi
     - test $PD_VERSION != 0.13.1 && conda install --yes "numba>=0.17" || echo "Not installing numba"
     - conda install --yes coverage
     - pip install coveralls
diff --git a/docs/sphinx/source/whatsnew/v0.2.0.txt b/docs/sphinx/source/whatsnew/v0.2.0.txt
index d637944e80..7c8cf5d566 100644
--- a/docs/sphinx/source/whatsnew/v0.2.0.txt
+++ b/docs/sphinx/source/whatsnew/v0.2.0.txt
@@ -3,17 +3,25 @@
 v0.2.0 (??, 2015)
 -----------------------
 
-This is a major release from 0.1 and includes a small number of API changes,
+This is a major release from 0.1 and includes a large number of API changes,
 several new features and enhancements along with a number of bug fixes.
 We recommend that all users upgrade to this version.
 
+Due to the large number of API changes, you will probably need to update your
+code.
+
 
 API changes
 ~~~~~~~~~~~
 
+* Change variable names to conform with new
+  `Variables and style rules wiki <https://github.com/pvlib/pvlib-python/wiki/Variables-and-style-rules>`_.
+  This impacts many function declarations and return values.
+  Your existing code probably will not work! (:issue:`37`, :issue:`54`).
 * Move ``dirint`` and ``disc`` algorithms from ``clearsky.py``
   to ``irradiance.py`` (:issue:`42`)
 * Mark some ``pvsystem.py`` methods as private (:issue:`20`)
+* Make output of ``pvsystem.sapm_celltemp`` a DataFrame (:issue:`54`)
 
 Enhancements
 ~~~~~~~~~~~~
@@ -23,6 +31,7 @@ Enhancements
 * Add optional ``projection_ratio`` keyword argument to the ``haydavies``
   calculator. Speeds calculations when irradiance changes but
   solar position remains the same (:issue:`58`)
+* Improved installation instructions in README.
 
 Bug fixes
 ~~~~~~~~~
diff --git a/docs/tutorials/pvsystem.ipynb b/docs/tutorials/pvsystem.ipynb
index 15ef626d64..c18b9cfed1 100644
--- a/docs/tutorials/pvsystem.ipynb
+++ b/docs/tutorials/pvsystem.ipynb
@@ -173,7 +173,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e51ca4278>"
+       "<matplotlib.text.Text at 0x7f77f2200390>"
       ]
      },
      "execution_count": 6,
@@ -182,240 +182,9 @@
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     {
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V8E7/XxxYF0xIEiXjkwPb1JwuMh+FPvFMJh9yJLKQSiZxM0sBbwDeDWwCUsBr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\nrA4wJhERkboRZBIvd/7Q9Im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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e410e85f8>"
+       "<matplotlib.figure.Figure at 0x7f77bb59b8d0>"
       ]
      },
      "metadata": {},
@@ -441,7 +210,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e22175e10>"
+       "<matplotlib.text.Text at 0x7f77bb521390>"
       ]
      },
      "execution_count": 7,
@@ -450,233 +219,9 @@
     },
     {
      "data": {
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+      "image/png": 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VksRPd4zaPaVuNKhNZGXmkrhGqMdOLcl4l5n9d+CjQILKmuoP+RqVxMqkppeJ\nrIg2QYmvWirxf0tldPojwMNAJ/Bv/AxK4kVLroqsjJrT46uW0eljwLsDiEViSs3pIitTndmhJB4/\nS+0n/grn3PcX21dc+4lLvVT78ZTERZbn5CYoWj89bpaqxG8Bvs/i+4oriUtdqE9cZGXasmmSiYQq\n8Rhaaj/xX/e+XhdYNBJLak4XWZlEIkGHNkGJpVoWe/lnZtblff8eM/tLMzvP/9AkLk4mcc1cFFmu\nzraMkngM1TI6/ZPAhJldAvw74HngM75GJbFS7cdTJS6yfB1tGSZn8pTKp+v9lKiqdSvSMvBG4C+d\ncx8Gev0NS+JkYjpPIlHp1xOR5elszVAuw3ROg9vipJarZsrMrgZ+HnjPWfwcZnYDcDuQAj7tnPvI\nIsddCfwAeIdz7su1PLdEx1SuQEdrhkQiEXYoIk2ro7VyWZ6aqbyfJB5qqcT/ELgL+IFz7kkzM+Dp\nM/2QmaWAO4AbgIuBm83sokWO+wjwTSorwknMTM7kaVcVLrIibfOSuMRHLVfO+51zX6vecM454O01\n/NxVwD7n3H4AM7sbuBHYs+C43wT+N3BlLQFL9EzPFOjt0/aJIitRrb6ntH56rNRSie83sz8zs21n\n+dybgIPzbh/y7ptjZpuoJPY7vbs0IiNm8oUSs4US7a2qxEVWotqaNalKPFZqSeIvBUaB75rZfWb2\nlhqfu5aEfDvw+97AuQRqTo+datXQrj48kRWpfhCe0sC2WKll7fQB4I/N7E+oVM1/YWafoNLf/Unn\n3MwiP3oY2DLv9hYq1fh8LwfurnSz0we80czyzrl7Fount7eddDp1prAD19/fFXYIoVjpec+UKl/X\nrGprqt9hM8VaTzrvxrVx3SQAiVSqbvE2w3n7oZnOu9ZR5u3Au4B/DeyjMk/81cB93tfT2QXsMLOt\nwAvATcDN8w9wzp0/7//4LPD1pRI4wMjIVC0hB6q/v4vBwfGwwwhcPc770JExAJLlctP8DvX3jpdm\nOe98rtKqdWxooi7xNst511sjnvdSHyrOmMTN7A4q08vuAd7pnHvCe+gLZrZ3sZ9zzhXM7DbgfipT\nzD7jnNtjZrd6j99V+ylIVFVH0qpPXGRl1JweT7VcOQ8AFzvnRk7z2GuW+kHn3H1UqvX59502eTvn\ntN1pDKlPXKQ+qgPbNMUsXmrpE/8ogJm1zD/eOTflnHvBx9gkBqpVQ4cqcZEVOTnFTEk8TmppTn87\n8HFg47y3kOQjAAAR9ElEQVS7y1SayEVWZFLN6SJ10ZJJkkomNE88Zmq5cn4UeAfwI+dc0ed4JGbm\nmtOzak4XWYlEIkF7a1rzxGOmliQ+5Jx70PdIJJaqTX9qThdZufbWjAa2xcyiV05vWlkC+IqZvQ+4\nG5ibE+6ca7y5XtJ0qkm8TUlcZMXas2mGxqYpl8vaUCgmlrpyTiy4fce879UnLnVRrRq0AYrIynW0\npikUy+QLJVoyukTHwaJXTudcLUuyiqzI5EyebCZFOqWXm8hKVQeITs4UlMRjotYV2/qAa7ybP3TO\nHfcvJImTqZmCRqaL1El1vYWpXIHeLu0MGAdnLH+8KWZ7gd/y/u0xs7f5HZjEw9RMQYPaROqkY25P\ncU0zi4tarp4fBq51zj0FYGY7gK8DX/EzMIm+UrnMdK5Ae7Yj7FBEIkHbkcZPLR2R09UEDuCcexrQ\nyHRZsZlcgTJaclWkXqpdU9NK4rFRSyX+NTP7j1R2LksA7wa+6k1B01QzWTat1iZSX9UPxJNqTo+N\nWq6eH/C+/tGC+z+EpprJCmgHM5H60k5m8VPLBiia+yO+qA6+6VBzukhdnBzYpiQeF0rQEpq55nQt\n9CJSFycHtqk5PS6UxCU00zk1p4vUU7u2I40dJXEJTTWJt6kSF6mL1pbKEKWZWW04GRdK4hKaae9C\n09aisZEi9ZBOJWlJJ+c+IEv0KYlLaKoXmlZV4iJ105ZNK4nHiJK4hGZaO5iJ1F1rNj3XyiXRpyQu\noaleaFSJi9RPezalSjxGlMQlNCcrcfWJi9RLa0uafKFEoVgKOxQJgJK4hGYmVyCVTGgvcZE6qs72\nUDUeD7p6SmimZ4u0ZdMkEomwQxGJjDavZUv94vGgJC6hmc4V5i44IlIfbS2VSnxGlXgsKIlLaKZz\nhbkLjojUh5rT40VJXEJRKpWZmS1qZLpInZ1M4mpOjwMlcQlFdVlIzREXqa+5PnFV4rGgJC6hmJmt\nrtamPnGRepqrxGeVxONASVxCMVXd/ER94iJ11dqiPvE4URKXUMx4/XXawUykvtrVJx4rSuISimpT\nn6aYidRXq/rEY0VJXEIxt4OZmtNF6qpdfeKxoiQuodAOZiL+mOsTn1ESjwMlcQlFtb9Oo9NF6qu1\nRcuuxomSuIRClbiIP5LJBK0t2o40LpTEJRTV/jr1iYvUX1s2rSQeE0riEorqBUaj00Xqry2bnlsV\nUaJNSVxCoXniIv5p85rTy+Vy2KGIz5TEJRRTmmIm4pu2bJpiqUy+UAo7FPGZkriEYma2QDqVJJPW\nS1Ck3lq1HWls6AoqoZjOFWlXf7iIL9q8aWZTSuKRpyQuoZjOFbSXuIhPqt1UubwGt0WdkriEYnq2\noB3MRHxSXfBlRpugRJ6SuASuVCozmy9pepmIT6orIc6oEo88JXEJXHX+ajajJC7ih1bvvTWjTVAi\nT0lcAlftp1OfuIg/qn3iWvAl+pTEJXDV6kCVuIg/qn3iOSXxyFMSl8BVq4PqhUZE6itbHdimJB55\nSuISOCVxEX+dbE5Xn3jUKYlL4KpNfFklcRFfZNWcHhtK4hK4mbzWTRfxU5ua02NDSVwCN9ecroFt\nIr5oVRKPDd9LITO7AbgdSAGfds59ZMHj7wR+F0gA48C/ds791O+4JDw59YmL+OrkwDb1iUedr5W4\nmaWAO4AbgIuBm83sogWHPQv8M+fcTuCPgb/yMyYJ34z6xEV8lUpWdghUJR59flfiVwH7nHP7Aczs\nbuBGYE/1AOfcD+Yd/yNgs88xSchOVuLqExfxS2tLSkk8BvzuE98EHJx3+5B332J+DbjX14gkdHOL\nvagSF/FNNpPSLmYx4HcpVK71QDN7NfCrwCuWOq63t510uvEu/v39XWGHEIplnXey8tlx0/oe+nvb\n6hxRMPT3jpdmPO/O9haOjUytKPZmPO96aKbz9juJHwa2zLu9hUo1fgoz2wl8CrjBOTey1BOOjEzV\nNcB66O/vYnBwPOwwArfc8x49MQPA5MQ0FJpv4I3+3vHSrOedTiWYzhU4duwEiUTirH++Wc97pRrx\nvJf6UOF3Et8F7DCzrcALwE3AzfMPMLNzgC8Dv+Sc2+dzPNIAqk18WjtdxD+tmRTlMswWSnqvRZiv\nfeLOuQJwG3A/sBv4onNuj5ndama3eod9AOgF7jSzR83sx37GJOGbmS2STiVJp7RMgYhfNFc8Hnwf\nHuycuw+4b8F9d837/j3Ae/yOQxrHzGxBc8RFfFad/ZGbLUBHS8jRiF9UCkngcvmikriIz7STWTwo\niUvgZnJFTS8T8Zma0+NBSVwCVS6XVYmLBEBJPB6UxCVQhWKJYqmszU9EfKY9xeNBSVwCNaMlV0UC\noUo8HpTEJVDa/EQkGNUknlMSjzQlcQmUtiEVCYa2I40HJXEJ1ExelbhIEE72iasSjzIlcQlUtSrQ\nwDYRf831iWsns0hTEpdAaS9xkWBUPyjP5JTEo0xJXAKlgW0iwai+x2ZViUeakrgEakYD20QCUd25\nLKckHmlK4hKouT5xJXERX2XSSRIoiUedkrgEqnpBUZ+4iL8SiQQtLSnNE484JXEJVHWQTVaj00V8\nl82kVIlHnJK4BOpkJa4kLuK3bCapJB5xSuISqOoFpUWVuIjvKpV4KewwxEdK4hKoWe+Cks3opSfi\nt2wmpSlmEacrqQRKlbhIcFoyKYqlMoWiqvGoUhKXQM3mi6SSCdIpvfRE/Ka54tGnK6kEKpcvqgoX\nCUhW25FGnpK4BCqXL6o/XCQg1feaKvHo0tVUAjWbL2mOuEhAqq1esxqhHllK4hKoSiWuJC4SBPWJ\nR5+SuASmXC6rT1wkQEri0ackLoEpFMuUy5ojLhKUuSSugW2RpaupBEZzxEWCNTc6XZV4ZCmJS2Cq\nK0dltW66SCBavFYvrdoWXUriEpi5SjytJC4ShJN94hqdHlVK4hKYk+umK4mLBEED26JPSVwCk5tr\nTtfLTiQISuLRp6upBGYuiasSFwmEknj0KYlLYGbVJy4SqBZvEOmspphFlpK4BCan0ekigWpVJR55\nSuISmOoI2RYt9iISCI1Ojz5dTSUw1VWj1CcuEoyMdjGLPCVxCcxsQSu2iQQpmUjQkkkqiUeYkrgE\nRqPTRYKXzaS0YluEKYlLYGZntdiLSNCymRQzGp0eWUriEpjcXHO6XnYiQVElHm26mkpgZtWcLhK4\nlkxKfeIRpiQugdHodJHgZTNJCsUyxZKmmUWRkrgEZrageeIiQZubKz6rJB5FuppKYHL5IulUglRS\nLzuRoFRXSFSTejTpaiqByeWLakoXCVh1XQYNbosmJXEJTG62qIVeRAKW9TYcqnZnSbQoiUtgZgsl\nVeIiAauOQVElHk1K4hKYXL6oQW0iAVNzerTpiiqBKJfLzM6qT1wkaHOj09WcHklK4hKIQrFEGc0R\nFwmamtOjTUlcAlHdz1hJXCRYLWlNMYsyJXEJRHW1NvWJiwTrZCWu5vQoSvv55GZ2A3A7kAI+7Zz7\nyGmO+TjwRmAKuMU596ifMUk4tA2pSDjmBrYVVIlHkW9lkZmlgDuAG4CLgZvN7KIFx7wJ2O6c2wG8\nF7jTr3ikMXS0ZcIOQSRWOlq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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e22118fd0>"
+       "<matplotlib.figure.Figure at 0x7f77bb59b650>"
       ]
      },
      "metadata": {},
@@ -702,7 +247,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f9e220fcc18>"
+       "<matplotlib.legend.Legend at 0x7f77bb2d1590>"
       ]
      },
      "execution_count": 8,
@@ -711,329 +256,9 @@
     },
     {
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8+waWtX14F5FQRJueSE1a3rqE4eQIByYm10tvqo/S0VLHzl5thCKTijqdy1p7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\nMsQlSZooQ1ySpIkyxCVJmihDXJKkifoJ8QxF/J6bLjEAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e220865c0>"
+       "<matplotlib.figure.Figure at 0x7f77bb50d990>"
       ]
      },
      "metadata": {},
@@ -1069,6 +294,13 @@
     "The $a$, $b$, and $\\Delta T$ parameters depend on the module and racking type. The default parameter set is ``open_rack_cell_glassback``."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``sapm_celltemp`` works with either scalar or vector inputs, but always returns a pandas DataFrame."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -1078,8 +310,29 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp_cell</th>\n",
+       "      <th>temp_module</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>43.509191</td>\n",
+       "      <td>40.809191</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "{'tcell': 43.509190983665746, 'tmodule': 40.809190983665744}"
+       "   temp_cell  temp_module\n",
+       "0  43.509191    40.809191"
       ]
      },
      "execution_count": 9,
@@ -1088,7 +341,8 @@
     }
    ],
    "source": [
-    "pvsystem.sapm_celltemp(900, 5, 20)"
+    "# scalar inputs\n",
+    "pvsystem.sapm_celltemp(900, 5, 20) # irrad, wind, temp"
    ]
   },
   {
@@ -1101,16 +355,33 @@
     {
      "data": {
       "text/plain": [
-       "{'tcell': 43.509190983665746, 'tmodule': 40.809190983665744}"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77bb415250>"
       ]
      },
      "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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iqjZhV9x+Tiede6hd1p4KdWXuGC+Ky6p5Z3cYaRLeQrQZCW4hOtCVuGxOXb6No40JLz8z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\nC1EqlHwavZOLaZd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N32kuRGcT4hDAisBFqJQqtkTv4kLapQb7mZuo+ePzwbg7WvDdtXS2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\nEyGXK5ATHgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f77bb3b9150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "pvsystem.sapm_celltemp(900, 5, 20, [-3.47, -.0594, 3])"
+    "# vector inputs\n",
+    "times = pd.DatetimeIndex(start='2015-01-01', end='2015-01-02', freq='12H')\n",
+    "temps = pd.Series([0, 10, 5], index=times)\n",
+    "irrads = pd.Series([0, 500, 0], index=times)\n",
+    "winds = pd.Series([10, 5, 0], index=times)\n",
+    "\n",
+    "pvtemps = pvsystem.sapm_celltemp(irrads, winds, temps)\n",
+    "pvtemps.plot()"
    ]
   },
   {
@@ -1130,7 +401,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e220e5908>"
+       "<matplotlib.text.Text at 0x7f77bb198550>"
       ]
      },
      "execution_count": 11,
@@ -1139,384 +410,9 @@
     },
     {
      "data": {
-      "image/png": [
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glOoBHC6tSlxXoE8gdxt38O25XyEhKI79VYf56cHfsL8qF0//6GR6egQP3Z/D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\nzCU6FAbxpvrF25Xq1z8+yMHBVg72t3J0pJPpVDA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D+Ii7v/YGmikSK+qZiyxPi/lX/McI\nDpfPOczd/TngmkEeeg/wwetol0hsqWcuIiISc7oATkREJOYU5iIiIjGnMBcREYk5hbmIiEjMKcxF\nRERiTmEuIiISc/8PZSXF3neA21UAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e2209ce48>"
+       "<matplotlib.figure.Figure at 0x7f77bb2d1fd0>"
       ]
      },
      "metadata": {},
@@ -1550,7 +446,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e21fcb438>"
+       "<matplotlib.text.Text at 0x7f77bb1502d0>"
       ]
      },
      "execution_count": 12,
@@ -1559,399 +455,9 @@
     },
     {
      "data": {
-      "image/png": [
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-       "k9tZeuLOnUyWmaQzk0knd9Kdm2RuujuJJ69O77gxGBuxg4TYwew7mFPahSTQhvatVFXP/aPkmHYw\n",
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IjkgysXIxUPW2Z/7mTRr1B0A28AOl1GPmlCWEEPcewzA4dbWKrYfzaG7rZnhk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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e2201a9b0>"
+       "<matplotlib.figure.Figure at 0x7f77bb0bd190>"
       ]
      },
      "metadata": {},
@@ -1960,7 +466,7 @@
    ],
    "source": [
     "atemp = np.linspace(-20,50,71)\n",
-    "temps = pd.DataFrame(pvsystem.sapm_celltemp(900, 2, atemp), index=atemp)\n",
+    "temps = pvsystem.sapm_celltemp(900, 2, atemp).set_index(atemp)\n",
     "\n",
     "temps.plot()\n",
     "plt.legend()\n",
@@ -1985,7 +491,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e21f9d588>"
+       "<matplotlib.text.Text at 0x7f77bb0bd810>"
       ]
      },
      "execution_count": 13,
@@ -1994,394 +500,9 @@
     },
     {
      "data": {
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hsaaPLY1eCCHEkGIYBqd0I6/sK6a5vY/RkcE8sTyLaVljsFgsvi7vY1p6W3mrvIBjdacw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\n2GwcsAAYCzzq1nYPMAP4R/e9n4YzxfDPgGeAH4tIpnsp2Aj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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e21f99f28>"
+       "<matplotlib.figure.Figure at 0x7f77bb004090>"
       ]
      },
      "metadata": {},
@@ -2390,7 +511,7 @@
    ],
    "source": [
     "irrad = np.linspace(0,1000,101)\n",
-    "temps = pd.DataFrame(pvsystem.sapm_celltemp(irrad, 2, 20), index=irrad)\n",
+    "temps = pvsystem.sapm_celltemp(irrad, 2, 20).set_index(irrad)\n",
     "\n",
     "temps.plot()\n",
     "plt.legend()\n",
@@ -2415,7 +536,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e21f416a0>"
+       "<matplotlib.text.Text at 0x7f77baf1f050>"
       ]
      },
      "execution_count": 14,
@@ -2424,306 +545,9 @@
     },
     {
      "data": {
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BwCeBNUmahbwI/BEYHxGT6x6hmZmZLdLpGbek/wN+BywFnAN8GtgVOIsk4f82fY2ZmZk1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\nhTuinsTEdm8G8LikFUxkl4/Znl+xpkG0rPk3vjM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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e21f08748>"
+       "<matplotlib.figure.Figure at 0x7f77baf5d450>"
       ]
      },
      "metadata": {},
@@ -2738,16 +562,21 @@
     "          'open_rack_polymer_thinfilm_steel',\n",
     "          '22x_concentrator_tracker']\n",
     "\n",
-    "temps = pd.DataFrame(index=['tcell','tmodule'])\n",
+    "temps = pd.DataFrame(index=['temp_cell','temp_module'])\n",
     "\n",
     "for model in models:\n",
-    "    temps[model] = pd.Series(pvsystem.sapm_celltemp(1000, 5, 20, model=model))\n",
+    "    temps[model] = pd.Series(pvsystem.sapm_celltemp(1000, 5, 20, model=model).ix[0])\n",
     "\n",
     "temps.T.plot(kind='bar') # try removing the transpose operation and replotting\n",
     "plt.legend()\n",
     "plt.ylabel('temperature (deg C)')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2765,7 +594,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -2796,339 +625,339 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Vac</th>\n",
-       "      <td> 208.000000</td>\n",
-       "      <td> 240.000000</td>\n",
-       "      <td> 208.000000</td>\n",
-       "      <td> 240.000000</td>\n",
-       "      <td> 208.000000</td>\n",
-       "      <td> 240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  208.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td> 240.000000</td>\n",
-       "      <td> 208.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
-       "      <td>  240.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>2.400000e+02</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>240.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Paco</th>\n",
-       "      <td> 250.000000</td>\n",
-       "      <td> 250.000000</td>\n",
-       "      <td> 300.000000</td>\n",
-       "      <td> 300.000000</td>\n",
-       "      <td> 300.000000</td>\n",
-       "      <td> 300.000000</td>\n",
-       "      <td> 2110.000000</td>\n",
-       "      <td> 3180.000000</td>\n",
-       "      <td> 4160.000000</td>\n",
-       "      <td> 5240.000000</td>\n",
+       "      <td>250.000000</td>\n",
+       "      <td>250.000000</td>\n",
+       "      <td>300.000000</td>\n",
+       "      <td>300.000000</td>\n",
+       "      <td>300.000000</td>\n",
+       "      <td>300.000000</td>\n",
+       "      <td>2110.000000</td>\n",
+       "      <td>3180.000000</td>\n",
+       "      <td>4160.000000</td>\n",
+       "      <td>5240.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td> 4900.000000</td>\n",
-       "      <td> 4600.000000</td>\n",
-       "      <td> 5300.000000</td>\n",
-       "      <td> 225.190000</td>\n",
-       "      <td> 213.830000</td>\n",
-       "      <td> 2110.000000</td>\n",
-       "      <td> 3180.000000</td>\n",
-       "      <td> 4160.000000</td>\n",
-       "      <td> 5240.000000</td>\n",
-       "      <td> 4030.000000</td>\n",
+       "      <td>4900.000000</td>\n",
+       "      <td>4600.000000</td>\n",
+       "      <td>5300.000000</td>\n",
+       "      <td>2.251900e+02</td>\n",
+       "      <td>213.830000</td>\n",
+       "      <td>2110.000000</td>\n",
+       "      <td>3180.000000</td>\n",
+       "      <td>4160.000000</td>\n",
+       "      <td>5240.000000</td>\n",
+       "      <td>4030.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pdco</th>\n",
-       "      <td> 259.522000</td>\n",
-       "      <td> 259.553000</td>\n",
-       "      <td> 312.523000</td>\n",
-       "      <td> 312.022000</td>\n",
-       "      <td> 311.715000</td>\n",
-       "      <td> 311.505000</td>\n",
-       "      <td> 2191.830000</td>\n",
-       "      <td> 3313.680000</td>\n",
-       "      <td> 4342.410000</td>\n",
-       "      <td> 5495.830000</td>\n",
+       "      <td>259.522000</td>\n",
+       "      <td>259.553000</td>\n",
+       "      <td>312.523000</td>\n",
+       "      <td>312.022000</td>\n",
+       "      <td>311.715000</td>\n",
+       "      <td>311.505000</td>\n",
+       "      <td>2191.830000</td>\n",
+       "      <td>3313.680000</td>\n",
+       "      <td>4342.410000</td>\n",
+       "      <td>5495.830000</td>\n",
        "      <td>...</td>\n",
-       "      <td> 5135.580000</td>\n",
-       "      <td> 4829.420000</td>\n",
-       "      <td> 5571.180000</td>\n",
-       "      <td> 234.842000</td>\n",
-       "      <td> 225.563000</td>\n",
-       "      <td> 2191.830000</td>\n",
-       "      <td> 3313.680000</td>\n",
-       "      <td> 4342.410000</td>\n",
-       "      <td> 5495.830000</td>\n",
-       "      <td> 4267.480000</td>\n",
+       "      <td>5135.580000</td>\n",
+       "      <td>4829.420000</td>\n",
+       "      <td>5571.180000</td>\n",
+       "      <td>2.348420e+02</td>\n",
+       "      <td>225.563000</td>\n",
+       "      <td>2191.830000</td>\n",
+       "      <td>3313.680000</td>\n",
+       "      <td>4342.410000</td>\n",
+       "      <td>5495.830000</td>\n",
+       "      <td>4267.480000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Vdco</th>\n",
-       "      <td>  40.242600</td>\n",
-       "      <td>  39.982200</td>\n",
-       "      <td>  45.259400</td>\n",
-       "      <td>  45.495000</td>\n",
-       "      <td>  40.227100</td>\n",
-       "      <td>  40.136100</td>\n",
-       "      <td>  399.207000</td>\n",
-       "      <td>  389.513000</td>\n",
-       "      <td>  388.562000</td>\n",
-       "      <td>  386.083000</td>\n",
+       "      <td>40.242600</td>\n",
+       "      <td>39.982200</td>\n",
+       "      <td>45.259400</td>\n",
+       "      <td>45.495000</td>\n",
+       "      <td>40.227100</td>\n",
+       "      <td>40.136100</td>\n",
+       "      <td>399.207000</td>\n",
+       "      <td>389.513000</td>\n",
+       "      <td>388.562000</td>\n",
+       "      <td>386.083000</td>\n",
        "      <td>...</td>\n",
-       "      <td>  275.000000</td>\n",
-       "      <td>  275.000000</td>\n",
-       "      <td>  274.900000</td>\n",
-       "      <td>  28.468400</td>\n",
-       "      <td>  28.632600</td>\n",
-       "      <td>  399.207000</td>\n",
-       "      <td>  389.513000</td>\n",
-       "      <td>  388.562000</td>\n",
-       "      <td>  386.083000</td>\n",
-       "      <td>  302.852000</td>\n",
+       "      <td>275.000000</td>\n",
+       "      <td>275.000000</td>\n",
+       "      <td>274.900000</td>\n",
+       "      <td>2.846840e+01</td>\n",
+       "      <td>28.632600</td>\n",
+       "      <td>399.207000</td>\n",
+       "      <td>389.513000</td>\n",
+       "      <td>388.562000</td>\n",
+       "      <td>386.083000</td>\n",
+       "      <td>302.852000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pso</th>\n",
-       "      <td>   1.771610</td>\n",
-       "      <td>   1.931190</td>\n",
-       "      <td>   1.882620</td>\n",
-       "      <td>   1.928590</td>\n",
-       "      <td>   1.971050</td>\n",
-       "      <td>   1.991340</td>\n",
-       "      <td>   30.843700</td>\n",
-       "      <td>   31.265000</td>\n",
-       "      <td>   31.601700</td>\n",
-       "      <td>   32.450800</td>\n",
+       "      <td>1.771610</td>\n",
+       "      <td>1.931190</td>\n",
+       "      <td>1.882620</td>\n",
+       "      <td>1.928590</td>\n",
+       "      <td>1.971050</td>\n",
+       "      <td>1.991340</td>\n",
+       "      <td>30.843700</td>\n",
+       "      <td>31.265000</td>\n",
+       "      <td>31.601700</td>\n",
+       "      <td>32.450800</td>\n",
        "      <td>...</td>\n",
-       "      <td>   29.358900</td>\n",
-       "      <td>   26.071500</td>\n",
-       "      <td>   28.519000</td>\n",
-       "      <td>   1.646710</td>\n",
-       "      <td>   1.845030</td>\n",
-       "      <td>   30.843700</td>\n",
-       "      <td>   31.265000</td>\n",
-       "      <td>   31.601700</td>\n",
-       "      <td>   32.450800</td>\n",
-       "      <td>   37.372800</td>\n",
+       "      <td>29.358900</td>\n",
+       "      <td>26.071500</td>\n",
+       "      <td>28.519000</td>\n",
+       "      <td>1.646710e+00</td>\n",
+       "      <td>1.845030</td>\n",
+       "      <td>30.843700</td>\n",
+       "      <td>31.265000</td>\n",
+       "      <td>31.601700</td>\n",
+       "      <td>32.450800</td>\n",
+       "      <td>37.372800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C0</th>\n",
-       "      <td>  -0.000025</td>\n",
-       "      <td>  -0.000027</td>\n",
-       "      <td>  -0.000049</td>\n",
-       "      <td>  -0.000035</td>\n",
-       "      <td>  -0.000036</td>\n",
-       "      <td>  -0.000031</td>\n",
-       "      <td>   -0.000004</td>\n",
-       "      <td>   -0.000006</td>\n",
-       "      <td>   -0.000004</td>\n",
-       "      <td>   -0.000005</td>\n",
+       "      <td>-0.000025</td>\n",
+       "      <td>-0.000027</td>\n",
+       "      <td>-0.000049</td>\n",
+       "      <td>-0.000035</td>\n",
+       "      <td>-0.000036</td>\n",
+       "      <td>-0.000031</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>-0.000006</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>-0.000005</td>\n",
        "      <td>...</td>\n",
-       "      <td>   -0.000006</td>\n",
-       "      <td>   -0.000006</td>\n",
-       "      <td>   -0.000006</td>\n",
-       "      <td>  -0.000000</td>\n",
-       "      <td>  -0.000121</td>\n",
-       "      <td>   -0.000004</td>\n",
-       "      <td>   -0.000006</td>\n",
-       "      <td>   -0.000004</td>\n",
-       "      <td>   -0.000005</td>\n",
-       "      <td>   -0.000009</td>\n",
+       "      <td>-0.000006</td>\n",
+       "      <td>-0.000006</td>\n",
+       "      <td>-0.000006</td>\n",
+       "      <td>-3.859730e-07</td>\n",
+       "      <td>-0.000121</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>-0.000006</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>-0.000005</td>\n",
+       "      <td>-0.000009</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C1</th>\n",
-       "      <td>  -0.000090</td>\n",
-       "      <td>  -0.000158</td>\n",
-       "      <td>  -0.000241</td>\n",
-       "      <td>  -0.000228</td>\n",
-       "      <td>  -0.000256</td>\n",
-       "      <td>  -0.000289</td>\n",
-       "      <td>   -0.000077</td>\n",
-       "      <td>   -0.000095</td>\n",
-       "      <td>   -0.000079</td>\n",
-       "      <td>   -0.000097</td>\n",
+       "      <td>-0.000090</td>\n",
+       "      <td>-0.000158</td>\n",
+       "      <td>-0.000241</td>\n",
+       "      <td>-0.000228</td>\n",
+       "      <td>-0.000256</td>\n",
+       "      <td>-0.000289</td>\n",
+       "      <td>-0.000077</td>\n",
+       "      <td>-0.000095</td>\n",
+       "      <td>-0.000079</td>\n",
+       "      <td>-0.000097</td>\n",
        "      <td>...</td>\n",
-       "      <td>    0.000020</td>\n",
-       "      <td>    0.000024</td>\n",
-       "      <td>    0.000019</td>\n",
-       "      <td>  -0.000358</td>\n",
-       "      <td>  -0.000533</td>\n",
-       "      <td>   -0.000077</td>\n",
-       "      <td>   -0.000095</td>\n",
-       "      <td>   -0.000079</td>\n",
-       "      <td>   -0.000097</td>\n",
-       "      <td>   -0.000029</td>\n",
+       "      <td>0.000020</td>\n",
+       "      <td>0.000024</td>\n",
+       "      <td>0.000019</td>\n",
+       "      <td>-3.578480e-04</td>\n",
+       "      <td>-0.000533</td>\n",
+       "      <td>-0.000077</td>\n",
+       "      <td>-0.000095</td>\n",
+       "      <td>-0.000079</td>\n",
+       "      <td>-0.000097</td>\n",
+       "      <td>-0.000029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C2</th>\n",
-       "      <td>   0.000669</td>\n",
-       "      <td>   0.001476</td>\n",
-       "      <td>   0.000975</td>\n",
-       "      <td>  -0.000224</td>\n",
-       "      <td>  -0.000833</td>\n",
-       "      <td>  -0.002113</td>\n",
-       "      <td>    0.000502</td>\n",
-       "      <td>    0.000261</td>\n",
-       "      <td>    0.000213</td>\n",
-       "      <td>   -0.000251</td>\n",
+       "      <td>0.000669</td>\n",
+       "      <td>0.001476</td>\n",
+       "      <td>0.000975</td>\n",
+       "      <td>-0.000224</td>\n",
+       "      <td>-0.000833</td>\n",
+       "      <td>-0.002113</td>\n",
+       "      <td>0.000502</td>\n",
+       "      <td>0.000261</td>\n",
+       "      <td>0.000213</td>\n",
+       "      <td>-0.000251</td>\n",
        "      <td>...</td>\n",
-       "      <td>    0.001868</td>\n",
-       "      <td>    0.002618</td>\n",
-       "      <td>    0.001630</td>\n",
-       "      <td>  -0.013450</td>\n",
-       "      <td>   0.025867</td>\n",
-       "      <td>    0.000502</td>\n",
-       "      <td>    0.000261</td>\n",
-       "      <td>    0.000213</td>\n",
-       "      <td>   -0.000251</td>\n",
-       "      <td>    0.002147</td>\n",
+       "      <td>0.001868</td>\n",
+       "      <td>0.002618</td>\n",
+       "      <td>0.001630</td>\n",
+       "      <td>-1.345040e-02</td>\n",
+       "      <td>0.025867</td>\n",
+       "      <td>0.000502</td>\n",
+       "      <td>0.000261</td>\n",
+       "      <td>0.000213</td>\n",
+       "      <td>-0.000251</td>\n",
+       "      <td>0.002147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C3</th>\n",
-       "      <td>  -0.018881</td>\n",
-       "      <td>  -0.034601</td>\n",
-       "      <td>  -0.027563</td>\n",
-       "      <td>  -0.039636</td>\n",
-       "      <td>  -0.039102</td>\n",
-       "      <td>  -0.049515</td>\n",
-       "      <td>   -0.003258</td>\n",
-       "      <td>   -0.001956</td>\n",
-       "      <td>   -0.001867</td>\n",
-       "      <td>   -0.002337</td>\n",
+       "      <td>-0.018881</td>\n",
+       "      <td>-0.034601</td>\n",
+       "      <td>-0.027563</td>\n",
+       "      <td>-0.039636</td>\n",
+       "      <td>-0.039102</td>\n",
+       "      <td>-0.049515</td>\n",
+       "      <td>-0.003258</td>\n",
+       "      <td>-0.001956</td>\n",
+       "      <td>-0.001867</td>\n",
+       "      <td>-0.002337</td>\n",
        "      <td>...</td>\n",
-       "      <td>   -0.000276</td>\n",
-       "      <td>    0.000468</td>\n",
-       "      <td>   -0.000371</td>\n",
-       "      <td> -33.506800</td>\n",
-       "      <td>  -0.066826</td>\n",
-       "      <td>   -0.003258</td>\n",
-       "      <td>   -0.001956</td>\n",
-       "      <td>   -0.001867</td>\n",
-       "      <td>   -0.002337</td>\n",
-       "      <td>   -0.001897</td>\n",
+       "      <td>-0.000276</td>\n",
+       "      <td>0.000468</td>\n",
+       "      <td>-0.000371</td>\n",
+       "      <td>-3.350680e+01</td>\n",
+       "      <td>-0.066826</td>\n",
+       "      <td>-0.003258</td>\n",
+       "      <td>-0.001956</td>\n",
+       "      <td>-0.001867</td>\n",
+       "      <td>-0.002337</td>\n",
+       "      <td>-0.001897</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pnt</th>\n",
-       "      <td>   0.020000</td>\n",
-       "      <td>   0.050000</td>\n",
-       "      <td>   0.060000</td>\n",
-       "      <td>   0.060000</td>\n",
-       "      <td>   0.020000</td>\n",
-       "      <td>   0.050000</td>\n",
-       "      <td>    0.250000</td>\n",
-       "      <td>    0.250000</td>\n",
-       "      <td>    0.200000</td>\n",
-       "      <td>    0.200000</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.200000</td>\n",
        "      <td>...</td>\n",
-       "      <td>    0.500000</td>\n",
-       "      <td>    0.500000</td>\n",
-       "      <td>    0.500000</td>\n",
-       "      <td>   0.170000</td>\n",
-       "      <td>   0.170000</td>\n",
-       "      <td>    0.250000</td>\n",
-       "      <td>    0.250000</td>\n",
-       "      <td>    0.200000</td>\n",
-       "      <td>    0.200000</td>\n",
-       "      <td>    0.190000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.700000e-01</td>\n",
+       "      <td>0.170000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.190000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Vdcmax</th>\n",
-       "      <td>  65.000000</td>\n",
-       "      <td>  65.000000</td>\n",
-       "      <td>  79.000000</td>\n",
-       "      <td>  79.000000</td>\n",
-       "      <td>  65.000000</td>\n",
-       "      <td>  65.000000</td>\n",
-       "      <td>  500.000000</td>\n",
-       "      <td>  500.000000</td>\n",
-       "      <td>  500.000000</td>\n",
-       "      <td>  500.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>  600.000000</td>\n",
-       "      <td>  600.000000</td>\n",
-       "      <td>  600.000000</td>\n",
-       "      <td>  55.000000</td>\n",
-       "      <td>  55.000000</td>\n",
-       "      <td>  500.000000</td>\n",
-       "      <td>  500.000000</td>\n",
-       "      <td>  500.000000</td>\n",
-       "      <td>  500.000000</td>\n",
-       "      <td>  600.000000</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>5.500000e+01</td>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>600.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Idcmax</th>\n",
-       "      <td>  10.000000</td>\n",
-       "      <td>  10.000000</td>\n",
-       "      <td>  10.500000</td>\n",
-       "      <td>  10.500000</td>\n",
-       "      <td>  10.000000</td>\n",
-       "      <td>  10.000000</td>\n",
-       "      <td>   14.600000</td>\n",
-       "      <td>   22.000000</td>\n",
-       "      <td>   28.000000</td>\n",
-       "      <td>   35.300000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.500000</td>\n",
+       "      <td>10.500000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>14.600000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>35.300000</td>\n",
        "      <td>...</td>\n",
-       "      <td>   25.000000</td>\n",
-       "      <td>   25.000000</td>\n",
-       "      <td>   25.000000</td>\n",
-       "      <td>  14.000000</td>\n",
-       "      <td>  14.000000</td>\n",
-       "      <td>   14.600000</td>\n",
-       "      <td>   22.000000</td>\n",
-       "      <td>   28.000000</td>\n",
-       "      <td>   35.300000</td>\n",
-       "      <td>   20.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>1.400000e+01</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>14.600000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>35.300000</td>\n",
+       "      <td>20.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mppt_low</th>\n",
-       "      <td>  20.000000</td>\n",
-       "      <td>  20.000000</td>\n",
-       "      <td>  30.000000</td>\n",
-       "      <td>  30.000000</td>\n",
-       "      <td>  30.000000</td>\n",
-       "      <td>  30.000000</td>\n",
-       "      <td>  150.000000</td>\n",
-       "      <td>  150.000000</td>\n",
-       "      <td>  150.000000</td>\n",
-       "      <td>  150.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>  200.000000</td>\n",
-       "      <td>  200.000000</td>\n",
-       "      <td>  200.000000</td>\n",
-       "      <td>  22.000000</td>\n",
-       "      <td>  22.000000</td>\n",
-       "      <td>  150.000000</td>\n",
-       "      <td>  150.000000</td>\n",
-       "      <td>  150.000000</td>\n",
-       "      <td>  150.000000</td>\n",
-       "      <td>  240.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>2.200000e+01</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>240.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mppt_high</th>\n",
-       "      <td>  50.000000</td>\n",
-       "      <td>  50.000000</td>\n",
-       "      <td>  75.000000</td>\n",
-       "      <td>  75.000000</td>\n",
-       "      <td>  50.000000</td>\n",
-       "      <td>  50.000000</td>\n",
-       "      <td>  450.000000</td>\n",
-       "      <td>  450.000000</td>\n",
-       "      <td>  450.000000</td>\n",
-       "      <td>  450.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>450.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>  550.000000</td>\n",
-       "      <td>  550.000000</td>\n",
-       "      <td>  550.000000</td>\n",
-       "      <td>  45.000000</td>\n",
-       "      <td>  45.000000</td>\n",
-       "      <td>  450.000000</td>\n",
-       "      <td>  450.000000</td>\n",
-       "      <td>  450.000000</td>\n",
-       "      <td>  450.000000</td>\n",
-       "      <td>  480.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>4.500000e+01</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>480.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3361,20 +1190,20 @@
        "Mppt_high                                      550.000000   \n",
        "\n",
        "           Zhejiang_Yuhui_Solar_Energy_Source__Replus_250A_240V__CEC_2012_  \\\n",
-       "Vac                                               240.000000                 \n",
-       "Paco                                              225.190000                 \n",
-       "Pdco                                              234.842000                 \n",
-       "Vdco                                               28.468400                 \n",
-       "Pso                                                 1.646710                 \n",
-       "C0                                                 -0.000000                 \n",
-       "C1                                                 -0.000358                 \n",
-       "C2                                                 -0.013450                 \n",
-       "C3                                                -33.506800                 \n",
-       "Pnt                                                 0.170000                 \n",
-       "Vdcmax                                             55.000000                 \n",
-       "Idcmax                                             14.000000                 \n",
-       "Mppt_low                                           22.000000                 \n",
-       "Mppt_high                                          45.000000                 \n",
+       "Vac                                             2.400000e+02                 \n",
+       "Paco                                            2.251900e+02                 \n",
+       "Pdco                                            2.348420e+02                 \n",
+       "Vdco                                            2.846840e+01                 \n",
+       "Pso                                             1.646710e+00                 \n",
+       "C0                                             -3.859730e-07                 \n",
+       "C1                                             -3.578480e-04                 \n",
+       "C2                                             -1.345040e-02                 \n",
+       "C3                                             -3.350680e+01                 \n",
+       "Pnt                                             1.700000e-01                 \n",
+       "Vdcmax                                          5.500000e+01                 \n",
+       "Idcmax                                          1.400000e+01                 \n",
+       "Mppt_low                                        2.200000e+01                 \n",
+       "Mppt_high                                       4.500000e+01                 \n",
        "\n",
        "           Zhejiang_Yuhui_Solar_Energy_Source__Replus_250B_208V__CEC_2012_  \\\n",
        "Vac                                               208.000000                 \n",
@@ -3495,7 +1324,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e21ebd390>"
+       "<matplotlib.text.Text at 0x7f77baedab90>"
       ]
      },
      "execution_count": 16,
@@ -3504,234 +1333,9 @@
     },
     {
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rjTkzvsOUCYPcLktE2lHoi8gl27armudX7aS+qY3JYwfyxMxJ9O2T4XZZInIW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\nDsxiQVEho4Zku12WiIjEgEL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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e21f54438>"
+       "<matplotlib.figure.Figure at 0x7f77bb28cf90>"
       ]
      },
      "metadata": {},
@@ -3781,7 +1385,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -3812,411 +1416,411 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>T_noct</th>\n",
-       "      <td>         65</td>\n",
-       "      <td>         65</td>\n",
-       "      <td>       47.4</td>\n",
-       "      <td>       47.4</td>\n",
-       "      <td>       47.4</td>\n",
-       "      <td>       47.4</td>\n",
-       "      <td>      49.9</td>\n",
-       "      <td>      49.9</td>\n",
-       "      <td>      49.9</td>\n",
-       "      <td>      48.3</td>\n",
+       "      <td>65</td>\n",
+       "      <td>65</td>\n",
+       "      <td>47.4</td>\n",
+       "      <td>47.4</td>\n",
+       "      <td>47.4</td>\n",
+       "      <td>47.4</td>\n",
+       "      <td>49.9</td>\n",
+       "      <td>49.9</td>\n",
+       "      <td>49.9</td>\n",
+       "      <td>48.3</td>\n",
        "      <td>...</td>\n",
-       "      <td>        47</td>\n",
-       "      <td>         47</td>\n",
-       "      <td>        47</td>\n",
-       "      <td>        47</td>\n",
-       "      <td>        47</td>\n",
-       "      <td>        46</td>\n",
-       "      <td>        46</td>\n",
-       "      <td>      46.8</td>\n",
-       "      <td>      46.8</td>\n",
-       "      <td>      46.8</td>\n",
+       "      <td>47</td>\n",
+       "      <td>47</td>\n",
+       "      <td>47</td>\n",
+       "      <td>47</td>\n",
+       "      <td>47</td>\n",
+       "      <td>46</td>\n",
+       "      <td>46</td>\n",
+       "      <td>46.8</td>\n",
+       "      <td>46.8</td>\n",
+       "      <td>46.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A_c</th>\n",
-       "      <td>       0.67</td>\n",
-       "      <td>       0.67</td>\n",
-       "      <td>      1.567</td>\n",
-       "      <td>      1.567</td>\n",
-       "      <td>      1.567</td>\n",
-       "      <td>      1.567</td>\n",
-       "      <td>     1.635</td>\n",
-       "      <td>     1.635</td>\n",
-       "      <td>     1.635</td>\n",
-       "      <td>     1.668</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.567</td>\n",
+       "      <td>1.567</td>\n",
+       "      <td>1.567</td>\n",
+       "      <td>1.567</td>\n",
+       "      <td>1.635</td>\n",
+       "      <td>1.635</td>\n",
+       "      <td>1.635</td>\n",
+       "      <td>1.668</td>\n",
        "      <td>...</td>\n",
-       "      <td>     1.941</td>\n",
-       "      <td>      1.941</td>\n",
-       "      <td>     1.941</td>\n",
-       "      <td>     1.941</td>\n",
-       "      <td>     1.941</td>\n",
-       "      <td>     1.277</td>\n",
-       "      <td>     1.277</td>\n",
-       "      <td>     1.277</td>\n",
-       "      <td>     1.277</td>\n",
-       "      <td>     1.277</td>\n",
+       "      <td>1.941</td>\n",
+       "      <td>1.941</td>\n",
+       "      <td>1.941</td>\n",
+       "      <td>1.941</td>\n",
+       "      <td>1.941</td>\n",
+       "      <td>1.277</td>\n",
+       "      <td>1.277</td>\n",
+       "      <td>1.277</td>\n",
+       "      <td>1.277</td>\n",
+       "      <td>1.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N_s</th>\n",
-       "      <td>         18</td>\n",
-       "      <td>         18</td>\n",
-       "      <td>         60</td>\n",
-       "      <td>         60</td>\n",
-       "      <td>         60</td>\n",
-       "      <td>         60</td>\n",
-       "      <td>        60</td>\n",
-       "      <td>        60</td>\n",
-       "      <td>        60</td>\n",
-       "      <td>        60</td>\n",
+       "      <td>18</td>\n",
+       "      <td>18</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
        "      <td>...</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>         72</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>        72</td>\n",
-       "      <td>        72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>I_sc_ref</th>\n",
-       "      <td>        7.5</td>\n",
-       "      <td>        7.5</td>\n",
-       "      <td>       7.84</td>\n",
-       "      <td>       7.97</td>\n",
-       "      <td>       8.09</td>\n",
-       "      <td>       8.18</td>\n",
-       "      <td>      8.54</td>\n",
-       "      <td>      8.58</td>\n",
-       "      <td>      8.62</td>\n",
-       "      <td>      8.81</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>7.84</td>\n",
+       "      <td>7.97</td>\n",
+       "      <td>8.09</td>\n",
+       "      <td>8.18</td>\n",
+       "      <td>8.54</td>\n",
+       "      <td>8.58</td>\n",
+       "      <td>8.62</td>\n",
+       "      <td>8.81</td>\n",
        "      <td>...</td>\n",
-       "      <td>      8.91</td>\n",
-       "      <td>       8.95</td>\n",
-       "      <td>      8.99</td>\n",
-       "      <td>      9.09</td>\n",
-       "      <td>      9.19</td>\n",
-       "      <td>      4.98</td>\n",
-       "      <td>      5.36</td>\n",
-       "      <td>      5.21</td>\n",
-       "      <td>       5.3</td>\n",
-       "      <td>       5.5</td>\n",
+       "      <td>8.91</td>\n",
+       "      <td>8.95</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>9.09</td>\n",
+       "      <td>9.19</td>\n",
+       "      <td>4.98</td>\n",
+       "      <td>5.36</td>\n",
+       "      <td>5.21</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>5.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>V_oc_ref</th>\n",
-       "      <td>       10.4</td>\n",
-       "      <td>       10.4</td>\n",
-       "      <td>       36.3</td>\n",
-       "      <td>       36.6</td>\n",
-       "      <td>       36.9</td>\n",
-       "      <td>       37.1</td>\n",
-       "      <td>        37</td>\n",
-       "      <td>      37.1</td>\n",
-       "      <td>      37.2</td>\n",
-       "      <td>      38.3</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>36.3</td>\n",
+       "      <td>36.6</td>\n",
+       "      <td>36.9</td>\n",
+       "      <td>37.1</td>\n",
+       "      <td>37</td>\n",
+       "      <td>37.1</td>\n",
+       "      <td>37.2</td>\n",
+       "      <td>38.3</td>\n",
        "      <td>...</td>\n",
-       "      <td>     46.45</td>\n",
-       "      <td>      45.22</td>\n",
-       "      <td>     46.54</td>\n",
-       "      <td>     46.63</td>\n",
-       "      <td>     46.72</td>\n",
-       "      <td>     44.21</td>\n",
-       "      <td>     44.57</td>\n",
-       "      <td>     44.71</td>\n",
-       "      <td>        45</td>\n",
-       "      <td>     44.86</td>\n",
+       "      <td>46.45</td>\n",
+       "      <td>45.22</td>\n",
+       "      <td>46.54</td>\n",
+       "      <td>46.63</td>\n",
+       "      <td>46.72</td>\n",
+       "      <td>44.21</td>\n",
+       "      <td>44.57</td>\n",
+       "      <td>44.71</td>\n",
+       "      <td>45</td>\n",
+       "      <td>44.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>I_mp_ref</th>\n",
-       "      <td>        6.6</td>\n",
-       "      <td>        6.6</td>\n",
-       "      <td>       7.35</td>\n",
-       "      <td>       7.47</td>\n",
-       "      <td>       7.58</td>\n",
-       "      <td>       7.65</td>\n",
-       "      <td>      8.02</td>\n",
-       "      <td>      8.07</td>\n",
-       "      <td>       8.1</td>\n",
-       "      <td>      8.06</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.47</td>\n",
+       "      <td>7.58</td>\n",
+       "      <td>7.65</td>\n",
+       "      <td>8.02</td>\n",
+       "      <td>8.07</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>8.06</td>\n",
        "      <td>...</td>\n",
-       "      <td>      8.32</td>\n",
-       "      <td>       8.45</td>\n",
-       "      <td>      8.43</td>\n",
-       "      <td>      8.54</td>\n",
-       "      <td>      8.65</td>\n",
-       "      <td>      4.63</td>\n",
-       "      <td>       4.7</td>\n",
-       "      <td>      4.79</td>\n",
-       "      <td>      4.87</td>\n",
-       "      <td>      5.04</td>\n",
+       "      <td>8.32</td>\n",
+       "      <td>8.45</td>\n",
+       "      <td>8.43</td>\n",
+       "      <td>8.54</td>\n",
+       "      <td>8.65</td>\n",
+       "      <td>4.63</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>4.79</td>\n",
+       "      <td>4.87</td>\n",
+       "      <td>5.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>V_mp_ref</th>\n",
-       "      <td>        8.4</td>\n",
-       "      <td>        8.4</td>\n",
-       "      <td>         29</td>\n",
-       "      <td>       29.3</td>\n",
-       "      <td>       29.6</td>\n",
-       "      <td>       29.9</td>\n",
-       "      <td>      29.3</td>\n",
-       "      <td>      29.7</td>\n",
-       "      <td>      30.2</td>\n",
-       "      <td>      30.2</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>29.3</td>\n",
+       "      <td>29.6</td>\n",
+       "      <td>29.9</td>\n",
+       "      <td>29.3</td>\n",
+       "      <td>29.7</td>\n",
+       "      <td>30.2</td>\n",
+       "      <td>30.2</td>\n",
        "      <td>...</td>\n",
-       "      <td>     37.86</td>\n",
-       "      <td>      37.28</td>\n",
-       "      <td>     37.96</td>\n",
-       "      <td>     38.06</td>\n",
-       "      <td>     38.15</td>\n",
-       "      <td>     36.72</td>\n",
-       "      <td>     37.22</td>\n",
-       "      <td>     37.58</td>\n",
-       "      <td>     38.09</td>\n",
-       "      <td>     37.73</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>37.28</td>\n",
+       "      <td>37.96</td>\n",
+       "      <td>38.06</td>\n",
+       "      <td>38.15</td>\n",
+       "      <td>36.72</td>\n",
+       "      <td>37.22</td>\n",
+       "      <td>37.58</td>\n",
+       "      <td>38.09</td>\n",
+       "      <td>37.73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alpha_sc</th>\n",
-       "      <td>      0.003</td>\n",
-       "      <td>      0.003</td>\n",
-       "      <td>   0.007997</td>\n",
-       "      <td>   0.008129</td>\n",
-       "      <td>   0.008252</td>\n",
-       "      <td>   0.008344</td>\n",
-       "      <td>   0.00743</td>\n",
-       "      <td>  0.007465</td>\n",
-       "      <td>  0.007499</td>\n",
-       "      <td>  0.006167</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.007997</td>\n",
+       "      <td>0.008129</td>\n",
+       "      <td>0.008252</td>\n",
+       "      <td>0.008344</td>\n",
+       "      <td>0.00743</td>\n",
+       "      <td>0.007465</td>\n",
+       "      <td>0.007499</td>\n",
+       "      <td>0.006167</td>\n",
        "      <td>...</td>\n",
-       "      <td>   0.00499</td>\n",
-       "      <td>    0.00546</td>\n",
-       "      <td>  0.005034</td>\n",
-       "      <td>   0.00509</td>\n",
-       "      <td>  0.005146</td>\n",
-       "      <td>  0.003735</td>\n",
-       "      <td>   0.00402</td>\n",
-       "      <td>  0.005314</td>\n",
-       "      <td>  0.005406</td>\n",
-       "      <td>   0.00561</td>\n",
+       "      <td>0.00499</td>\n",
+       "      <td>0.00546</td>\n",
+       "      <td>0.005034</td>\n",
+       "      <td>0.00509</td>\n",
+       "      <td>0.005146</td>\n",
+       "      <td>0.003735</td>\n",
+       "      <td>0.00402</td>\n",
+       "      <td>0.005314</td>\n",
+       "      <td>0.005406</td>\n",
+       "      <td>0.00561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beta_oc</th>\n",
-       "      <td>      -0.04</td>\n",
-       "      <td>      -0.04</td>\n",
-       "      <td>   -0.13104</td>\n",
-       "      <td>   -0.13213</td>\n",
-       "      <td>   -0.13321</td>\n",
-       "      <td>   -0.13393</td>\n",
-       "      <td>  -0.13653</td>\n",
-       "      <td>   -0.1369</td>\n",
-       "      <td>  -0.13727</td>\n",
-       "      <td>  -0.13635</td>\n",
+       "      <td>-0.04</td>\n",
+       "      <td>-0.04</td>\n",
+       "      <td>-0.13104</td>\n",
+       "      <td>-0.13213</td>\n",
+       "      <td>-0.13321</td>\n",
+       "      <td>-0.13393</td>\n",
+       "      <td>-0.13653</td>\n",
+       "      <td>-0.1369</td>\n",
+       "      <td>-0.13727</td>\n",
+       "      <td>-0.13635</td>\n",
        "      <td>...</td>\n",
-       "      <td>  -0.14864</td>\n",
-       "      <td>   -0.16686</td>\n",
-       "      <td>  -0.14893</td>\n",
-       "      <td>  -0.14922</td>\n",
-       "      <td>   -0.1495</td>\n",
-       "      <td>  -0.16579</td>\n",
-       "      <td>  -0.16714</td>\n",
-       "      <td>  -0.16274</td>\n",
-       "      <td>   -0.1638</td>\n",
-       "      <td>  -0.16329</td>\n",
+       "      <td>-0.14864</td>\n",
+       "      <td>-0.16686</td>\n",
+       "      <td>-0.14893</td>\n",
+       "      <td>-0.14922</td>\n",
+       "      <td>-0.1495</td>\n",
+       "      <td>-0.16579</td>\n",
+       "      <td>-0.16714</td>\n",
+       "      <td>-0.16274</td>\n",
+       "      <td>-0.1638</td>\n",
+       "      <td>-0.16329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A_ref</th>\n",
-       "      <td>      0.473</td>\n",
-       "      <td>      0.473</td>\n",
-       "      <td>     1.6413</td>\n",
-       "      <td>     1.6572</td>\n",
-       "      <td>     1.6732</td>\n",
-       "      <td>     1.6888</td>\n",
-       "      <td>    1.6292</td>\n",
-       "      <td>    1.6425</td>\n",
-       "      <td>    1.6617</td>\n",
-       "      <td>    1.6351</td>\n",
+       "      <td>0.473</td>\n",
+       "      <td>0.473</td>\n",
+       "      <td>1.6413</td>\n",
+       "      <td>1.6572</td>\n",
+       "      <td>1.6732</td>\n",
+       "      <td>1.6888</td>\n",
+       "      <td>1.6292</td>\n",
+       "      <td>1.6425</td>\n",
+       "      <td>1.6617</td>\n",
+       "      <td>1.6351</td>\n",
        "      <td>...</td>\n",
-       "      <td>    1.9594</td>\n",
-       "      <td>     1.9767</td>\n",
-       "      <td>    1.9592</td>\n",
-       "      <td>    1.9616</td>\n",
-       "      <td>    1.9638</td>\n",
-       "      <td>    2.0618</td>\n",
-       "      <td>    2.1732</td>\n",
-       "      <td>    2.0685</td>\n",
-       "      <td>    2.0917</td>\n",
-       "      <td>    2.0806</td>\n",
+       "      <td>1.9594</td>\n",
+       "      <td>1.9767</td>\n",
+       "      <td>1.9592</td>\n",
+       "      <td>1.9616</td>\n",
+       "      <td>1.9638</td>\n",
+       "      <td>2.0618</td>\n",
+       "      <td>2.1732</td>\n",
+       "      <td>2.0685</td>\n",
+       "      <td>2.0917</td>\n",
+       "      <td>2.0806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>I_l_ref</th>\n",
-       "      <td>      7.545</td>\n",
-       "      <td>      7.545</td>\n",
-       "      <td>      7.843</td>\n",
-       "      <td>      7.974</td>\n",
-       "      <td>      8.094</td>\n",
-       "      <td>      8.185</td>\n",
-       "      <td>     8.543</td>\n",
-       "      <td>     8.582</td>\n",
-       "      <td>     8.623</td>\n",
-       "      <td>     8.844</td>\n",
+       "      <td>7.545</td>\n",
+       "      <td>7.545</td>\n",
+       "      <td>7.843</td>\n",
+       "      <td>7.974</td>\n",
+       "      <td>8.094</td>\n",
+       "      <td>8.185</td>\n",
+       "      <td>8.543</td>\n",
+       "      <td>8.582</td>\n",
+       "      <td>8.623</td>\n",
+       "      <td>8.844</td>\n",
        "      <td>...</td>\n",
-       "      <td>     8.921</td>\n",
-       "      <td>      8.952</td>\n",
-       "      <td>     8.998</td>\n",
-       "      <td>     9.096</td>\n",
-       "      <td>     9.195</td>\n",
-       "      <td>     4.984</td>\n",
-       "      <td>     5.372</td>\n",
-       "      <td>     5.215</td>\n",
-       "      <td>     5.303</td>\n",
-       "      <td>     5.505</td>\n",
+       "      <td>8.921</td>\n",
+       "      <td>8.952</td>\n",
+       "      <td>8.998</td>\n",
+       "      <td>9.096</td>\n",
+       "      <td>9.195</td>\n",
+       "      <td>4.984</td>\n",
+       "      <td>5.372</td>\n",
+       "      <td>5.215</td>\n",
+       "      <td>5.303</td>\n",
+       "      <td>5.505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>I_o_ref</th>\n",
-       "      <td>  1.943e-09</td>\n",
-       "      <td>  1.943e-09</td>\n",
-       "      <td>  1.936e-09</td>\n",
-       "      <td>   2.03e-09</td>\n",
-       "      <td>  2.126e-09</td>\n",
-       "      <td>  2.332e-09</td>\n",
-       "      <td> 1.166e-09</td>\n",
-       "      <td> 1.325e-09</td>\n",
-       "      <td> 1.623e-09</td>\n",
-       "      <td>   5.7e-10</td>\n",
+       "      <td>1.943e-09</td>\n",
+       "      <td>1.943e-09</td>\n",
+       "      <td>1.936e-09</td>\n",
+       "      <td>2.03e-09</td>\n",
+       "      <td>2.126e-09</td>\n",
+       "      <td>2.332e-09</td>\n",
+       "      <td>1.166e-09</td>\n",
+       "      <td>1.325e-09</td>\n",
+       "      <td>1.623e-09</td>\n",
+       "      <td>5.7e-10</td>\n",
        "      <td>...</td>\n",
-       "      <td>  4.43e-10</td>\n",
-       "      <td>   1.04e-09</td>\n",
-       "      <td>  4.28e-10</td>\n",
-       "      <td>  4.27e-10</td>\n",
-       "      <td>  4.24e-10</td>\n",
-       "      <td> 2.378e-09</td>\n",
-       "      <td> 6.055e-09</td>\n",
-       "      <td> 2.062e-09</td>\n",
-       "      <td> 2.317e-09</td>\n",
-       "      <td> 2.286e-09</td>\n",
+       "      <td>4.43e-10</td>\n",
+       "      <td>1.04e-09</td>\n",
+       "      <td>4.28e-10</td>\n",
+       "      <td>4.27e-10</td>\n",
+       "      <td>4.24e-10</td>\n",
+       "      <td>2.378e-09</td>\n",
+       "      <td>6.055e-09</td>\n",
+       "      <td>2.062e-09</td>\n",
+       "      <td>2.317e-09</td>\n",
+       "      <td>2.286e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>R_s</th>\n",
-       "      <td>      0.094</td>\n",
-       "      <td>      0.094</td>\n",
-       "      <td>      0.359</td>\n",
-       "      <td>      0.346</td>\n",
-       "      <td>      0.334</td>\n",
-       "      <td>      0.311</td>\n",
-       "      <td>     0.383</td>\n",
-       "      <td>     0.335</td>\n",
-       "      <td>     0.272</td>\n",
-       "      <td>     0.421</td>\n",
+       "      <td>0.094</td>\n",
+       "      <td>0.094</td>\n",
+       "      <td>0.359</td>\n",
+       "      <td>0.346</td>\n",
+       "      <td>0.334</td>\n",
+       "      <td>0.311</td>\n",
+       "      <td>0.383</td>\n",
+       "      <td>0.335</td>\n",
+       "      <td>0.272</td>\n",
+       "      <td>0.421</td>\n",
        "      <td>...</td>\n",
-       "      <td>     0.338</td>\n",
-       "      <td>      0.253</td>\n",
-       "      <td>     0.332</td>\n",
-       "      <td>     0.326</td>\n",
-       "      <td>     0.321</td>\n",
-       "      <td>     0.322</td>\n",
-       "      <td>     0.204</td>\n",
-       "      <td>     0.213</td>\n",
-       "      <td>     0.145</td>\n",
-       "      <td>     0.195</td>\n",
+       "      <td>0.338</td>\n",
+       "      <td>0.253</td>\n",
+       "      <td>0.332</td>\n",
+       "      <td>0.326</td>\n",
+       "      <td>0.321</td>\n",
+       "      <td>0.322</td>\n",
+       "      <td>0.204</td>\n",
+       "      <td>0.213</td>\n",
+       "      <td>0.145</td>\n",
+       "      <td>0.195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>R_sh_ref</th>\n",
-       "      <td>      15.72</td>\n",
-       "      <td>      15.72</td>\n",
-       "      <td>      839.4</td>\n",
-       "      <td>     751.03</td>\n",
-       "      <td>     670.65</td>\n",
-       "      <td>     462.56</td>\n",
-       "      <td>   1257.84</td>\n",
-       "      <td>   1463.82</td>\n",
-       "      <td>    724.06</td>\n",
-       "      <td>    109.31</td>\n",
+       "      <td>15.72</td>\n",
+       "      <td>15.72</td>\n",
+       "      <td>839.4</td>\n",
+       "      <td>751.03</td>\n",
+       "      <td>670.65</td>\n",
+       "      <td>462.56</td>\n",
+       "      <td>1257.84</td>\n",
+       "      <td>1463.82</td>\n",
+       "      <td>724.06</td>\n",
+       "      <td>109.31</td>\n",
        "      <td>...</td>\n",
-       "      <td>    285.83</td>\n",
-       "      <td>    1425.11</td>\n",
-       "      <td>    396.81</td>\n",
-       "      <td>    478.07</td>\n",
-       "      <td>    602.26</td>\n",
-       "      <td>    437.19</td>\n",
-       "      <td>     92.28</td>\n",
-       "      <td>    237.77</td>\n",
-       "      <td>    228.17</td>\n",
-       "      <td>     203.6</td>\n",
+       "      <td>285.83</td>\n",
+       "      <td>1425.11</td>\n",
+       "      <td>396.81</td>\n",
+       "      <td>478.07</td>\n",
+       "      <td>602.26</td>\n",
+       "      <td>437.19</td>\n",
+       "      <td>92.28</td>\n",
+       "      <td>237.77</td>\n",
+       "      <td>228.17</td>\n",
+       "      <td>203.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adjust</th>\n",
-       "      <td>       10.6</td>\n",
-       "      <td>       10.6</td>\n",
-       "      <td>       16.5</td>\n",
-       "      <td>       16.8</td>\n",
-       "      <td>       17.1</td>\n",
-       "      <td>       17.9</td>\n",
-       "      <td>       8.7</td>\n",
-       "      <td>       9.8</td>\n",
-       "      <td>      11.6</td>\n",
-       "      <td>     6.502</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>6.502</td>\n",
        "      <td>...</td>\n",
-       "      <td>     16.21</td>\n",
-       "      <td>      7.696</td>\n",
-       "      <td>     15.79</td>\n",
-       "      <td>     15.64</td>\n",
-       "      <td>     15.48</td>\n",
-       "      <td>      18.7</td>\n",
-       "      <td>      27.7</td>\n",
-       "      <td>      20.2</td>\n",
-       "      <td>      21.2</td>\n",
-       "      <td>      20.7</td>\n",
+       "      <td>16.21</td>\n",
+       "      <td>7.696</td>\n",
+       "      <td>15.79</td>\n",
+       "      <td>15.64</td>\n",
+       "      <td>15.48</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>27.7</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>20.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gamma_r</th>\n",
-       "      <td>       -0.5</td>\n",
-       "      <td>       -0.5</td>\n",
-       "      <td>     -0.495</td>\n",
-       "      <td>     -0.495</td>\n",
-       "      <td>     -0.495</td>\n",
-       "      <td>     -0.495</td>\n",
-       "      <td>    -0.482</td>\n",
-       "      <td>    -0.482</td>\n",
-       "      <td>    -0.482</td>\n",
-       "      <td>    -0.453</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>-0.495</td>\n",
+       "      <td>-0.495</td>\n",
+       "      <td>-0.495</td>\n",
+       "      <td>-0.495</td>\n",
+       "      <td>-0.482</td>\n",
+       "      <td>-0.482</td>\n",
+       "      <td>-0.482</td>\n",
+       "      <td>-0.453</td>\n",
        "      <td>...</td>\n",
-       "      <td>    -0.453</td>\n",
-       "      <td>     -0.476</td>\n",
-       "      <td>    -0.453</td>\n",
-       "      <td>    -0.453</td>\n",
-       "      <td>    -0.453</td>\n",
-       "      <td>    -0.517</td>\n",
-       "      <td>    -0.517</td>\n",
-       "      <td>     -0.47</td>\n",
-       "      <td>     -0.47</td>\n",
-       "      <td>     -0.47</td>\n",
+       "      <td>-0.453</td>\n",
+       "      <td>-0.476</td>\n",
+       "      <td>-0.453</td>\n",
+       "      <td>-0.453</td>\n",
+       "      <td>-0.453</td>\n",
+       "      <td>-0.517</td>\n",
+       "      <td>-0.517</td>\n",
+       "      <td>-0.47</td>\n",
+       "      <td>-0.47</td>\n",
+       "      <td>-0.47</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Source</th>\n",
-       "      <td> Multi-c-Si</td>\n",
-       "      <td> Multi-c-Si</td>\n",
-       "      <td> Multi-c-Si</td>\n",
-       "      <td> Multi-c-Si</td>\n",
-       "      <td> Multi-c-Si</td>\n",
-       "      <td> Multi-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
+       "      <td>Multi-c-Si</td>\n",
+       "      <td>Multi-c-Si</td>\n",
+       "      <td>Multi-c-Si</td>\n",
+       "      <td>Multi-c-Si</td>\n",
+       "      <td>Multi-c-Si</td>\n",
+       "      <td>Multi-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
        "      <td>...</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Multi-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
-       "      <td> Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Multi-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
+       "      <td>Mono-c-Si</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4522,7 +2126,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -4553,1011 +2157,1011 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Vintage</th>\n",
-       "      <td>                                              2006</td>\n",
-       "      <td>                                              2008</td>\n",
-       "      <td>                                              2009</td>\n",
-       "      <td>                                          2007 (E)</td>\n",
-       "      <td>                                          2007 (E)</td>\n",
-       "      <td>                                          2007 (E)</td>\n",
-       "      <td>                                          2007 (E)</td>\n",
-       "      <td>                                          2007 (E)</td>\n",
-       "      <td>                                          2007 (E)</td>\n",
-       "      <td>                                          2007 (E)</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>2007 (E)</td>\n",
+       "      <td>2007 (E)</td>\n",
+       "      <td>2007 (E)</td>\n",
+       "      <td>2007 (E)</td>\n",
+       "      <td>2007 (E)</td>\n",
+       "      <td>2007 (E)</td>\n",
+       "      <td>2007 (E)</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                          1997 (E)</td>\n",
-       "      <td>                                          2005 (E)</td>\n",
-       "      <td>                                          2003 (E)</td>\n",
-       "      <td>                                          2003 (E)</td>\n",
-       "      <td>                                          2003 (E)</td>\n",
-       "      <td>                                              2008</td>\n",
-       "      <td>                                          2008 (E)</td>\n",
-       "      <td>                                              2009</td>\n",
-       "      <td>                                          2009 (E)</td>\n",
-       "      <td>                                          2008 (E)</td>\n",
+       "      <td>1997 (E)</td>\n",
+       "      <td>2005 (E)</td>\n",
+       "      <td>2003 (E)</td>\n",
+       "      <td>2003 (E)</td>\n",
+       "      <td>2003 (E)</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>2008 (E)</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>2009 (E)</td>\n",
+       "      <td>2008 (E)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Area</th>\n",
-       "      <td>                                             1.312</td>\n",
-       "      <td>                                             1.646</td>\n",
-       "      <td>                                             1.646</td>\n",
-       "      <td>                                              1.28</td>\n",
-       "      <td>                                              1.28</td>\n",
-       "      <td>                                             1.378</td>\n",
-       "      <td>                                             1.378</td>\n",
-       "      <td>                                             1.378</td>\n",
-       "      <td>                                             1.378</td>\n",
-       "      <td>                                             1.378</td>\n",
+       "      <td>1.312</td>\n",
+       "      <td>1.646</td>\n",
+       "      <td>1.646</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>1.378</td>\n",
+       "      <td>1.378</td>\n",
+       "      <td>1.378</td>\n",
+       "      <td>1.378</td>\n",
+       "      <td>1.378</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                             1.012</td>\n",
-       "      <td>                                             1.012</td>\n",
-       "      <td>                                             0.235</td>\n",
-       "      <td>                                             0.606</td>\n",
-       "      <td>                                             0.136</td>\n",
-       "      <td>                                               1.7</td>\n",
-       "      <td>                                               1.7</td>\n",
-       "      <td>                                             1.634</td>\n",
-       "      <td>                                             1.634</td>\n",
-       "      <td>                                               1.7</td>\n",
+       "      <td>1.012</td>\n",
+       "      <td>1.012</td>\n",
+       "      <td>0.235</td>\n",
+       "      <td>0.606</td>\n",
+       "      <td>0.136</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>1.634</td>\n",
+       "      <td>1.634</td>\n",
+       "      <td>1.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Material</th>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                              c-Si</td>\n",
-       "      <td>                                              c-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>c-Si</td>\n",
+       "      <td>c-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            3-a-Si</td>\n",
-       "      <td>                                            3-a-Si</td>\n",
-       "      <td>                                            3-a-Si</td>\n",
-       "      <td>                                            3-a-Si</td>\n",
-       "      <td>                                            3-a-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
-       "      <td>                                             mc-Si</td>\n",
+       "      <td>3-a-Si</td>\n",
+       "      <td>3-a-Si</td>\n",
+       "      <td>3-a-Si</td>\n",
+       "      <td>3-a-Si</td>\n",
+       "      <td>3-a-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
+       "      <td>mc-Si</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>#Series</th>\n",
-       "      <td>                                                72</td>\n",
-       "      <td>                                                60</td>\n",
-       "      <td>                                                60</td>\n",
-       "      <td>                                                72</td>\n",
-       "      <td>                                                72</td>\n",
-       "      <td>                                                50</td>\n",
-       "      <td>                                                50</td>\n",
-       "      <td>                                                50</td>\n",
-       "      <td>                                                50</td>\n",
-       "      <td>                                                50</td>\n",
+       "      <td>72</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                                11</td>\n",
-       "      <td>                                                11</td>\n",
-       "      <td>                                                11</td>\n",
-       "      <td>                                                11</td>\n",
-       "      <td>                                                11</td>\n",
-       "      <td>                                                60</td>\n",
-       "      <td>                                                60</td>\n",
-       "      <td>                                                60</td>\n",
-       "      <td>                                                60</td>\n",
-       "      <td>                                                60</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>#Parallel</th>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                                 2</td>\n",
-       "      <td>                                                 2</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Isco</th>\n",
-       "      <td>                                             5.564</td>\n",
-       "      <td>                                              8.34</td>\n",
-       "      <td>                                              8.49</td>\n",
-       "      <td>                                               5.1</td>\n",
-       "      <td>                                               5.2</td>\n",
-       "      <td>                                               7.9</td>\n",
-       "      <td>                                              7.95</td>\n",
-       "      <td>                                               8.1</td>\n",
-       "      <td>                                              8.15</td>\n",
-       "      <td>                                               8.2</td>\n",
+       "      <td>5.564</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.49</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>8.15</td>\n",
+       "      <td>8.2</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                               5.1</td>\n",
-       "      <td>                                               4.8</td>\n",
-       "      <td>                                              0.78</td>\n",
-       "      <td>                                               2.4</td>\n",
-       "      <td>                                              0.37</td>\n",
-       "      <td>                                             8.163</td>\n",
-       "      <td>                                               8.1</td>\n",
-       "      <td>                                             8.222</td>\n",
-       "      <td>                                               8.4</td>\n",
-       "      <td>                                               7.8</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>8.163</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>8.222</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>7.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Voco</th>\n",
-       "      <td>                                            42.832</td>\n",
-       "      <td>                                             35.31</td>\n",
-       "      <td>                                             35.92</td>\n",
-       "      <td>                                              43.5</td>\n",
-       "      <td>                                              43.6</td>\n",
-       "      <td>                                                30</td>\n",
-       "      <td>                                              30.1</td>\n",
-       "      <td>                                              30.2</td>\n",
-       "      <td>                                              30.3</td>\n",
-       "      <td>                                              30.5</td>\n",
+       "      <td>42.832</td>\n",
+       "      <td>35.31</td>\n",
+       "      <td>35.92</td>\n",
+       "      <td>43.5</td>\n",
+       "      <td>43.6</td>\n",
+       "      <td>30</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>30.2</td>\n",
+       "      <td>30.3</td>\n",
+       "      <td>30.5</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                              21.3</td>\n",
-       "      <td>                                              23.8</td>\n",
-       "      <td>                                              23.8</td>\n",
-       "      <td>                                              23.8</td>\n",
-       "      <td>                                              23.8</td>\n",
-       "      <td>                                            36.871</td>\n",
-       "      <td>                                              36.6</td>\n",
-       "      <td>                                             37.28</td>\n",
-       "      <td>                                                37</td>\n",
-       "      <td>                                                36</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>36.871</td>\n",
+       "      <td>36.6</td>\n",
+       "      <td>37.28</td>\n",
+       "      <td>37</td>\n",
+       "      <td>36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Impo</th>\n",
-       "      <td>                                             5.028</td>\n",
-       "      <td>                                              7.49</td>\n",
-       "      <td>                                              7.74</td>\n",
-       "      <td>                                              4.55</td>\n",
-       "      <td>                                              4.65</td>\n",
-       "      <td>                                              7.08</td>\n",
-       "      <td>                                              7.23</td>\n",
-       "      <td>                                              7.38</td>\n",
-       "      <td>                                              7.53</td>\n",
-       "      <td>                                              7.67</td>\n",
+       "      <td>5.028</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>7.74</td>\n",
+       "      <td>4.55</td>\n",
+       "      <td>4.65</td>\n",
+       "      <td>7.08</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>7.38</td>\n",
+       "      <td>7.53</td>\n",
+       "      <td>7.67</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                               4.1</td>\n",
-       "      <td>                                              3.88</td>\n",
-       "      <td>                                              0.62</td>\n",
-       "      <td>                                              1.94</td>\n",
-       "      <td>                                               0.3</td>\n",
-       "      <td>                                             7.388</td>\n",
-       "      <td>                                               7.4</td>\n",
-       "      <td>                                             7.727</td>\n",
-       "      <td>                                               7.8</td>\n",
-       "      <td>                                               7.2</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>3.88</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>1.94</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>7.388</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>7.727</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>7.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Vmpo</th>\n",
-       "      <td>                                             32.41</td>\n",
-       "      <td>                                             27.61</td>\n",
-       "      <td>                                             27.92</td>\n",
-       "      <td>                                              35.6</td>\n",
-       "      <td>                                              35.8</td>\n",
-       "      <td>                                              23.3</td>\n",
-       "      <td>                                              23.5</td>\n",
-       "      <td>                                              23.7</td>\n",
-       "      <td>                                              23.9</td>\n",
-       "      <td>                                              24.1</td>\n",
+       "      <td>32.41</td>\n",
+       "      <td>27.61</td>\n",
+       "      <td>27.92</td>\n",
+       "      <td>35.6</td>\n",
+       "      <td>35.8</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>23.7</td>\n",
+       "      <td>23.9</td>\n",
+       "      <td>24.1</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                              15.6</td>\n",
-       "      <td>                                              16.5</td>\n",
-       "      <td>                                              16.5</td>\n",
-       "      <td>                                              16.5</td>\n",
-       "      <td>                                              16.5</td>\n",
-       "      <td>                                            28.633</td>\n",
-       "      <td>                                                30</td>\n",
-       "      <td>                                            29.886</td>\n",
-       "      <td>                                              29.5</td>\n",
-       "      <td>                                              29.5</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>28.633</td>\n",
+       "      <td>30</td>\n",
+       "      <td>29.886</td>\n",
+       "      <td>29.5</td>\n",
+       "      <td>29.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aisc</th>\n",
-       "      <td>                                          0.000537</td>\n",
-       "      <td>                                           0.00077</td>\n",
-       "      <td>                                           0.00082</td>\n",
-       "      <td>                                            0.0003</td>\n",
-       "      <td>                                            0.0003</td>\n",
-       "      <td>                                            0.0008</td>\n",
-       "      <td>                                            0.0008</td>\n",
-       "      <td>                                            0.0008</td>\n",
-       "      <td>                                            0.0008</td>\n",
-       "      <td>                                            0.0008</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.0003</td>\n",
+       "      <td>0.0003</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0008</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                          0.000436</td>\n",
-       "      <td>                                          0.000436</td>\n",
-       "      <td>                                          0.000746</td>\n",
-       "      <td>                                          0.000746</td>\n",
-       "      <td>                                             0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.000436</td>\n",
+       "      <td>0.000436</td>\n",
+       "      <td>0.000746</td>\n",
+       "      <td>0.000746</td>\n",
+       "      <td>0.001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aimp</th>\n",
-       "      <td>                                         -0.000491</td>\n",
-       "      <td>                                          -0.00015</td>\n",
-       "      <td>                                          -0.00013</td>\n",
-       "      <td>                                          -0.00025</td>\n",
-       "      <td>                                          -0.00025</td>\n",
-       "      <td>                                           -0.0003</td>\n",
-       "      <td>                                           -0.0003</td>\n",
-       "      <td>                                           -0.0003</td>\n",
-       "      <td>                                           -0.0003</td>\n",
-       "      <td>                                           -0.0003</td>\n",
+       "      <td>-0.000491</td>\n",
+       "      <td>-0.00015</td>\n",
+       "      <td>-0.00013</td>\n",
+       "      <td>-0.00025</td>\n",
+       "      <td>-0.00025</td>\n",
+       "      <td>-0.0003</td>\n",
+       "      <td>-0.0003</td>\n",
+       "      <td>-0.0003</td>\n",
+       "      <td>-0.0003</td>\n",
+       "      <td>-0.0003</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                             0.001</td>\n",
-       "      <td>                                         -0.000176</td>\n",
-       "      <td>                                         -0.000176</td>\n",
-       "      <td>                                          0.000176</td>\n",
-       "      <td>                                          0.000176</td>\n",
-       "      <td>                                           -0.0002</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>-0.000176</td>\n",
+       "      <td>-0.000176</td>\n",
+       "      <td>0.000176</td>\n",
+       "      <td>0.000176</td>\n",
+       "      <td>-0.0002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C0</th>\n",
-       "      <td>                                            1.0233</td>\n",
-       "      <td>                                             0.937</td>\n",
-       "      <td>                                             1.015</td>\n",
-       "      <td>                                              0.99</td>\n",
-       "      <td>                                              0.99</td>\n",
-       "      <td>                                              0.99</td>\n",
-       "      <td>                                              0.99</td>\n",
-       "      <td>                                              0.99</td>\n",
-       "      <td>                                              0.99</td>\n",
-       "      <td>                                              0.99</td>\n",
+       "      <td>1.0233</td>\n",
+       "      <td>0.937</td>\n",
+       "      <td>1.015</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                             1.121</td>\n",
-       "      <td>                                             1.121</td>\n",
-       "      <td>                                             1.096</td>\n",
-       "      <td>                                             1.096</td>\n",
-       "      <td>                                             1.096</td>\n",
-       "      <td>                                            0.9877</td>\n",
-       "      <td>                                            0.9877</td>\n",
-       "      <td>                                            0.9993</td>\n",
-       "      <td>                                            0.9993</td>\n",
-       "      <td>                                             0.977</td>\n",
+       "      <td>1.121</td>\n",
+       "      <td>1.121</td>\n",
+       "      <td>1.096</td>\n",
+       "      <td>1.096</td>\n",
+       "      <td>1.096</td>\n",
+       "      <td>0.9877</td>\n",
+       "      <td>0.9877</td>\n",
+       "      <td>0.9993</td>\n",
+       "      <td>0.9993</td>\n",
+       "      <td>0.977</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C1</th>\n",
-       "      <td>                                           -0.0233</td>\n",
-       "      <td>                                             0.063</td>\n",
-       "      <td>                                            -0.015</td>\n",
-       "      <td>                                              0.01</td>\n",
-       "      <td>                                              0.01</td>\n",
-       "      <td>                                              0.01</td>\n",
-       "      <td>                                              0.01</td>\n",
-       "      <td>                                              0.01</td>\n",
-       "      <td>                                              0.01</td>\n",
-       "      <td>                                              0.01</td>\n",
+       "      <td>-0.0233</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>-0.015</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            -0.121</td>\n",
-       "      <td>                                            -0.121</td>\n",
-       "      <td>                                            -0.096</td>\n",
-       "      <td>                                            -0.096</td>\n",
-       "      <td>                                            -0.096</td>\n",
-       "      <td>                                            0.0123</td>\n",
-       "      <td>                                            0.0123</td>\n",
-       "      <td>                                            0.0007</td>\n",
-       "      <td>                                            0.0007</td>\n",
-       "      <td>                                             0.023</td>\n",
+       "      <td>-0.121</td>\n",
+       "      <td>-0.121</td>\n",
+       "      <td>-0.096</td>\n",
+       "      <td>-0.096</td>\n",
+       "      <td>-0.096</td>\n",
+       "      <td>0.0123</td>\n",
+       "      <td>0.0123</td>\n",
+       "      <td>0.0007</td>\n",
+       "      <td>0.0007</td>\n",
+       "      <td>0.023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bvoco</th>\n",
-       "      <td>                                           -0.1703</td>\n",
-       "      <td>                                            -0.133</td>\n",
-       "      <td>                                            -0.135</td>\n",
-       "      <td>                                            -0.152</td>\n",
-       "      <td>                                            -0.152</td>\n",
-       "      <td>                                             -0.11</td>\n",
-       "      <td>                                             -0.11</td>\n",
-       "      <td>                                             -0.11</td>\n",
-       "      <td>                                             -0.11</td>\n",
-       "      <td>                                             -0.11</td>\n",
+       "      <td>-0.1703</td>\n",
+       "      <td>-0.133</td>\n",
+       "      <td>-0.135</td>\n",
+       "      <td>-0.152</td>\n",
+       "      <td>-0.152</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>-0.11</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            -0.088</td>\n",
-       "      <td>                                            -0.088</td>\n",
-       "      <td>                                            -0.088</td>\n",
-       "      <td>                                            -0.088</td>\n",
-       "      <td>                                            -0.088</td>\n",
-       "      <td>                                           -0.1378</td>\n",
-       "      <td>                                           -0.1378</td>\n",
-       "      <td>                                           -0.1294</td>\n",
-       "      <td>                                           -0.1294</td>\n",
-       "      <td>                                            -0.133</td>\n",
+       "      <td>-0.088</td>\n",
+       "      <td>-0.088</td>\n",
+       "      <td>-0.088</td>\n",
+       "      <td>-0.088</td>\n",
+       "      <td>-0.088</td>\n",
+       "      <td>-0.1378</td>\n",
+       "      <td>-0.1378</td>\n",
+       "      <td>-0.1294</td>\n",
+       "      <td>-0.1294</td>\n",
+       "      <td>-0.133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mbvoc</th>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bvmpo</th>\n",
-       "      <td>                                           -0.1731</td>\n",
-       "      <td>                                            -0.135</td>\n",
-       "      <td>                                            -0.136</td>\n",
-       "      <td>                                            -0.158</td>\n",
-       "      <td>                                            -0.158</td>\n",
-       "      <td>                                            -0.115</td>\n",
-       "      <td>                                            -0.115</td>\n",
-       "      <td>                                            -0.115</td>\n",
-       "      <td>                                            -0.115</td>\n",
-       "      <td>                                            -0.115</td>\n",
+       "      <td>-0.1731</td>\n",
+       "      <td>-0.135</td>\n",
+       "      <td>-0.136</td>\n",
+       "      <td>-0.158</td>\n",
+       "      <td>-0.158</td>\n",
+       "      <td>-0.115</td>\n",
+       "      <td>-0.115</td>\n",
+       "      <td>-0.115</td>\n",
+       "      <td>-0.115</td>\n",
+       "      <td>-0.115</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            -0.051</td>\n",
-       "      <td>                                            -0.051</td>\n",
-       "      <td>                                            -0.051</td>\n",
-       "      <td>                                            -0.051</td>\n",
-       "      <td>                                            -0.051</td>\n",
-       "      <td>                                           -0.1421</td>\n",
-       "      <td>                                           -0.1421</td>\n",
-       "      <td>                                            -0.137</td>\n",
-       "      <td>                                            -0.137</td>\n",
-       "      <td>                                            -0.133</td>\n",
+       "      <td>-0.051</td>\n",
+       "      <td>-0.051</td>\n",
+       "      <td>-0.051</td>\n",
+       "      <td>-0.051</td>\n",
+       "      <td>-0.051</td>\n",
+       "      <td>-0.1421</td>\n",
+       "      <td>-0.1421</td>\n",
+       "      <td>-0.137</td>\n",
+       "      <td>-0.137</td>\n",
+       "      <td>-0.133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mbvmp</th>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
-       "      <td>                                                 0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N</th>\n",
-       "      <td>                                             1.174</td>\n",
-       "      <td>                                             1.495</td>\n",
-       "      <td>                                             1.373</td>\n",
-       "      <td>                                              1.25</td>\n",
-       "      <td>                                              1.25</td>\n",
-       "      <td>                                              1.35</td>\n",
-       "      <td>                                              1.35</td>\n",
-       "      <td>                                              1.35</td>\n",
-       "      <td>                                              1.35</td>\n",
-       "      <td>                                              1.35</td>\n",
+       "      <td>1.174</td>\n",
+       "      <td>1.495</td>\n",
+       "      <td>1.373</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>1.35</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                              3.51</td>\n",
-       "      <td>                                              3.51</td>\n",
-       "      <td>                                              3.77</td>\n",
-       "      <td>                                              3.77</td>\n",
-       "      <td>                                              3.77</td>\n",
-       "      <td>                                             1.442</td>\n",
-       "      <td>                                             1.442</td>\n",
-       "      <td>                                             1.263</td>\n",
-       "      <td>                                             1.263</td>\n",
-       "      <td>                                             1.384</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>3.77</td>\n",
+       "      <td>3.77</td>\n",
+       "      <td>3.77</td>\n",
+       "      <td>1.442</td>\n",
+       "      <td>1.442</td>\n",
+       "      <td>1.263</td>\n",
+       "      <td>1.263</td>\n",
+       "      <td>1.384</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C2</th>\n",
-       "      <td>                                          -0.76444</td>\n",
-       "      <td>                                            0.0182</td>\n",
-       "      <td>                                            0.0036</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.12</td>\n",
-       "      <td>                                             -0.12</td>\n",
-       "      <td>                                             -0.12</td>\n",
-       "      <td>                                             -0.12</td>\n",
-       "      <td>                                             -0.12</td>\n",
+       "      <td>-0.76444</td>\n",
+       "      <td>0.0182</td>\n",
+       "      <td>0.0036</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-0.12</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                          -1.24058</td>\n",
-       "      <td>                                          -1.24058</td>\n",
-       "      <td>                                          -1.14162</td>\n",
-       "      <td>                                          -1.14162</td>\n",
-       "      <td>                                          -1.14162</td>\n",
-       "      <td>                                         -0.050321</td>\n",
-       "      <td>                                         -0.050321</td>\n",
-       "      <td>                                         -0.058706</td>\n",
-       "      <td>                                         -0.058706</td>\n",
-       "      <td>                                           -0.3872</td>\n",
+       "      <td>-1.24058</td>\n",
+       "      <td>-1.24058</td>\n",
+       "      <td>-1.14162</td>\n",
+       "      <td>-1.14162</td>\n",
+       "      <td>-1.14162</td>\n",
+       "      <td>-0.050321</td>\n",
+       "      <td>-0.050321</td>\n",
+       "      <td>-0.058706</td>\n",
+       "      <td>-0.058706</td>\n",
+       "      <td>-0.3872</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C3</th>\n",
-       "      <td>                                          -15.5087</td>\n",
-       "      <td>                                           -10.758</td>\n",
-       "      <td>                                           -7.2509</td>\n",
-       "      <td>                                             -8.96</td>\n",
-       "      <td>                                             -8.96</td>\n",
-       "      <td>                                            -11.08</td>\n",
-       "      <td>                                            -11.08</td>\n",
-       "      <td>                                            -11.08</td>\n",
-       "      <td>                                            -11.08</td>\n",
-       "      <td>                                            -11.08</td>\n",
+       "      <td>-15.5087</td>\n",
+       "      <td>-10.758</td>\n",
+       "      <td>-7.2509</td>\n",
+       "      <td>-8.96</td>\n",
+       "      <td>-8.96</td>\n",
+       "      <td>-11.08</td>\n",
+       "      <td>-11.08</td>\n",
+       "      <td>-11.08</td>\n",
+       "      <td>-11.08</td>\n",
+       "      <td>-11.08</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                          -3.91126</td>\n",
-       "      <td>                                          -3.91126</td>\n",
-       "      <td>                                          -2.89115</td>\n",
-       "      <td>                                          -2.89115</td>\n",
-       "      <td>                                          -2.89115</td>\n",
-       "      <td>                                          -8.81507</td>\n",
-       "      <td>                                          -8.81507</td>\n",
-       "      <td>                                          -8.35334</td>\n",
-       "      <td>                                          -8.35334</td>\n",
-       "      <td>                                           -9.5542</td>\n",
+       "      <td>-3.91126</td>\n",
+       "      <td>-3.91126</td>\n",
+       "      <td>-2.89115</td>\n",
+       "      <td>-2.89115</td>\n",
+       "      <td>-2.89115</td>\n",
+       "      <td>-8.81507</td>\n",
+       "      <td>-8.81507</td>\n",
+       "      <td>-8.35334</td>\n",
+       "      <td>-8.35334</td>\n",
+       "      <td>-9.5542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A0</th>\n",
-       "      <td>                                            0.9281</td>\n",
-       "      <td>                                            0.9067</td>\n",
-       "      <td>                                            0.9323</td>\n",
-       "      <td>                                             0.938</td>\n",
-       "      <td>                                             0.938</td>\n",
-       "      <td>                                             0.924</td>\n",
-       "      <td>                                             0.924</td>\n",
-       "      <td>                                             0.924</td>\n",
-       "      <td>                                             0.924</td>\n",
-       "      <td>                                             0.924</td>\n",
+       "      <td>0.9281</td>\n",
+       "      <td>0.9067</td>\n",
+       "      <td>0.9323</td>\n",
+       "      <td>0.938</td>\n",
+       "      <td>0.938</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>0.924</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                             1.047</td>\n",
-       "      <td>                                             1.047</td>\n",
-       "      <td>                                             1.047</td>\n",
-       "      <td>                                             1.047</td>\n",
-       "      <td>                                             1.047</td>\n",
-       "      <td>                                            0.9407</td>\n",
-       "      <td>                                            0.9219</td>\n",
-       "      <td>                                            0.9011</td>\n",
-       "      <td>                                            0.9219</td>\n",
-       "      <td>                                            0.9219</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>1.047</td>\n",
+       "      <td>0.9407</td>\n",
+       "      <td>0.9219</td>\n",
+       "      <td>0.9011</td>\n",
+       "      <td>0.9219</td>\n",
+       "      <td>0.9219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A1</th>\n",
-       "      <td>                                           0.06615</td>\n",
-       "      <td>                                           0.09573</td>\n",
-       "      <td>                                           0.06526</td>\n",
-       "      <td>                                           0.05422</td>\n",
-       "      <td>                                           0.05422</td>\n",
-       "      <td>                                           0.06749</td>\n",
-       "      <td>                                           0.06749</td>\n",
-       "      <td>                                           0.06749</td>\n",
-       "      <td>                                           0.06749</td>\n",
-       "      <td>                                           0.06749</td>\n",
+       "      <td>0.06615</td>\n",
+       "      <td>0.09573</td>\n",
+       "      <td>0.06526</td>\n",
+       "      <td>0.05422</td>\n",
+       "      <td>0.05422</td>\n",
+       "      <td>0.06749</td>\n",
+       "      <td>0.06749</td>\n",
+       "      <td>0.06749</td>\n",
+       "      <td>0.06749</td>\n",
+       "      <td>0.06749</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                        0.00082115</td>\n",
-       "      <td>                                        0.00082115</td>\n",
-       "      <td>                                        0.00082115</td>\n",
-       "      <td>                                        0.00082115</td>\n",
-       "      <td>                                        0.00082115</td>\n",
-       "      <td>                                           0.05086</td>\n",
-       "      <td>                                            0.0709</td>\n",
-       "      <td>                                            0.1021</td>\n",
-       "      <td>                                            0.0709</td>\n",
-       "      <td>                                           0.07089</td>\n",
+       "      <td>0.00082115</td>\n",
+       "      <td>0.00082115</td>\n",
+       "      <td>0.00082115</td>\n",
+       "      <td>0.00082115</td>\n",
+       "      <td>0.00082115</td>\n",
+       "      <td>0.05086</td>\n",
+       "      <td>0.0709</td>\n",
+       "      <td>0.1021</td>\n",
+       "      <td>0.0709</td>\n",
+       "      <td>0.07089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A2</th>\n",
-       "      <td>                                          -0.01384</td>\n",
-       "      <td>                                           -0.0266</td>\n",
-       "      <td>                                          -0.01567</td>\n",
-       "      <td>                                         -0.009903</td>\n",
-       "      <td>                                         -0.009903</td>\n",
-       "      <td>                                         -0.012549</td>\n",
-       "      <td>                                         -0.012549</td>\n",
-       "      <td>                                         -0.012549</td>\n",
-       "      <td>                                         -0.012549</td>\n",
-       "      <td>                                         -0.012549</td>\n",
+       "      <td>-0.01384</td>\n",
+       "      <td>-0.0266</td>\n",
+       "      <td>-0.01567</td>\n",
+       "      <td>-0.009903</td>\n",
+       "      <td>-0.009903</td>\n",
+       "      <td>-0.012549</td>\n",
+       "      <td>-0.012549</td>\n",
+       "      <td>-0.012549</td>\n",
+       "      <td>-0.012549</td>\n",
+       "      <td>-0.012549</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                           -0.0259</td>\n",
-       "      <td>                                           -0.0259</td>\n",
-       "      <td>                                           -0.0259</td>\n",
-       "      <td>                                           -0.0259</td>\n",
-       "      <td>                                           -0.0259</td>\n",
-       "      <td>                                         -0.008095</td>\n",
-       "      <td>                                           -0.0143</td>\n",
-       "      <td>                                          -0.02942</td>\n",
-       "      <td>                                           -0.0143</td>\n",
-       "      <td>                                          -0.01427</td>\n",
+       "      <td>-0.0259</td>\n",
+       "      <td>-0.0259</td>\n",
+       "      <td>-0.0259</td>\n",
+       "      <td>-0.0259</td>\n",
+       "      <td>-0.0259</td>\n",
+       "      <td>-0.008095</td>\n",
+       "      <td>-0.0143</td>\n",
+       "      <td>-0.02942</td>\n",
+       "      <td>-0.0143</td>\n",
+       "      <td>-0.01427</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A3</th>\n",
-       "      <td>                                          0.001298</td>\n",
-       "      <td>                                           0.00343</td>\n",
-       "      <td>                                           0.00193</td>\n",
-       "      <td>                                         0.0007297</td>\n",
-       "      <td>                                         0.0007297</td>\n",
-       "      <td>                                         0.0010049</td>\n",
-       "      <td>                                         0.0010049</td>\n",
-       "      <td>                                         0.0010049</td>\n",
-       "      <td>                                         0.0010049</td>\n",
-       "      <td>                                         0.0010049</td>\n",
+       "      <td>0.001298</td>\n",
+       "      <td>0.00343</td>\n",
+       "      <td>0.00193</td>\n",
+       "      <td>0.0007297</td>\n",
+       "      <td>0.0007297</td>\n",
+       "      <td>0.0010049</td>\n",
+       "      <td>0.0010049</td>\n",
+       "      <td>0.0010049</td>\n",
+       "      <td>0.0010049</td>\n",
+       "      <td>0.0010049</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                         0.0031736</td>\n",
-       "      <td>                                         0.0031736</td>\n",
-       "      <td>                                         0.0031736</td>\n",
-       "      <td>                                         0.0031736</td>\n",
-       "      <td>                                         0.0031736</td>\n",
-       "      <td>                                         0.0003498</td>\n",
-       "      <td>                                            0.0012</td>\n",
-       "      <td>                                           0.00397</td>\n",
-       "      <td>                                            0.0012</td>\n",
-       "      <td>                                           0.00117</td>\n",
+       "      <td>0.0031736</td>\n",
+       "      <td>0.0031736</td>\n",
+       "      <td>0.0031736</td>\n",
+       "      <td>0.0031736</td>\n",
+       "      <td>0.0031736</td>\n",
+       "      <td>0.0003498</td>\n",
+       "      <td>0.0012</td>\n",
+       "      <td>0.00397</td>\n",
+       "      <td>0.0012</td>\n",
+       "      <td>0.00117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A4</th>\n",
-       "      <td>                                          -4.6e-05</td>\n",
-       "      <td>                                        -0.0001794</td>\n",
-       "      <td>                                         -9.81e-05</td>\n",
-       "      <td>                                        -1.907e-05</td>\n",
-       "      <td>                                        -1.907e-05</td>\n",
-       "      <td>                                       -2.8797e-05</td>\n",
-       "      <td>                                       -2.8797e-05</td>\n",
-       "      <td>                                       -2.8797e-05</td>\n",
-       "      <td>                                       -2.8797e-05</td>\n",
-       "      <td>                                       -2.8797e-05</td>\n",
+       "      <td>-4.6e-05</td>\n",
+       "      <td>-0.0001794</td>\n",
+       "      <td>-9.81e-05</td>\n",
+       "      <td>-1.907e-05</td>\n",
+       "      <td>-1.907e-05</td>\n",
+       "      <td>-2.8797e-05</td>\n",
+       "      <td>-2.8797e-05</td>\n",
+       "      <td>-2.8797e-05</td>\n",
+       "      <td>-2.8797e-05</td>\n",
+       "      <td>-2.8797e-05</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                       -0.00011026</td>\n",
-       "      <td>                                       -0.00011026</td>\n",
-       "      <td>                                       -0.00011026</td>\n",
-       "      <td>                                       -0.00011026</td>\n",
-       "      <td>                                       -0.00011026</td>\n",
-       "      <td>                                          8.85e-06</td>\n",
-       "      <td>                                            -3e-05</td>\n",
-       "      <td>                                        -0.0002105</td>\n",
-       "      <td>                                            -3e-05</td>\n",
-       "      <td>                                         -3.37e-05</td>\n",
+       "      <td>-0.00011026</td>\n",
+       "      <td>-0.00011026</td>\n",
+       "      <td>-0.00011026</td>\n",
+       "      <td>-0.00011026</td>\n",
+       "      <td>-0.00011026</td>\n",
+       "      <td>8.85e-06</td>\n",
+       "      <td>-3e-05</td>\n",
+       "      <td>-0.0002105</td>\n",
+       "      <td>-3e-05</td>\n",
+       "      <td>-3.37e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B0</th>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B1</th>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                          -0.00502</td>\n",
-       "      <td>                                          -0.00502</td>\n",
-       "      <td>                                          -0.00502</td>\n",
-       "      <td>                                          -0.00502</td>\n",
-       "      <td>                                          -0.00502</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
-       "      <td>                                         -0.002438</td>\n",
+       "      <td>-0.00502</td>\n",
+       "      <td>-0.00502</td>\n",
+       "      <td>-0.00502</td>\n",
+       "      <td>-0.00502</td>\n",
+       "      <td>-0.00502</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
+       "      <td>-0.002438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B2</th>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                           0.00031</td>\n",
-       "      <td>                                           0.00031</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.00031</td>\n",
+       "      <td>0.00031</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                         0.0005842</td>\n",
-       "      <td>                                         0.0005842</td>\n",
-       "      <td>                                         0.0005842</td>\n",
-       "      <td>                                         0.0005842</td>\n",
-       "      <td>                                         0.0005842</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                         0.0003103</td>\n",
-       "      <td>                                           0.00031</td>\n",
+       "      <td>0.0005842</td>\n",
+       "      <td>0.0005842</td>\n",
+       "      <td>0.0005842</td>\n",
+       "      <td>0.0005842</td>\n",
+       "      <td>0.0005842</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.0003103</td>\n",
+       "      <td>0.00031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B3</th>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                          -2.3e-05</td>\n",
-       "      <td>                                          -2.3e-05</td>\n",
-       "      <td>                                          -2.3e-05</td>\n",
-       "      <td>                                          -2.3e-05</td>\n",
-       "      <td>                                          -2.3e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
-       "      <td>                                        -1.246e-05</td>\n",
+       "      <td>-2.3e-05</td>\n",
+       "      <td>-2.3e-05</td>\n",
+       "      <td>-2.3e-05</td>\n",
+       "      <td>-2.3e-05</td>\n",
+       "      <td>-2.3e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
+       "      <td>-1.246e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B4</th>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                         3.826e-07</td>\n",
-       "      <td>                                         3.826e-07</td>\n",
-       "      <td>                                         3.826e-07</td>\n",
-       "      <td>                                         3.826e-07</td>\n",
-       "      <td>                                         3.826e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
-       "      <td>                                         2.112e-07</td>\n",
+       "      <td>3.826e-07</td>\n",
+       "      <td>3.826e-07</td>\n",
+       "      <td>3.826e-07</td>\n",
+       "      <td>3.826e-07</td>\n",
+       "      <td>3.826e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
+       "      <td>2.112e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B5</th>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                         -2.31e-09</td>\n",
-       "      <td>                                         -2.31e-09</td>\n",
-       "      <td>                                         -2.31e-09</td>\n",
-       "      <td>                                         -2.31e-09</td>\n",
-       "      <td>                                         -2.31e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
-       "      <td>                                        -1.359e-09</td>\n",
+       "      <td>-2.31e-09</td>\n",
+       "      <td>-2.31e-09</td>\n",
+       "      <td>-2.31e-09</td>\n",
+       "      <td>-2.31e-09</td>\n",
+       "      <td>-2.31e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
+       "      <td>-1.359e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DTC</th>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
-       "      <td>                                                 3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FD</th>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
-       "      <td>                                                 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
-       "      <td>                                             -3.35</td>\n",
-       "      <td>                                             -3.45</td>\n",
-       "      <td>                                             -3.47</td>\n",
-       "      <td>                                             -3.56</td>\n",
-       "      <td>                                             -3.56</td>\n",
-       "      <td>                                             -3.56</td>\n",
-       "      <td>                                             -3.56</td>\n",
-       "      <td>                                             -3.56</td>\n",
-       "      <td>                                             -3.56</td>\n",
-       "      <td>                                             -3.56</td>\n",
+       "      <td>-3.35</td>\n",
+       "      <td>-3.45</td>\n",
+       "      <td>-3.47</td>\n",
+       "      <td>-3.56</td>\n",
+       "      <td>-3.56</td>\n",
+       "      <td>-3.56</td>\n",
+       "      <td>-3.56</td>\n",
+       "      <td>-3.56</td>\n",
+       "      <td>-3.56</td>\n",
+       "      <td>-3.56</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            -3.581</td>\n",
-       "      <td>                                            -3.581</td>\n",
-       "      <td>                                            -3.581</td>\n",
-       "      <td>                                            -3.581</td>\n",
-       "      <td>                                            -3.581</td>\n",
-       "      <td>                                            -3.303</td>\n",
-       "      <td>                                            -3.303</td>\n",
-       "      <td>                                            -3.348</td>\n",
-       "      <td>                                            -3.348</td>\n",
-       "      <td>                                             -3.54</td>\n",
+       "      <td>-3.581</td>\n",
+       "      <td>-3.581</td>\n",
+       "      <td>-3.581</td>\n",
+       "      <td>-3.581</td>\n",
+       "      <td>-3.581</td>\n",
+       "      <td>-3.303</td>\n",
+       "      <td>-3.303</td>\n",
+       "      <td>-3.348</td>\n",
+       "      <td>-3.348</td>\n",
+       "      <td>-3.54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
-       "      <td>                                           -0.1161</td>\n",
-       "      <td>                                            -0.077</td>\n",
-       "      <td>                                            -0.087</td>\n",
-       "      <td>                                            -0.075</td>\n",
-       "      <td>                                            -0.075</td>\n",
-       "      <td>                                            -0.075</td>\n",
-       "      <td>                                            -0.075</td>\n",
-       "      <td>                                            -0.075</td>\n",
-       "      <td>                                            -0.075</td>\n",
-       "      <td>                                            -0.075</td>\n",
+       "      <td>-0.1161</td>\n",
+       "      <td>-0.077</td>\n",
+       "      <td>-0.087</td>\n",
+       "      <td>-0.075</td>\n",
+       "      <td>-0.075</td>\n",
+       "      <td>-0.075</td>\n",
+       "      <td>-0.075</td>\n",
+       "      <td>-0.075</td>\n",
+       "      <td>-0.075</td>\n",
+       "      <td>-0.075</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            -0.113</td>\n",
-       "      <td>                                            -0.113</td>\n",
-       "      <td>                                            -0.113</td>\n",
-       "      <td>                                            -0.113</td>\n",
-       "      <td>                                            -0.113</td>\n",
-       "      <td>                                           -0.1012</td>\n",
-       "      <td>                                           -0.1012</td>\n",
-       "      <td>                                          -0.09143</td>\n",
-       "      <td>                                          -0.09143</td>\n",
-       "      <td>                                            -0.072</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>-0.1012</td>\n",
+       "      <td>-0.1012</td>\n",
+       "      <td>-0.09143</td>\n",
+       "      <td>-0.09143</td>\n",
+       "      <td>-0.072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C4</th>\n",
-       "      <td>                                            0.9974</td>\n",
-       "      <td>                                             0.972</td>\n",
-       "      <td>                                             0.989</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.995</td>\n",
+       "      <td>0.9974</td>\n",
+       "      <td>0.972</td>\n",
+       "      <td>0.989</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.995</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                             1.059</td>\n",
-       "      <td>                                             1.059</td>\n",
-       "      <td>                                            1.0515</td>\n",
-       "      <td>                                            1.0515</td>\n",
-       "      <td>                                            1.0515</td>\n",
-       "      <td>                                            0.9892</td>\n",
-       "      <td>                                            0.9892</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.995</td>\n",
-       "      <td>                                             0.985</td>\n",
+       "      <td>1.059</td>\n",
+       "      <td>1.059</td>\n",
+       "      <td>1.0515</td>\n",
+       "      <td>1.0515</td>\n",
+       "      <td>1.0515</td>\n",
+       "      <td>0.9892</td>\n",
+       "      <td>0.9892</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.985</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C5</th>\n",
-       "      <td>                                            0.0026</td>\n",
-       "      <td>                                             0.028</td>\n",
-       "      <td>                                             0.012</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.005</td>\n",
+       "      <td>0.0026</td>\n",
+       "      <td>0.028</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            -0.059</td>\n",
-       "      <td>                                            -0.059</td>\n",
-       "      <td>                                           -0.0515</td>\n",
-       "      <td>                                           -0.0515</td>\n",
-       "      <td>                                           -0.0515</td>\n",
-       "      <td>                                            0.0108</td>\n",
-       "      <td>                                            0.0108</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.005</td>\n",
-       "      <td>                                             0.015</td>\n",
+       "      <td>-0.059</td>\n",
+       "      <td>-0.059</td>\n",
+       "      <td>-0.0515</td>\n",
+       "      <td>-0.0515</td>\n",
+       "      <td>-0.0515</td>\n",
+       "      <td>0.0108</td>\n",
+       "      <td>0.0108</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.015</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IXO</th>\n",
-       "      <td>                                              5.54</td>\n",
-       "      <td>                                              8.25</td>\n",
-       "      <td>                                              8.49</td>\n",
-       "      <td>                                              5.04</td>\n",
-       "      <td>                                              5.14</td>\n",
-       "      <td>                                               7.8</td>\n",
-       "      <td>                                              7.85</td>\n",
-       "      <td>                                                 8</td>\n",
-       "      <td>                                              8.05</td>\n",
-       "      <td>                                               8.1</td>\n",
+       "      <td>5.54</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>8.49</td>\n",
+       "      <td>5.04</td>\n",
+       "      <td>5.14</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>7.85</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.05</td>\n",
+       "      <td>8.1</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                              4.73</td>\n",
-       "      <td>                                              4.44</td>\n",
-       "      <td>                                              0.71</td>\n",
-       "      <td>                                              2.22</td>\n",
-       "      <td>                                             0.342</td>\n",
-       "      <td>                                            8.0491</td>\n",
-       "      <td>                                              8.29</td>\n",
-       "      <td>                                            8.1509</td>\n",
-       "      <td>                                              8.31</td>\n",
-       "      <td>                                              7.65</td>\n",
+       "      <td>4.73</td>\n",
+       "      <td>4.44</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>2.22</td>\n",
+       "      <td>0.342</td>\n",
+       "      <td>8.0491</td>\n",
+       "      <td>8.29</td>\n",
+       "      <td>8.1509</td>\n",
+       "      <td>8.31</td>\n",
+       "      <td>7.65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IXXO</th>\n",
-       "      <td>                                              3.56</td>\n",
-       "      <td>                                               5.2</td>\n",
-       "      <td>                                              5.45</td>\n",
-       "      <td>                                              3.16</td>\n",
-       "      <td>                                              3.25</td>\n",
-       "      <td>                                              4.92</td>\n",
-       "      <td>                                              5.08</td>\n",
-       "      <td>                                              5.18</td>\n",
-       "      <td>                                              5.39</td>\n",
-       "      <td>                                              5.54</td>\n",
+       "      <td>3.56</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>5.45</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>4.92</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>5.18</td>\n",
+       "      <td>5.39</td>\n",
+       "      <td>5.54</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                              2.87</td>\n",
-       "      <td>                                              2.74</td>\n",
-       "      <td>                                             0.438</td>\n",
-       "      <td>                                              1.37</td>\n",
-       "      <td>                                             0.212</td>\n",
-       "      <td>                                            5.1339</td>\n",
-       "      <td>                                            5.1339</td>\n",
-       "      <td>                                            5.5099</td>\n",
-       "      <td>                                              5.57</td>\n",
-       "      <td>                                              5.06</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>0.438</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>0.212</td>\n",
+       "      <td>5.1339</td>\n",
+       "      <td>5.1339</td>\n",
+       "      <td>5.5099</td>\n",
+       "      <td>5.57</td>\n",
+       "      <td>5.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C6</th>\n",
-       "      <td>                                             1.173</td>\n",
-       "      <td>                                             1.067</td>\n",
-       "      <td>                                             1.137</td>\n",
-       "      <td>                                              1.15</td>\n",
-       "      <td>                                              1.15</td>\n",
-       "      <td>                                              1.15</td>\n",
-       "      <td>                                              1.15</td>\n",
-       "      <td>                                              1.15</td>\n",
-       "      <td>                                              1.15</td>\n",
-       "      <td>                                              1.15</td>\n",
+       "      <td>1.173</td>\n",
+       "      <td>1.067</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>1.15</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                             1.159</td>\n",
-       "      <td>                                             1.159</td>\n",
-       "      <td>                                              1.13</td>\n",
-       "      <td>                                              1.13</td>\n",
-       "      <td>                                              1.13</td>\n",
-       "      <td>                                            1.1335</td>\n",
-       "      <td>                                            1.1335</td>\n",
-       "      <td>                                            1.1325</td>\n",
-       "      <td>                                            1.1325</td>\n",
-       "      <td>                                             1.113</td>\n",
+       "      <td>1.159</td>\n",
+       "      <td>1.159</td>\n",
+       "      <td>1.13</td>\n",
+       "      <td>1.13</td>\n",
+       "      <td>1.13</td>\n",
+       "      <td>1.1335</td>\n",
+       "      <td>1.1335</td>\n",
+       "      <td>1.1325</td>\n",
+       "      <td>1.1325</td>\n",
+       "      <td>1.113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C7</th>\n",
-       "      <td>                                            -0.173</td>\n",
-       "      <td>                                            -0.067</td>\n",
-       "      <td>                                            -0.137</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.15</td>\n",
-       "      <td>                                             -0.15</td>\n",
+       "      <td>-0.173</td>\n",
+       "      <td>-0.067</td>\n",
+       "      <td>-0.137</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.15</td>\n",
+       "      <td>-0.15</td>\n",
        "      <td>...</td>\n",
-       "      <td>                                            -0.159</td>\n",
-       "      <td>                                            -0.159</td>\n",
-       "      <td>                                             -0.13</td>\n",
-       "      <td>                                             -0.13</td>\n",
-       "      <td>                                             -0.13</td>\n",
-       "      <td>                                           -0.1335</td>\n",
-       "      <td>                                           -0.1335</td>\n",
-       "      <td>                                           -0.1325</td>\n",
-       "      <td>                                           -0.1325</td>\n",
-       "      <td>                                            -0.113</td>\n",
+       "      <td>-0.159</td>\n",
+       "      <td>-0.159</td>\n",
+       "      <td>-0.13</td>\n",
+       "      <td>-0.13</td>\n",
+       "      <td>-0.13</td>\n",
+       "      <td>-0.1335</td>\n",
+       "      <td>-0.1335</td>\n",
+       "      <td>-0.1325</td>\n",
+       "      <td>-0.1325</td>\n",
+       "      <td>-0.113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Notes</th>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
        "      <td>...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
-       "      <td> Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
+       "      <td>Source: Sandia National Laboratories Updated 9...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -6620,88 +4224,95 @@
     {
      "data": {
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-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>Imp</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Vmp</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Ix</th>\n",
-       "      <th>Ixx</th>\n",
-       "      <th>Ee</th>\n",
+       "      <th>i_sc</th>\n",
+       "      <th>i_mp</th>\n",
+       "      <th>v_oc</th>\n",
+       "      <th>v_mp</th>\n",
+       "      <th>p_mp</th>\n",
+       "      <th>i_x</th>\n",
+       "      <th>i_xx</th>\n",
+       "      <th>effective_irradiance</th>\n",
        "    </tr>\n",
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        "</div>"
       ],
       "text/plain": [
-       "                           Isc  Imp  Voc  Vmp  Pmp  Ix  Ixx  Ee\n",
-       "2014-04-01 00:00:00-07:00    0    0    0    0    0   0    0   0\n",
-       "2014-04-01 00:00:30-07:00    0    0    0    0    0   0    0   0\n",
-       "2014-04-01 00:01:00-07:00    0    0    0    0    0   0    0   0\n",
-       "2014-04-01 00:01:30-07:00    0    0    0    0    0   0    0   0\n",
-       "2014-04-01 00:02:00-07:00    0    0    0    0    0   0    0   0"
+       "                           i_sc  i_mp  v_oc  v_mp  p_mp  i_x  i_xx  \\\n",
+       "2014-04-01 00:00:00-07:00     0     0     0     0     0    0     0   \n",
+       "2014-04-01 00:00:30-07:00     0     0     0     0     0    0     0   \n",
+       "2014-04-01 00:01:00-07:00     0     0     0     0     0    0     0   \n",
+       "2014-04-01 00:01:30-07:00     0     0     0     0     0    0     0   \n",
+       "2014-04-01 00:02:00-07:00     0     0     0     0     0    0     0   \n",
+       "\n",
+       "                           effective_irradiance  \n",
+       "2014-04-01 00:00:00-07:00                     0  \n",
+       "2014-04-01 00:00:30-07:00                     0  \n",
+       "2014-04-01 00:01:00-07:00                     0  \n",
+       "2014-04-01 00:01:30-07:00                     0  \n",
+       "2014-04-01 00:02:00-07:00                     0  "
       ]
      },
      "execution_count": 22,
@@ -6711,7 +4322,7 @@
    ],
    "source": [
     "sapm_1 = pvsystem.sapm(sandia_module, irrad_data['DNI']*np.cos(np.radians(aoi)),\n",
-    "                     irrad_data['DHI'], temps['tcell'], am, aoi)\n",
+    "                     irrad_data['DHI'], temps['temp_cell'], am, aoi)\n",
     "sapm_1.head()"
    ]
   },
@@ -6736,31 +4347,33 @@
     "    plt.subplots_adjust(wspace=.2, hspace=.3)\n",
     "\n",
     "    ax = axes[0,0]\n",
-    "    sapm_data.filter(like='I').plot(ax=ax)\n",
+    "    sapm_data.filter(like='i').plot(ax=ax)\n",
     "    ax.set_ylabel('Current (A)')\n",
     "\n",
     "    ax = axes[0,1]\n",
-    "    sapm_data.filter(like='V').plot(ax=ax)\n",
+    "    sapm_data.filter(like='v').plot(ax=ax)\n",
     "    ax.set_ylabel('Voltage (V)')\n",
     "\n",
     "    ax = axes[0,2]\n",
-    "    sapm_data.filter(like='P').plot(ax=ax)\n",
+    "    sapm_data.filter(like='p').plot(ax=ax)\n",
     "    ax.set_ylabel('Power (W)')\n",
     "\n",
     "    ax = axes[1,0]\n",
-    "    [ax.plot(sapm_data['Ee'], current, label=name) for name, current in sapm_data.filter(like='I').items()]\n",
+    "    [ax.plot(sapm_data['effective_irradiance'], current, label=name) for name, current in\n",
+    "     sapm_data.filter(like='i').iteritems()]\n",
     "    ax.set_ylabel('Current (A)')\n",
     "    ax.set_xlabel('Effective Irradiance')\n",
     "    ax.legend(loc=2)\n",
     "\n",
     "    ax = axes[1,1]\n",
-    "    [ax.plot(sapm_data['Ee'], voltage, label=name) for name, voltage in sapm_data.filter(like='V').items()]\n",
+    "    [ax.plot(sapm_data['effective_irradiance'], voltage, label=name) for name, voltage in\n",
+    "     sapm_data.filter(like='v').iteritems()]\n",
     "    ax.set_ylabel('Voltage (V)')\n",
     "    ax.set_xlabel('Effective Irradiance')\n",
     "    ax.legend(loc=4)\n",
     "\n",
     "    ax = axes[1,2]\n",
-    "    ax.plot(sapm_data['Ee'], sapm_data['Pmp'], label='Pmp')\n",
+    "    ax.plot(sapm_data['effective_irradiance'], sapm_data['p_mp'], label='p_mp')\n",
     "    ax.set_ylabel('Power (W)')\n",
     "    ax.set_xlabel('Effective Irradiance')\n",
     "    ax.legend(loc=2)\n",
@@ -6780,1231 +4393,9 @@
    "outputs": [
     {
      "data": {
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H9pXW3V5i8sg+xEcFs2FXGQtnDsEa3Gu+2uhK07T1qqqmtrHqceBO4L+HLfsZ\nsEzTNCeQq6pqNjAR2OD3oMLvvF4vDT9uQrFYCB3Rs6YJCxs/gbqvvqTquw2EXzxA7ziAfjVNQkIi\nY8aMoaKigXPOOY+VK5dz2WW/anPbadOmAzBo0GCampoICQkhJCQEs9mMzeYbJycjYzh9+iQDMGfO\nOWzfvpUZM2b7JbtcXdNReXkZBoOBmppqvF6v3nEC2ne7StmVW8OIQTGkbVtN8/4cwiedQdzPL+m0\nY1iHZZB49TV4mppo+s+/WTg5maYWF8s+39dpxxBC+I/X6+WrrcVYzAamjpDpxXoLo0Fh2qhkHC4P\nm7RyveP0aqqq/gwo1DRt+1GrkoHCw54XAn27LJjwK0dxEc6yUl8X4KCe1UBgTVcxhIZS+e0GvB6P\n3nF0dXh3Y6/X22H3Y4vFDIDBYMBsNh9abjAYcLvdx2x/vP2dLrkMqhOXy8Xf/vYXHnjgYT7++EOW\nL3+93askvV2D3cHyL7KxmA38IraW+lXrCUoZQOLVv0YxdO41m4jJU2gpKqLmk49I376KtOSJ/LCn\nnKk5lTJVkRABLr+skfLaJiZlJGINNh//BaLHmDI8ife+2s93O0s5KzNZ7zi9kqqqVuBefN2ZD+ro\nG+xxr/THx4efbqzTJhmOf/z8z33XN5JnnOm3rHp+BnWTJ1H+xRqCq0uIGDZUtxyg3+fQ0hJKWVkp\nW7duZfTo0axf/wVTp05uM4/BoBAbG0ZUVDhhYUGEhFgObXdwXWWllT17smhpqSM5OZmvv17LokWL\n/Pb+pOD1o46uVLz22n/IzBzLyJGZpKUN4frrr2Tq1GmkpKR2XcBu4r2vD9DY5OTysdHY334OQ0gI\nfX7zPxjMFr8cL+6in9OcvY/GTT/wywXpPFRq4o3V+8hIjcFklE4RQgSqzXsrABiXLvfu9jaxkcEM\nTI4gu6iOphYXIUHy9UYHaUAqsE1VVYB+wI+qqk4CioD+h23br3VZhyoq9J1uKj4+vNdnOJHjl633\njc7sGZjul6x6fwam4aPgizUUfvEV8XH6dUzQ83OorraRkjKA119/nbvuupvU1EHcdNP8NvN4PFBV\n1YjTaaSxsYXmZueh7Q6uq621M3RoBn/8459aB62aQGbmpBN6f6dSFMv/CH60atW6dtddffV1hx5b\nrVZef73tG797u5IqG+u2FJMYFUz65k9odjhIvOE3WBIS/HZMxWgk6bobyPvzH3F++BbnzL2Wj/c0\nsmZzEWecNQLRAAAgAElEQVRP6H/8HQghdLG3oBYFGD5QBgDsjYanxrC/uJ49eTWMkYseXU7TtB1A\n4sHnqqoeAMZpmlatqur7wBuqqj6OryvzEGCjPklFZ3KUFOMoKiR09JgOp4TszqzDhmO0WmnYvIm4\nhYt67ewdRqORRx999LhF6cqVP92+P2/efObNm9/mOqs1lEceeaLzg7ZBmqtEQFu5NgeP18tl0RU0\nZ+8lbMw4wif4fwRAc2wcCZf/Cm9LCxP2f0WIxcgH3xzA1uz0+7GFECfP7fFwoLSevvGh0rrXS6kp\nUQDsL6nXOUnvoKrqMuBbIF1V1QJVVX991CaHuixrmpYFvAlkAZ8AN2uaJoOX9AANP24CILyHzL3b\nFoPZTPT4cbiqqmjJyzv+C3qoziz0FUWhK68byLcCP8vJyebBB+8/YpnFEsTixUt1StR97MmrYWt2\nJZnxRkK++gTFGkrCL6/ositr4ZOnUP/dt9izdrJo1jCW5ofw4be5/GLWkC45vhDixJVW2XE4PQzs\nE6F3FKGTfglhABSWN+qcpHfQNO2y46wfdNTzh4GH/RpKdLnGrVvAaCQ0c7TeUfwqbspkKr9aT+Pm\nTQSnpuodp8v16ZPMyy8vP2LZvff+gZKSI+9MuPnmW5kwYfJx9zdmzDjGjBnXqRk7IgWvn6WlDWbp\n0jf0jtHteL1e3lu/H4DzbNvxtjSTcPU1mKKiuiyDoigk/Ooq8v50H8mbPiNp4MWs2VzEuRNTiJRp\nioQIKBW1zQAkxlh1TiL0EmG1EBlqoajSpncUIXoFV10tLbkHCBk6DKM1VO84fhU1OhPFZKJx+7ZO\nnSGkO3v44Uf1jnDCpEuzCEh78mvZW1jHWbEteLf/SNCAVCKmTOvyHJaEBGLOm4+7vp4FpjycLg+f\nfJ/f5TmEEB2rqG0CIC4yWOckQk9xkcHUNLTgkan+hPA72w7f6Mxho3p26y6AMSSEkKHDcBQW4Kyq\n0juOOElS8IqA9P7XB8DrZWqRb076hEWXd/oURCcq+uxzMUZFEbH9G/oHO/lySxF1NocuWYQQbauo\n8xW88VE9c9AUcWKiw4Nwe7w0yDlaCL+zbdsGQGhmps5JukbYKN/7tO3YpnMScbKkS7MIOHvyatAK\najnbWoU3J4/wCRMJGZJ+wq93edzsqMxCq84mv6GIBmcDbo+HULOVPqGJpEWlkhk/gjDziXW/MQQF\nEXfxAsqWvsTFjt085RnFp9/nyb28QgSQ+tYCJ0puN+jVosN9LfzVDS1y64kQfuRxOrFl7cScmIQl\nMUnvOF0idFQmvPEatu3biJoxS+844iRIwSsCzkff5aJ4PYwt2QRGI7EneK9Es6uZtQXfsO6bb2ho\n8Q1aoqAQZg7FaDBSbCslv6GQ70t/ZIX2HuMSM5mXOocEa9xx9x1xxlRqP18Ne7eSNnQQa7cUcf4Z\nqYSFmE/nrQohOom9xQWANVj+W+vNwq2+c3Jjk4yoL4Q/Ne3V8La0HGr17A3McfFYkvti352Fp6UF\nQ5BcVOsu5JuBH9100zU899wSvWN0K4XljezKrWGupQwqy4k8azqW+OPPubu1YifL97xDg7ORMEso\nM/tNY3TCSFLC+2Ex+r4AebweSm3l7Kraw4bSH9lYuplNZVuZmzKDeQPnYDa0/+ugGAzEXryA4qee\nYF6Lxr+M41i31Vf0CiH019TswmhQsJjkTp3e7OAFj6bWCyBCCP+wbdsK0ONHZz5a6KhMaj79GPue\n3YT1svfenUnB60dS7J68VT8UoHg9jCnZDEYjMedf2OH2To+L5do7bCjZhMlg4vyBc1k45jwaa4+9\num9QDCSHJZEclsTslLPYWrGTd7M/4rO8Neys2s0NI68iLiSm3WOFjhxFUOpAOLCLvoOG8MWPhZwz\nMQWTUb5gC6E3W7OL0GBTl01bJgLTwTmY7VLwCuE3Xq8X2/ZtGEJCCBncu27vCsscTc2nH2PbvlUK\n3m6k1xS8367JYf+e8k7d56ChCUyZldbu+rlzz2T16vVtrlu3bi3vvLOSJ598lsrKSm655QaeeeYF\nVq/+lP37c7jnnvvJycnmgQfu44UXXiGoF3SbqLM52JBVyhR3AYbaKiJnzsIcG9vu9g2ORp7f8Qr7\n63JJCe/LVRmXkRSaQIg5mEY67s5mUAyMTRhFRkw6b+/7kG9LNvLopqe5fuSVDI4a2OZrFEUh9sKf\nUfzUP5nv1FjcGMHG3WVMGdHntN63EOL02VtchATLLQa9nbW14G1qloJXCH9xlBTjrKwgbPxEFFOv\nKSUACB6UhiE0FNv27Xi9XrnI2k1I05Rftf9LMH36TOLiYnn77RU88shDXHfdb4iJiWXhwsspKipk\n3bq1/PWvf+HOO+/rFcUuwNrNhbhcHibX7vK17s6b3+62DY5G/rllMfvrchmXkMn/jr2ZpNDjd30+\nWrApmF8Ou4RF6s+xu5p4ZuuL7K3JaXf70JGZBKUOJLpwD3GOWj7bWIBXpr8QQnf2ZtehYkf0Xgdb\neJscUvAK4S/2nTsBCB0xUuckXU8xGgkdPgJXTTWOkmK944gT1Gu+HUyZldZha6webrvtTq64YiEj\nRoxi9uyzAV8r4r33/omrrlrERRddwogRo3RO2TWcLjdrtxQxwlmCubaSiKlnYo5pu3txo8PGU1ue\np9RWxsz+01gw+ILTvsJ2Zt/JRAVF8OKOV3l22xL+J/NahkQPOmY7RVGIPf8Cip95ivO8+3mlPIq9\nBbWoKdGndXwhxKnzer243B65f1dgMfv+DTicHp2TCNFz2bJ2AWAdPkLnJPqwZoygYeP32HftJCi5\nr95xxAmQbwc6Ki8vw2AwUFNTfUQrYUFBPlarlYqKzu2CHcg27amgwe5kRrMG+Oa+bYvT4+L5HS9T\nbCtler+pnVLsHjQyLoPrR16Jx+vh+R0vU2Zr+/MPzRyNOTGR5OLdhLqaWLulqFOOL4Q4NS637/xp\nkoK31zO3jqngdEvBK4Q/eJxOmvbuwZKcjDm6d17st2YMB8DeWviLwCffDnTicrn429/+wgMPPExK\nSirLl78OQGNjI08++RjPPPMi9fV1fPnlFzon7Rpfbi2iT3MFYeUFWEeMIqjvsVfMvF4vy/e8Q05r\nN+ZLh1zY6fdOjIgbxuVDF2B3NfHstiU0OmzHbKMYDETPOQfcbqY79/OjVnFoDlAhRNdztRY3JsPp\nnQ/sTjsFDUXsr8tlf10u5fZKml0tnRFRtMHr9VLbUkdRYwnZtQc4UJdPub0St8d9yvs0t170cLqk\n4BXCH5pzsvE6HIeKvt7IHBODpU8ydm0PHqdMgdYd9JouzXroqBh77bX/kJk5lpEjM0lLG8L111/J\nlCnTeOONV1iwYCH9+vXn7rv/yK23/obRo8cRFRXVhcm7VlFFI/sK67jSkQ1AzLnz2tzuy8Jv2FC6\niQHh/fnVsIV+Gyhgcp/xVDRV8WnuF7y8ezk3jfo1BuXIa0MRU6ZS+d93GF61m1VBKt/sKGHe5AF+\nySOE6Jjb09rCewojphc1lvB10QZ2Vu2hurmmzW2ig6LoF96HAeH9SY8ezICIfqeVt7eqbakju/YA\n2bUHyKsvoMxeTov72IuFZoOJ1IgUxiVmMjFpHEFGywkfw2wyAlLwCuEvB1s1e3PBC773X/vFappz\nsrEOHaZ3HHEcUvD60apV69pdd/XV1x16bLVaef31twC45577Dy1PSEhk+fJ3/RcwQKzbWkyEs5E+\nFdkEpQwgRB16zDaFDcW8l/0RYeZQbhh15aG5df3l/IFzyasvIKtK44v8r5g7YMYR6w1BQUTNmEn1\nhx8w2rafL7eGcc6kFAwyWp8QXe5gcXMyXZobHI2s3PtffizfBkCIKYSMWJX4kDiCjBa8Xi+NThs1\nzbUU20rZUbmbHZW74cAqLEYLwxOGoEakMzIug6igSL+8r+7O4/WQW5/Ptopd7KjMosxecWidSTGS\nYI0n0RpPRFAEwcYgXF4XjQ4bhY3FZNceYF/tfj4+8DkXDz6fiUljT+iY0sIrhH/ZsnwDi1rTj/2u\n1ptYh/sKXnvWLil4uwEpeIWuHE433+4sZWpTDorXS9TsOce03DrcDpbuegOX180VwxZ2yZdLg2Lg\nqoxF/HXjE7y//1PSo9MYENH/iG2iZs6m+pOPOcO+j001Q8jKrWbEwPanURJC+If7JLs05zcU8ty2\npdQ7GhgQ0Z95qbPJiFExGoztvqbe0cD+ujy06mz21mSzpWQXW0p2sVx7lwER/cmMG05m/HASrQm9\nepoKp9uJVpPN9spdbK/MosHRCIDFaGFE7FAGRw1icNQgUsL7dvh51zTX8nXx96zJ/4qXs5aj1WRz\nubqgw9cAGFv/DbjkHl4hOp27sZGWvFxChqRjCA7WO46urOlDwWjElrWLuJ9foncccRwBXfCqqmoE\nNgGFmqZdoHeeU5GTk82DD95/xDKLJYjFi5fqlCiw/LCnnObmFkbVZ2OwhhI+YdIx27yX8wml9nJm\n9JvKiLiuu4oWbgnjyoxFPL31BV7bvZK7JtyKyfDTr4wpMorwceNh4/f0iyhn3ZZiKXhFh44+p6mq\nGgOsAAYAucBCTdNqdYzYLR0coMh4Al2a8+sLeXLLYlrcDi5KO4/ZKWcdc8tCWyIs4YyOH8Ho+NZR\nSa0OvtR+YFvlLrJr95NXX8D7+z8lwRpHZtwIMuOHMyCi/wntu7trcjWxq3IPWyt2sqtaw9HaTTnM\nHMqUPhMZFZ+BGj3kpHrmRAdHccGgczijzwRe2vkaG0o20eJ2cM3wyzv8TA8WvB6ZLs7vVFVdApwP\nlGuaNrJ12aPAfMAB5AC/1jStrnXdPcA1gBu4VdO0VboEF6fMvjsLvN5e350ZwBAcTEjaYJr27cXd\n2IgxLEzvSKIDAV3wAr8DsoBwvYOcqrS0wSxd+obeMQLW+u0lqI35mJttRM49B4PlyHu1DtTl81Xh\ntyRa47ko7bwuzzc0ZgjTkifxdfH3fJa7hvMHnX3E+sjpM2nY+D1TW/bzVnYSjU1OwkL8291adGtH\nn9PuBlZrmvaIqqp3tT6/W69w3ZW7dZRm83EK3tqWOv69fSktbge/Hn4Z4xJHn/Ix40NjmdF/KjP6\nT8XmtLOrag/bKnaRVbWH1flfsjr/SyItEYyKH05m3HCGRA864oJZd9fgaGR75S52797D9tI9uL2+\ngabiQ2Jb3/MIBkamnHbBHxcSw+/G3Mi/ty9lS/l23g+O4aLB7f9fYDhY8Hqk4O0CS4GngVcOW7YK\nuEvTNI+qqn8D7gHuVlU1A/gFkAH0BT5XVTVd0zRpiu9GbFmt8+/20umIjmbNGE7TXg37nizCx0/U\nO47oQMD+76uqaj/gPOAh4Had4wg/qKxtYm9BLde15AAQOWPmEevdHjfLtLfx4uUydQFmP9+3256L\nBp/Pzqo9fJq3hjEJo0gOSzq0LiRdxZLUh9Ty/VjCx7BxdxmzxsqANuJY7ZzTLgSmtz5+GfgSKXhP\nmstzsIW3/a7EXq+XV7PepM7RwILB80+r2D1aqNnKxKSxTEwai8PtRKvZx9aKneyozGJ90XesL/qO\nEFMwI2KHMSp+OBkxKsGmoE47flfwer2U2yt8hX3lLnJqc/HiKyr7hSUzOn4EmfEj6BOa2OlduoNN\nQdww8ioe3fQ0q/O/JD06jYxYtc1tD46jIAWv/2matl5V1dSjlq0+7On3wILWxz8Dlmma5gRyVVXN\nBiYCG7oiq+gc9t1ZGKyhBA1I1TtKQLBmjKDqvXewZ0nBG+gCtuAFngD+AEToHUT4x3e7SolrqSWu\nthjrsOFYEpOOWL+mYD1FjSVM6TOBIdGDdEoJIaZgLlN/znPbl7Jy73+5dcwNh77QKYpC5IyZVCx/\ng1EN2XyzI0EKXtGets5piZqmlbU+LgMSuzxVD+ByHX+U5u9KNrGnZh8ZsSoz+5/ptywWo5mRcRmM\njMvA7XGTU5fL9opdbK3YyQ9lW/ihbAsmg4n0qDTUmMGo0YPpG9YnILs+N7ua0WpyyKrW2F2lUdU6\nirWCwsDIAYyOH8EsdRJKk/+Ld6s5hGtG/IpHNj3Fcu0d7pv0+zZHbz5Ya0u9GxCuAZa1Pk7myOK2\nEF9Lr+gmnFWVuCorCR0zFsUQeOcrPQQPGIAhOBj73j16RxHHEZAFr6qq8/HdE7JFVdUZeucRnc/r\n9fLtzlLGNu4DIHLmrCPW17U08Enu54SZQ7lo8Pl6RDzCiLhhjIgdys4q331qYxJGHloXccZUKt9e\nyURbDk8XD6ekykZ8fLfthS/84ETOaZqmeVVVla/pp+DQPLzttPA63A4+3P8pFoOZy9UFXTaolNFg\nJD06jfToNBYMuYCCxiK2V+zydX2u1siq1gBfC3F6VBppUQNJjehPv7BkXXq01LU0kFN3gP21ueTU\nHaCwsQSP1/fZhphCGJMwiowYleGxKpFBvus28WHhVDQ1dEm+/uHJzO5/Fqvzv2RtwXrOTZ19zDaK\nomBQFLmHV2eqqt4HODRN6+ieLvkhdSNNmu98ZW1jJo3eSjEaCR6cjn3ndly1tZh68BSi3V1AFrzA\nFOBCVVXPA4KBCFVVX9E07cqOXhQIRYbeGfQ+/olm0PKqqaxuZKTtAOaoKFJnT8Ng+umf47s/vE+L\n28EVo39OavLJN3r543O4ftJl3P7pX/jvgY+ZoY7HYmptXYgPp+HMqXjXfElKUxlbcqoZNTSp2/ws\nekOGANDWOe1VoExV1SRN00pVVe0DlJ/IzgLhMw2kDMW1zQCEhQW3meu/u1dR52jg4mHnkt6//zHr\nT/f4JyohIYJxg4YBl1DdVMvOMo2dZRo7yvewpWIHWyp2AGBUDAyI6seg6BT6RiTRNyKJ5Igk4qzR\nx7QEn8rPwe5ootxWSVFDKXm1Ra1/Cqlu+mm8NKPByJCYVIYnqozpM5zBMantjpDclf8Wfhl1Id+V\n/sCagq/4eebZhFqsx2QwGBSMBkOX/xsNhN+JQKCq6tX4bt84/IpEEXD4L1+/1mUdCoTPVDL4jl+b\n57v9LHnSWMJ0yKP3Z9BehpZxmeTt3I6pJI/4IZ33/8vJZOhqgZDhZAVkwatp2r3AvQCqqk4H7jhe\nsQtQUdE1V5nbEx8ffkIZ1qz5nCVLFhMbG8eTTz7Hn/50L7m5Bzj//AtZuPCyEz5eY2Mjq1d/ysUX\n+4ZD93js3H//Azz44N9P+T0cbs+e3Xz66UfcdtsdJ/ya430GDz30Z6ZOPZMCRz8aN79KRbCD4TMu\noqqm6dA2xY2lfLH/G5KsCYwKzzzpn+uJ/hxOlokQZvabxuf561ix5RPOTf2pVdoybjKs+ZLR9v18\n8cMAfjVvGNVVjZ2e4WT463PoThkC5aTczjntClVVHwGuAv7e+vd7J7K/3v5zPTpDXa0dALut5Zhc\nTo+L93evJsQUzJS4Mzot9+l/BkaGhWYwbFAGlwz0UtFURW59Prn1BeTVF5BfW8T+mvwjXmEymIi0\nRBAZ5PuTGBGDx6lgMVgIMpoxGcx48ODx+v443S5sLhs2px27005dSz1VzTXYXU3HpIkKimRE7FAG\nRaaSFjWQlPB+P42q7IXqKrufPoeTN6f/dN7L+Zh3t63mnNRZx2QwKNDicHVpLr1/JwLlXKeq6rn4\nbt2Yrmla82Gr3gfeUFX1cXxdmYcAG4+3v0A6z/TWDAePX7N9BwarFXtoDE1dnEfvz6CjDJ6+AwEo\n27QVhmXqkqErBUqGkxWQBW8belS3lw8//C933nkfo0aNpqqqEk3bzfLl7570fhoa6nn33ZWHCt7E\nxMROK3YBhg4dxtA2JtN2uVyYDmuNPfp5RxRFweOFjVllXN0/leT6fCKmTD1im3ezP8KLl4sHn3/c\nORe72rmps/mu5Ac+z/+Ss/pOxmr2tS6EDEnHFBeHWpPHJ3WN7MypJDmqd89RJzp08Jz2N+BNVVWv\npXVaIt0SdWMd/QextXwHDc5GZvc/C6s5pMsynQxFUUiwxpFgjWNi0lgAXB4XZfYKSm3llNnLKbWV\nU9FURV1LPbn1+b6uxifUH+AnZoOZ2OBoBkYOIDY4mnhrHH1D+9A3rA9hllA/vDP/mNZ3Mp/kfs76\nog3MSZl+zHrFIF2au4KqqsvwDboXp6pqAfAnfKMyW4DVqqoCfKdp2s2apmWpqvomvlHqXcDNmqbJ\nD6mbcFZX4ayoIHT0GLl/9yjBAwagBAXTpMl9vIEs4AteTdPWAetOdz81Raux12Z1QqKfWKMyiO47\nt8NtPvvsY956awUul5OMjBHExcWzY8c2/va3/2PatLP4/vsNVFSU8+tfX85tt91JXFwcjz/+CLW1\nNQQHB3PXXfeRkpJKdXUVjz76V0pKigG44467WblyGUVFhfz615czYcJkrrvuaq677npeeWUFN9xw\nNffccz8DB/oGe/rtb2/glltuJyVlAE888QgHDuzH7XZxzTU3MG3asV8YADZv3sTy5a/zyCNP8NJL\niykuLqS4uJjExCRSUgZQVFRAcXExSUl9uPHG/+H//u9+XC4HTqeb22+/kxEjRuH1enniiUfYtGkj\nCQmJmM1m8kob8DaaeXPnOq4YNYb0fv157LG/sWdPFg32BlyDjJy14GyGxw7lkksuYN68+XzzzXrc\nbhf/939/IyUlFbvdzj//+SiathtQuOaa65k+fRYbN27g1VdfwmZrom/fftx7758ICem8L7khpmDO\nHjCTd7M/4vP8r7gw7VwAFIOBiDOmUv3Bf1Eb81m/tYhfzEjrtOOKnuPwc5qmadXAHH0T9SBt3Jq7\nrvBbFBTO6ndG1+c5DSaDib5hvmL0aB6vhwaHDVOoh7KqGlpcDlo8DlweFwbFgFExYFAMmAwmQs1W\nQk1WQs1WgoxBXXb/sj+FmIKZmDSO9UXfsbNqN0mJR/5sjYqCRya78TtN09rqkrakg+0fBh72XyLh\nL4fu301ve3T03kwxGgkZMgT7zh246moxRcp9vIEo4Ave7iw39wBr1qzm3/9egtFo5B//+Dt9+iQz\ndOgwfvvb/0VVh/Lzny/kzjtvOzRX7+9+dxN/+MO99OvXn127dvKPf/ydJ598jn/+8zHGjh3PpZcu\nwuPx0NRk56abbuXAgf2HXtvSUnfo2HPmnM2aNau59tobqayspLq6ClUdyuLFzzB+/ETuvfdPNDQ0\ncMMNVzF+/CSCg4/fGpmXl8ezz76IxWLhpZcWH/G8paWZJ554hr59Y9m8eRcPPPD/ePHFV/jqq7UU\nFOTz+utvUVVVxa9+dSmWxLEMb2zgRyA00zc1yA033ExERAT//PHffPLEW4wxDENRFBRFISoqmiVL\nXuPdd99i2bLXuOuu/8d//vMi4eHhvPzycgAaGhqora3llVeW8PLL/6Ghwclrr/2HFSte5+qrr+vU\nn+tZfaewJv8r1hZ+zYz+U4mw+LpWREzxFbxj7Pt5b/swFpw5sMNRY4UQ/lVur+BAfR4ZMSpxIbF6\nx+k0BsVAZFA48dHhhLoi9Y6jizP7TmZ90Xd8X7qZORlHFrwGaeEVolMdHIU4JF0GrGqLNV3FvnMH\nTXv3Ej5BpicKRL2m4I3uO/e4rbGd7ccfN6Jpe7juuisAcDgcREdHA75Rig//G8But7Nz53b++Me7\nDi1zOl2Ar7X1/vv/DwCDwUBoaBj19fXtHnvmzDncfvtvufbaG1mzZjUzZ/oakTZu3MA333zFsmWv\ntu7fSXl5KSkpqR2+F0VRmDbtLCwWS5vPnU4XTzzxdw4cyMHrhcLCAgC2bt3C3LnnoigKcXFxjBkz\njgPF9cx35vIjEDbCN9rxmjWrWPnuCsoaK/DaXDgrf7r1Z/p0372y6elDWbduTetn+wN/+ctfD20T\nHh7ON9+sJzd3P4sWLcLlcuN0uhg5clSH7+tUWIxmzk2dzYq977Eqdy2XpF/oWx6fQMiQdPru24tS\nV82evBpGDOo5X7KF6G5+LNsOwPhOnHNXBIa+YX1IDk1iV9Ue7I4j70k2KDIPrxCdqWmvhiEkhKCU\nFL2jBKSQ1pGr7doeKXgDVK8pePUyb958brzxf45YdsstN7bZrczr9RAWFn6oxfbY9Sf+H3h8fAKR\nkVHk5GSzdu1q/vCHew+te+ihR+nf/+RPWkFBwe0+X7HidWJj43jqqX9SWlrLrFlTAN+ciIfnrrM5\nsLrqiXVUYQwLx2ANpbi4iOXLX2f8rbM50JxPxFdeHI6WQ6+xWHwDpxiNBtxu96HlbX0e48dP4pln\nnvL7DfVTkieyOn8dXxdv4JzUWYRbwgBfK2/Tvr2MaNjP97sHS8ErRBdo78z4Y/lWTAYTo+KHd2ke\n0TXGJWbywf7P2Fi0leFhIw4tl3t4heg8LVXVOMvKCB2VKffvtiN4QCpKUJDcxxvA/P4vV1VVk6qq\nKaqqDlBVtesnFtTRuHETWbv2C2pqagCor6+jtLSk3e1DQ8NITk5m7drPAV9Bl529r3VfE3j33bcA\ncLvd2GyNWK1W7Pa2R84EmDVrLq+//jI2m41BgwYDMHHiZN56a/mhbfae4GTZxyu27XYbMTG+4u7T\nTz/C03oDVWbmWL74YjUej4fKykr2ZG2jf7NvpBVjpG8eR5vNhmI2sL8pj4GmfuzavP24eSZMmMQ7\n77x56HlDQwPDh49kx45t5Of7RjZtamqioCC/vV2cFpPBxNyU6Tg9LtYWfH1oedj4iSgWC6NsB9is\nVeB0yY1kPU1vPqcFusMvI5bayimxlTE8dighJhlArical+Brud9YuPWI5UaDIi28QnSS+l27AAiR\n+3fbpZhMhAwegqOkGFcHvS+FfvxS8Kqqqqiq+gtVVVcBNcAPwPdAjaqqq1VVXaSqavcfOeM4UlMH\ncv31N3H77f/DVVddxu2330JVVdUx2x3e2nv//Q/y4Yfvc/XVl3PFFb/g669943XddtsdbNmyiauu\nWsR1111Bbm4ukZFRjByZyZVX/oJnn33q0D2vB82cOZsvvljFrFk/deW++urrcLlcXHXVIq64YiEv\nvVJcWHsAACAASURBVLS43fy+/R37+Kf1Pz2++OJL+eT/s3ff8ZGk1aH3f9W51co5jjQzkh5N1ISd\n2Zx3DSywGGOWcDELxjY421wHuPhe49cXDOuX19jGYByANclLMHiBZWHZHGcn53mUc8651aHeP6ql\n0URJM+qulnS+n09/JFVXVx31tGrqPOE8P/0Jb3vb22hra8Xvt6oX33nn3ZSVlfG+972Tv/6//wdv\nxgY2BPtx+P04A1avaFVVNa4CH/ofDlD32FF27rxSWffzv9/DD3+I8fFx3v/+d/GBD7yXo0cPk5mZ\nySc+8Uk++tGP8vDD7+EjH/l12tpar/j7Xa+bivaR6g7wQucrTIetIdhOv5/Azloyg6Okjfdzunko\nbucXiSPXtNXnzJBVZGVH7labIxHxkpeSQ0FKHif7NKFoeH67w5AeXiFWytiZs4AkvIvxV1YBMNNY\nb3Mk4nKM5QyTXSql1KvAUeAx4HWt9XRsux/YD7wL2K21XsmymWYyrAuVDGul2elqMRw618cPv/0s\n7+/8Kem33Erhr/8mYK27+6nX/z82Z1Tw0b2/E9cYVtqTLc/wo6Yn+eXND3B/+V0AjB8+SPeX/olX\nM7cTvPNN/NZb7RlOmeyfhwSdf0WSUJuuaVey7q91F8dwunmIzz12jLffvpG33mqtifiFY//G2aE6\nPn3rX5DhTY/r+e0iMcD36h/n2faX+P1dv0lNtnXD+WdfeoWoafL//s6ti7x65dj9PqzUtS7JyLUu\nCWLo/NQnmersovIfvoixxCUoV5rd78FSYpg6e4aOzz1C1hveRN4732VLDImQJDEs+3oXr0/u27TW\nl6wQGLtJfB54XimVH6dziyR1SPexdaIZgLR9N85vf6b9RQDuvcx6isnujpKbear1WZ5pf5G7Sm/F\n7XQT2FGLw+dj+1Qr/1ZnDWt2u2Teyyr3oNa6/+KNck1LMrERILORWepHmihJLYpLsiuSx7bsGp5t\nf4nTg+fmE16xPEqpEuAeYBPWzIAm4FmtdbutgQnbRaanmWxtw19ZZVuyu1r4Nm4Ch4PpBunhTUZx\nuQu/XLILoJS6TSn171fbRyTegQOv8sEPvveCxyc+8acreo5gKMLx+j62TrXhCARI2WINMxwNjnOw\n5wj5/lx25G5Z0XMmQorbz+0lNzM2O87B3qMAODwecm7cT9rsONnjfZxtlWHNa8BnlVJX7S6Sa1ry\nqB9pJhwNszVbhuCtdZWZG/E43ZwbuvAmU0Y0L04ptU8p9VPgBeCNQArgA34JeFYp9aRSSkrOrmMz\nTY0QjeLbXGl3KEnP4fPhLS0j2NpCNBSyOxxxkbg31yilioCHgQ9iFdP8ZrzPKZbnxhtv5sYb4zsS\n81TTIAVjPaSEpki7+a75lsIXOl4mbEa4Z8PtOIzV2Qt6Z+ktPN3+As91vMzNRfusJZhuv5X+519g\ny3gzR+oG2Lk51+4wxfVpA76ulAoBXwEe1Vr32ByTiDEvqtPcOGKNJKnO2mxHOCKB3E43VTkbOdNX\nz1RoihR3yiX1JsQV/Snwl1rr1y/3ZCzZ/RPgoYRGJZLGTGMDAH5JeJfEX1lJsK2VYGvL/JxekRzi\nVbTKpZT6FaXUj4HjQAWQprWu0Vr/dTzOKZLbsfoBtky0AMyvUTYbmeXFztcIuFO4sXCvjdFdnyxf\nJrV52+mc6KYhdqOduasWR0oKW6daOVbXJxVDVzmt9SeBzcBvA9sBrZT6kVLq7Uopp63BiXlzeU7T\naAsGBhszZM3I9aAmtxITk6bR+BUpXIu01g9dKdmNPf+61lqS3XVsWhLeZfHFkty5900kj3h1qXUB\nHwa+BpRqrT8CBK/6CrFmRaJRTjb0UzPZijM9fX6B7kO9x5gMT3F7yc14nB6bo7w+d5Vao12f63gZ\nAIfbTeqevaSGpkgf6qSxa9TO8MQK0FqbWutntNa/BpQBjwOfxLreiSQRiUZoGWunOLUQv8tvdzgi\nAWryrJ78xtEWewNZZZRSLyil/kopdZdSymt3PCK5mNEoM02N+EuKcaal2R3OqjDXqyvzeJNPvBLe\nZ4FarHkgMv9jnWvoGCV7qB1/JEjq3n3zC5e/2PkqBga3Fd+4yBGS3+aMCkpTizkxcJqhGWvd5bnC\nXDUTrRytG7AzPLGCYssP3YZ1fduIdb0TdlowgKJ9opNQNMSmjArbwhGJVZWzEQNjfii7gYxpXqJ/\nAHKAf8RaYu05pdRfKqXuUEqt7lZocd1muzqJTk+TVlNjdyirhjs7B1dWNjMN9cRjFRxx7eJVtOpd\nwFbgJPAPSqlGIFMptSke5xPJ7Wj9AFWTVrHHtL03ANA61k7beCc7creS5cu0M7wVYRgGd5XeStSM\n8mLnawCkqBocKSlUT7ZzRPfJxW+VU5bPAB3A/8WqzlyutX63vZGJOYYBzaNtAGzKKLc5GpEoKW4/\nxamFtI93EjWjdoezamitv6e1/j2t9Q6gHCvxzQW+CozYGpyw3dyw3LQaKf63HP7KSiLj44T6pI5l\nMolblSCt9ZDW+h+11nuAXwW+AbweW89SrBOmaXKsrh812Y4jEMBfVQ3ACx3Wx+D2kpvsDG9F3VCw\ni4A7hZe7DhCKhDBcLgI7a0kPT+Lo7aBzYNLuEMU1il23XsaqYvpmrfUerfUXtNbDNocmLtIxYY0w\nL0srsTkSkUilqcXMRkP0T1mjaaSBcemUUqnAPuBGrFF5Qax7NrGOzTRYCW+6JLzL4tssw5qTUULK\n4mqtj2qtfx8oAT6fiHOK5NA1OIWzt53U8BSptbswnE4mQ1Mc7jtGrj9nTa2b6Ha6uanwBiZDUxzs\nPAFA6u49AFRNtnO07pJlXMXq8XmgRGv9B1rrY3YHIy60MLXpmujG5XCR75fK6OtJaVoxEGvwkBHN\nS6KUekQp9RrWskQPAAex1hzfqrX+LXujE3abbmzAkZKCv1QaD5djbh7vTKMkvMkkXlWaLztvV2sd\n1Fo/FttnXzzOLZLLsfp+qiesIYapu61KzAe6DxGKhrmt+MZVuxTRldxSbH2sn2myilcFtu8Etxs1\n2caRepnHu4q1aK2vWnhPrmn2i5pRuiZ7KQoU4HRI8ez1pDR1LuHttjmSVeUDWM0Dj2H16H5fa91r\na0QiKYTHxgj19eLbVDlfd0UsjbesDMPjYbqpye5QxALxWof3T5RSecC3gVeBztj2UuAm4N3AALK2\n25p3tH6AeyfbMTweUrZtxzRNXuo6gMvh4uaitZcfFAYK2JRRwYneswxsGiLXn01g23Y4dpSxtk5G\nJ3aSkSrFMFeh/ynXtOQ3aY4QjoYpSS2yOxSRYKWxf/POiW4gExnQvCQFWPVW7gT+GNgbq7nyLPC8\n1vqqU9CUUl8B3gz0xeYBo5TKxkqgy4EW4CGt9UjsuY8Dvw5EgD/QWv88Hr+UuH4zTY2ANR9VLI/h\ndOIrr2C6oZ5oMIjDK/d8ySBeRaseAv4I2IF1g9gee3wb2Al8VNZ2W/tGJ4KMtrSRExojsG0HDo+H\n5rE2eqf6qc3dRqonYHeIcXFrsTXA4dXug8D5Yc3Vk22cbBqyLS5x7eSaltzmpmtOmladncKUfBuj\nEXZIcacQcKfQPz0gI5qXKLbU2mmt9RdjxUYV8E3gg8BLSzjEV4E3XrTtY8BTWutq4OnYzyiltgJz\nBU3fCHxRKSVdh0lqptnqnfRtlFqz18JXsRFMk5nWFrtDETHx6uFFa30c+P14HV8kvxONg1RPWNWZ\nU/dYSd9r3YcA1mTv7pzd+Tv5XsPjvNZ9iAcq7iN15y56DYPqiTZONA1y207pfVqN5JqW/KbNcQBy\n/Tk2RyLskOvPoWO8ixTp312SWMJ5A3BX7HErMAw8B3x6sddrrV9USlVctPlBrB5jgEdjx/oY8Dbg\n21rrENCilGrAKpD12vX9FiIeZlqsJb585RX2BrJKzTUUzDQ3kVItRb+SgbSuibg52TRI9WQbOBwE\ndu5iNhLicO9xMr0ZqOy1O0zG6/Rw+4b9jARHOTtUhzMtDX+1oiQ4QEtdO+GILJshRDxMmWMA5Piz\nbI5E2CHPn0PEjGC6ppGcd0lGgO8B24HvAru11hVa6w9orR+9xmMWLJgH3Is1bBqgGGtJtzkdWIVM\nRZIxTZOZ5mbc+QU4U1PtDmdVOp/wNtsciZgTtx5esb5FolFa6zu4LziIf8tWnIEAR3uOMhOZ4c7S\nW9ZcsaqL3bPpVn7e+AKvdh9ke+4WUnfvZVqfo3S4hcbOUdQGuSEXYqVNR62EN9eXbXMkwg55sZ79\nqGcSpmTe3BLs0Vo3xOvgWmtTKXW1podFmyXy8tJWMKJrs95imO7uJjo1SfYNu+fPu97eg+uNwcxN\npT09nVBby4rHvZreh2QiCa+Ii8bOMUqGWwFI3bUbgNd6DgNwY9Fe2+JKlI1ZZRQFCjg1cJapkLUk\nU/9/fpPKyU6ONw5KwivEirLum6fMcfwuHynuFJvjEXbI9lnX1ahr2uZIVo1FW4aUUvu01geXccxe\npVSh1rpHKVUE9MW2dwJlC/Yr5Xzxvyvq7x9fxqlXXl5e2rqLYezwKQCMojL6+8fX5XuwEjF4yyuY\nPHmC7sZOXOnptsQQD8kSw3LFtZtNKVWzlG1i7TnZNMjmKWv0UmBnLcMzI5wbqmdTRjkFKXk2Rxd/\nhmGwv3APYTPC0b6TuPPycBcWUT7dzZl6WfVBiHgImpNkejPsDkPYJN1j3QSZzhkZ0bw0f6KUelYp\n9VtKqR1KqezYY2ds2zPAny7zmI8DD8e+fxj44YLt71ZKeZRSG4Eq4PUV+S3EipovWFUhBauux/yw\n5hZZnigZxHtc6beXuE2sMacbeqmY6sFVUIgnL5/Xe45gYnJT4Q12h5Yw+wqsnu0DPUcASK2txW1G\ncLU3MTAqPRCrkVKqQCn1DaXUi7GfdyqlPmJ3XAIwooQIkupem9XfxeLSPNZ8Q9N11SWzRcz1Vp9X\nSn0beMX6VrUrpT4IfAa4XylVB9wT+xmt9RngO8AZ4KfA72itpV0iCc20NIPDgXfDBrtDWdVkHm9y\nicuQ5th6lfmAL1aKfk4mIGPN1rjRiSBGSyNuM0zazlpM0+RAzxHcDhd7CnbaHV7CZPkyqcrcRP1I\nE4PTQwR21DL8syfZPNXBycZB7t5TaneIYvn+FetmrTb2s8ZaxuOfbYtIWMsSuUIApHqkyMp6db6H\nVxLepbqe6vNa6/dc4an7rrD/p1lC9WdhHzMSIdjWirekRNaPvU6+io3A+R5zYa949fD+D+DHwCbg\nJwseXwIeidM5RZI42TTE5ilrak5gZy0dE930TvWxPXcrfpff5ugSa3+hNV/5YO9R/JVVGD4/myc7\nOdEwYHNk4hqVaK2/BIQBtNZBQMpuJwHDNQtAmvTwrltzjR1R58z5xZmFEEs229WJOTuLN5asiWvn\nTEvDnZfHTHMTplyPbBeXHl6t9eeBzyulPqG1/lQ8ziGS18nGAfZMdYLXh7+qmsMtPwfghvzaRV55\n/WamQ5w62kndmV6GBiYZH50hNBsmGjXxeF34Uzxk5wXIK0hlw+ZsMrLiO+Bgd/52vlP3A17vOcob\nyu8hsH075qGD9DW0Eo7sxOVc29Wq16CwUsqY+0EplWlnMOK8uYRXhjSvX26HC5/Tx6xz1u5QhFiV\npufm726U+bsrwbdxE+OvHyDU348nP9/ucNa1uFZp1lp/SimVglWNz7Vg+5l4nlfYJxKJ0qlbuDc0\nTmDPXnA6Odx3HJ/Ty9ac+NQri0SitNQPcvZ4Fx0twxc07AdSPfhTPDgcBrPBMEP9E/T3jKNPAr+A\nrJwUttQWUbOzEK/PveKx+V1+duRu5UjfCdrGO8jcsZOJQwcpG22V5YlWp//CGr6crpT6APC7wFdt\njUhgAobbSnICHkl41zOfy8usI2x3GEKsSsEWa76pT3p4V4Svwkp4Z5obJeG1WVwTXqXU72IVLBgG\nIgueWvQvSSnlA54HvIAH+G+t9cfjEadYOXVtI5SMxJYj2rGTlrE2hmaGubFwLx7nyiaU0ahJw9k+\nDr7YzNjIDAD5xWlsqy0mKy9Adm4At8d50WuijA5P090xSmvDIO1NQ7zyTCOvv9hM7b4yaveX4fWt\n7J/FvoLdHOk7waHeY7xt+x0AVE52crplSBLeVUZr/Vml1PuALOAB4O+11t9YymuvdE1TSmUDjwHl\nQAvwkNZ6JB7xr2kO678Yv9NncyDCTj6Xj1HHsFRpXiKllBP4kNb6X+yORdhvprkJw+PBW1xidyhr\ngm+jle4EW1rgxpvtDWadi/c6vH8CbNdaty73hVrrGaXU3VrrKaWUC3hJKXWb1vqllQ9TrJSjdX1s\nnowtR7RjJz/vfQGAvQUrO5x5sG+CZ5/Q9PeM43AYbN1dzI49JdZw5ausEeZwOMjKCZCVE2BrbTHT\nU7OcO9nDiYMdHH6lldNHO7n1viqqtuZjGMZlj7FcW3IUPqePo30n+ZXKt+Apr6C0tZUf1vfAHZtX\n5BwicWIJ7pKS3Ited9lrGvAg8JTW+hGl1J8DH4s9xHI4rV49r0sKraxnfqcXU3p4l0xrHVFKfRiQ\nhHediwaDBDs78W3chOGKd3qwPnjLNoBhMNO27DRIrLB4f6K7ryXZnaO1nop96wGcwNCKRCXi5tSZ\nTt443Ye7dAOOjAyOnDpBwJVCTVbVihw/GjU58korh19pJRo1qdqaz/47NpKeeW3FsPwpHnbfuIHt\nu0s4edhKep/+0VkazvZx9wMKf4rnumN2O1zszNvK6z1HaBlrJ712F7OtLTibNZMz+wnEYSi1iA+l\n1HeJjaCNbTKBMaylOb6mtb5qAavLXNOGsRLeO2PbHwWeQxLe5THBiPXwep3X/zcrVi+fywdGFNOI\nLL6zmPOMUuqdWuvv2h2IsE+wox2i0fleSXH9HD4fnoJCgm2tmNEohkPqttgl3u/8U0qpR5RSe5RS\nW+ceS32xUsqhlDoG9ALPytzf5DYdDDNbdxYnUdJqa2kYaWJsdpxd+TtwOpyLH2ARM9MhnvjuCQ6+\n1EJKqoc3P7SD+x7ces3J7kJuj5M9N5fzrg/to6Q8k9aGQb73tcP0do1d97EB9uRbyzEd6TtOYIf1\nfcVUF2dbhlfk+CJheoEy4EXgJaz6BAAPAZ9f7MWXuaadBgq01r0Ljl+w4lGvB3M9vJLwrmtep9XD\nbxrSy7sMHwQeU0pNKaX6Y48+u4MSiRVsbQHAt6HC1jjWGm95OdHpaUIDsjqHneLdw/swVg/IOy/a\nvqTmo1hvyS6lVAbwM6XUXVrr5660f15e2rXGuWLsjsHO8x8808OGyS4Aim/dzwujRwG4T91y3XEN\n9E3wg68fYWRomsot+bz9vbuv2vt6refLy0tj0+Y8XnqmgWefPMd/f/MYb3v3LrbvWf58loUx3J69\nh0fP/icnBk/zW296iPaUFCqmumnqGedNt8dvWLPdn8dkiWEF1QJ3xZYjQin1ZeBp4B7g2GIvvsw1\n7e6LnjeVUotOP0yG9zSZYsjom5zv4S3MzSIvMzGxJdN7IDFYMWSmpkI/ONzhhMeVDO/DNbrB7gCE\n/Wba2gDwbthgcyRri3dDOeMHXiPY1iKFq2wU7yrNFSt0nFGl1E+wLsrPXWm/K83bTJSrzR1dD+d/\n9XgXG6e6MD1epjLzOaCPku5JI5eC64qrr3uMn3znJDPTIfbeWs6+2yqYmAwyMRm87P4r8T7U1BYS\nSPfw8x+e5r++eYTe3jF23lC6+AuvEsPOnG0c6DnMoRZNypatmIcPUX9U039XfBJeuz8PyRBDHG5A\n84HQgp9DQK7WOqiUmlnqQRZc0/YCvUqpQq11j1KqCFi0Z2W9/7teHMPI6BQ4rYR3aixMfyj+sSXb\neyAxWDFEZ63ZBlEzktC47H4frudap7VuUUqlA5Va6yMrF5VYTYJtrRhuN56iYrtDWVN85RUAzLS2\nknbDfnuDWcfiPphcKXWfUur3Yt8XKKWql/i63Lk1LpVSfuB+4Gj8IhXXq023kB0aJ1BTQ9NkBxOh\nSWrztuMwrv1j1t0+wuPfPk5wJsSdb6xm/+0bV6yY1GLKNmbztvfuJiXg4eVfNHD4lesrOrA7fwcQ\nG9a8bTsAmb3N9A1PXe1lIrk8D/xYKfUepdR7gceBF5VSqcDlW2BirnJNexxrNAyxrz+MV/Br2vwc\nXilatZ65HbF2fMdVp9OLBZRSDwCngR/Eft6nlPqRvVGJRDLDYYKdHXhKSjGc1z8FTZw312MelMJV\ntoprwquU+jjwl8AfxjZ5WPqalUVYhRSOAQeAH2mtn175KMVKGJ0I4utoBCCwbTvH+k8BUJu37ZqP\n2d8zzhPfO0kkHOX+t21j667EtzrmFqTy9l/bTVqGj9dfaOb4wfZrPlZNdjV+l1WtOWWL9b5UTHVz\nWubxria/B/wU+FXgHcCTwG9rrSe01jct8torXdM+A9yvlKrDGhr9mbhFv4YZsR5et0OKwK1n8wmv\nFK1ajv8H2E+sMKjW+iAgSwisI8GuTohE8G0otzuUNceZEsCdl0ewtRXTlAXT7BLvObzvwRqGfABA\na92ulFrSuBut9UlgTxxjEyvobOswFdPdAKRs2cbx5q/id/mpzry2/zOHBib58WPHCc1GuO/BrWyu\nyVvJcJclPdPPW99dyw+/eZRXnm7E7XGytXb5ybfb4WJnrjWsudMzjSMvnw0DPbzY2M/du2XNu9VA\naz0L/GPssdzXXvaaprUeAu67/ujWMRMwrB69+YRHrEuuuQYPQ3p4l0Nr3a2UWrhp1q5YROLN9T56\nyyXhjQfvhnImDh8iPDyEOzvH7nDWpXgPaZ6O3SCKNe5MyyAVU904srLpDoQZCY6yI3fLNVVnnpqc\n5SffOcHMdJg736io3GL/JP+MLCvp9fndvPBkHe3N17ZC1tyw5uP9p0jbvgOvGWbkXB3RqLT6rQZK\nKbdS6sNKqS8qpb4y97A7LsF8gnM9UyjE6ud2Wg0esizRsowppQrnflBK3YW1ZJpYJ+YT3jJJeONh\nbh7vXCVskXjxvjNoU0rdDqCUciql/gI4FedzigQzTZP+M/X4o7Pk7NnF8YHTAOzK277sY0XCUX72\nX6eYGAuy//YKttQWrXS41yw7N8Cb3rEdh8Pg5z88zfDA5LKPobKq8DjcnBg4Mz+Pt2i0nfa+iZUO\nV8THl4FbgLcC9VjDAKdtjUhYDBMDR8Lm+Ivk5DLm5vBKwrsMHweeACqUUs8D3wL+1N6QRCLNtLWB\nw4G3dOnFOcXSeWNDxWdaZR6vXeKd8P4B8H+A7cAUcBfwx3E+p0iwvuFpsvutP+KsXbUc7z+F2+Fm\nS/aS6pPNM02T55/U9HSOUbk1nz23JF9LY2FpBnc9UMNsMMIT37MqRy+Hx2m9L71TfUyU5WA6HGyc\n6uZcmzSmrxL7gQ8Aw1rrvwFuxbq+CRuZgGFEccS/DqNIcufn8MqQ5qXSWh/Aqh/wP4BHgG1a60P2\nRiUSxYxGCba34SkqxuGRdczjYS7hlcJV9onbZCellBN4q9b6fqVUAHBore1du0DExZnWYSqmuzAx\nmNpcQO9L/ezK247HubwL55lj3ehTveQXpXH3m1TS9tRUbytgeHCSI6+08exPzvHGd2xfVqw787Zx\nfOA0Jyea2Fy+iaLmBp5p6Ib9svbdKjAdWys3opQKxJYXsn/MvZjv4RXrm0uqNC+bUuo3gV9orZ9Y\nwWN+HHgfEAVOAh8EAsBjQDnQAjyktR5ZqXOKaxPq68UMBmX93ThypafjysqWHl4bxe3uQGsdAT4c\n+35Skt21Szf0Ujrdj7OklMOjDQDULnM482DfBC8/3YDX5+INb9+Gy53cZfH33baRkvJMWhoGOXGw\nY1mv3Z6zBQODEwOnydi5AwOYqTsn83hXh0GlVDZWdeafKqX+C1jeB0DEh2HK/F2B2zlXtEqGNC/D\nbqzrWbNS6t9iy64VXOvBlFIVwG8Ce7TWOwAn8G7gY8BTWutq4OnYz8JmM7FeR5/M340rb3k5kdER\nwiPSxmOHeN8dPKOUemeczyFsZJomU/ocTqJk7NjB6x3HcBgOtudsWfIxQrMRfv7fZ4iEo9zz5hpS\n031xjHhlOBwG9z24FX/AzWvPNdHbNbbk16Z6AmzOrKB5tA2zeiMAJWMdtPZKm9Aq8OZYVeVPAP8K\nPIO1PJGwmyOKQXI3lIn4c841ehjSgLhUWuvf0VrXALcDB4FPA13XccgxIASkKKVcQErseA8Cj8b2\neRT45es4h1ghwVap0JwIc0s+zbS12BvIOhXvhPeDwGNKqSmlVH/s0Rfnc4oE6h6cIn/YWpvWrKqg\nabiN6szNpLj9Sz7Gy083MDI4xc4bSqmoyo1XqCsuJeDhvrduJRo1efpHZwnNLr1HoTZ3GyYmOmUS\n0+Nlw3QPuk1a/VaB/wmgtY5qrb+utf4C8BGbY1r3zNiyRDKHV8z18ptIwrtUSqm9Sqk/B76Ctdb4\nz7B6ZK9JrFHwc0AbVqI7orV+CijQWvfGdusFrrkXWaycYFsbAN4yGdIcT/PzeNvbbY5kfYr33cF9\nwCZgK7Av9tgf53OKBNLtI5RP92A6XdRnWAWctucuvXe3rWmIs8e7yckPcNNdm+IVZtyUVmRRu7+M\n0eFpDjzftOTX7czbBsCJoXN4K6vJDo3TUtcWrzDFynnPEreJBDMMUxJegdOI9fJLD+9yHMSqPP8p\nrfUOrfVHtNbfvdaDKaU2A38EVADFQKpS6n0L99FamyCtEnYzTZOZthbceXk4U1LsDmdN85aVAZLw\n2iWeRascwLe01lvjdQ5hv8aGLm6fHca1qYqTY/UASx7OPBsM8/yTGofD4J431+B0rc6b1f23V9Da\nOMjJw51srM6lpDxr0dfk+nMoDhRybried2y5hdkzJ5mt10Sit+B0rM73YS1TSt0P/BJQrJR6BJir\nUpZhX1TiAkZU5vCK858BqdK8HDcB9wJ/oZT6R+Al4Gmt9fev8Xg3AK9orQcBYrUObgZ6lFKFb2sv\nOAAAIABJREFUWusepVQRsOiIv7y8tGsMYeWs5RiC/f1EJyfJqt151XOs5fcgUTGYuam0BQJEujuu\n6zir/X2wS9wSXq11VCnVrpTKjg1vEWuMaZpMao0BpG2tQQ8doSStkLyUnCW9/tVnG5kYC7L31nJy\nC1bfH88cl9vJPW+u4QdfP8KzT2je9aEblvS6nXnbeLLlabqL/PiB4vEuWnsm2FScHt+AxbWYBSax\nKo5OYiW8JtZwvU/bGJcAwJQqzQJY0MMrnYdLprV+HXhdKfUfWD29H8OaqnGtf1DngP+tlPIDM1ij\n/V7HunY+DHw29vWHix2ov9/e2hZ5eWlrOoaJo2esbwqKr3iOtf4eJDIGT0kp0/V19HYM4PB6bYnh\neiVLDMsVt4Q3Zgw4qpT6CdaFDsDUWv9ZnM8rEqBveJrc4U4ABkrSmR0Psbt4adWZO1uHOXOsm+y8\nAHuTcL3d5SooTmfXjRs4+lobh15u5cGHFu/l3Z6zhSdbnuaUe4i9Xj8bpnvRbcOS8CYhrfXzwPNK\nqe9prU/ZHY+4DMPEIUWr1r35ETIypHnJlFJfwOrh9WMV4vsE8Oy1Hk9rfTyWPB/CaiQ8AvwLkAZ8\nRyn1IWLLEl1f5OJ6BTus4bVzw21FfHlLy5iu0wQ7O/FvWn3T+FazeCe8p2MPsJpb53pFxBowN383\n6nRxOjAG47C3eMeir4tEorz4c2v4890PKJzOtdErc8Ot5TSc7ePEwQ5uumMzxiL33uXppaS6A5we\n1txaVY3j1HEO6xa4afU3AKw1SqnfJXYNU0rdseApA6sR74v2RCbmyZBmgQxpvkYngb/TWjeu1AG1\n1o8Aj1y0eQirt1ckifmEt1QKViXCXMPCbEe7JLwJFteEV2v9yXgeX9irsaGLW2eHcWyq4uRIHX6X\nD5W7meHBqau+7uShDoYHp9i6u5j8orXTm+lyO7nt/kp++r1TPPH9Ezzwzh0YhnHF/R2Ggy3ZioO9\nRzArFZw6TrixnnDkdlxrpBFgDdmHNNYlrWjUxHDIkGYhRauuhdb6y0qpHKXUm2ObXpubfyvWtmBH\nB46UAK6sxUelievnLY0VruqQIqWJFteEVyn1t1ymZ1eGNK9+pmkyee4cAE5VzuDMCfbk78TluHq3\n5sR4kIMvteDzu7jxjo2JCDWhKipz2ViVS3P9AHWne1HbC6+6/7YcK+FtznNQCpSMd9HaO87mYqmF\nlEy01h+wOwZxZSZWb55UaRbSw7t8Sqk3AN8AjsU21Sql3qe1/rmNYYk4iwaDhPp68VdVX7VxXqwc\nT0kpGIZUarZBvIc0T3I+0fUDb8Eqfy9WucHRGbKHOgBoz3dDaGnVmV99ppFwKMqt91bi87vjHaYt\nbr2vko7WYV59tpGNVbl4vFf+M9uSU42BwTFnD8X+ABume6hvG5GEN0nFqs//FtawPBN4CvjX2BIb\nwiZRM5bwLjaPQKx5UrTqmnwauENrfRZAKbUFKwGWhHcNm+3qBNPEW1pqdyjrhsPjwVNQSLCjHdM0\npaEhgeLaHK61/qTW+q9ij49hlaUviec5RWLo9hE2TPcQdbk54h3AwGBrjrrqa3o6Rmk420d+URpb\naosSFGnipWX4uOWuzUxPhjj2+tVb8VLdASrSN9A01oa7spL08BSddS2JCVRci88Cvwr8APhv4J1c\nOk9NJFiECIAMaRY4YjeQpgxpXg7XXLILEPs+3h0iwmZz83c9pVKwKpG8ZWVEp6cJDw7YHcq6kui7\ngwlAZsavAY31XeTPjmBsqKBhoo2K9A2keVKvuL9pmrz6nFUP45Z7K9d8q9bNd20mJdXD8QPtTIwH\nr7rvtpwaTEwmK7IBCDXWETXlZi1JvRF4k9b6m1rrbwAPxLYJG5mx3jwZ0izOFy6Ta+gyDCilPgig\nlDKUUh8A+u0NScRbsMMapeeVhDehvGVWGiTDmhMrrncHSqm/XfD4HFaZ+zPxPKdIjMlzVmNwcFMe\nJibbcmquun9L/SA9HWNUVOVQVLr2h+t6vC72376RcDjKwRebr7rvtlyrZ/xcdhiAgtFOehYp/CVs\nZV7he2EXaSASMQbG3Ddi6T4MfEQpNQNMY63B+2F7QxLxFuxoB8PAWywDLxPJM1+4ShLeRErkHN4w\n8CXgv+J8ThFnQ2MzZA5af6jNudY/79ac6ivuH41Gee35JgwDbrpz/ZRhVzsKOXGog3Mneth5Qyk5\n+ZfvAS9NLSbNk8qRaAc7UlIpn+6lrn2Y4txAgiMWS/Az4KdKqa9i3VI/HNsmbBSN/TdjSJaz7p0f\nPSSNIEuhlMoGMoBfwlozF631uK1BibgzTZNgZwfuvHwcPp/d4awr8z28kvAmVFx6eJVSLqVU4KI5\nvJ8Cfoz8L7Tq1XWcn797yNNPwJ1CWdqVWwjPnehhZHCKLbVFZK2jJM7hMLj5bivBf+35pivvZzjY\nll3DeHiSaEUZqZFpOs61JChKsRRKqdtj3/4ZVqPdO4C3x76XqvPJQvLdde98o4fcaixGKfUuoAN4\nAmgF9kuyuz5ERkeITkxIwSobuDIzcaSmypDmBIvXkObPAO+9zPZ3Y1UDFKtYS0MXebOjRMrKGAqP\nUZNVtWDe1IXC4QiHXmrB5XZww20ViQ00CZRtzKaoLIO2xiF6u8auuN+2XGtI+HCp1SAw01CXkPjE\nkj2qlKoD/hz4b631O7TWv6q1/mettax/YrPzqY1kvOvdWq8PscL+ArhFa12A1YD3f2yORyTIfMGq\nEkl4E80wDLylZYT6+4jOzNgdzroRr4T3HuArl9n+NeDNl9kuVpFJra2vFdZC5TXZVx7OfPZ4N5MT\ns2zfU0Ig1ZuQ+JKJYRjsv91ab/jgSy1X3K8mqxIDgzOZ1sUva6iToTG5ECaRzVhz2rYAZ5VSP1FK\nvUMpJZVMk4AZW5ZIUh0xX6lbqjQvRURrfQxAa/0skG5zPCJBgu1SsMpO3tIyiA0rF4kRr4TXqbWO\nXLwxtk16Q1ax6WAYX28bAPVZIQC2ZFdddt9IOMrR19pwuR3sunH9XlSLN2RSUp5Je9MQPR2jl90n\nxZ3ChvRSTrr6iXh8lE73UtcxkuBIxZVorU2t9bNa6/cDpVjLEv0x0KmU+jt7oxPnScq73jmkh3c5\nvEqprbHHNsC34Oetdgcn4meuh1cSXnt4y2KFq9rbbI5k/YhXwutTSl0yWVMplQp44nROkQDN3WOU\nTPcRdTg44hmgMFBAli/zsvuePdHN5Pgs23aX4E9Z3//s+2LDua/Wy7slu5ooJsGyQrLCE7TUS8tf\nMorNcfsK8DdAO1LN1HbmfNEqsd7JHN5l8QM/iT1+fNHPP7ExLhFnwY52DI8Hd16e3aGsS7I0UeLF\nazjeY8DXlFK/obUeBVBKZQJfBr4bp3OKBGhs7qcqOESwqIAZR/iqvbtHXm3D5VrfvbtzisoyKa3I\noqNlmO6O0csuzVSTVcWTLU8zXJJCSiNMntPwthtsiFZciVKqBvgg8GtAF1bi+y1bgxILSMq73s3P\n4ZUhzYvSWlfYHYNIPDMcZranG195OYZD1i63g6eoGJxOqdScQPFKeP8a+CrWcL/62LYq4HHgr+J0\nTpEAQ+fqcGAyVpIOjLAlW112v3Mne5gcD1K7r5SUwPru3Z1zw20VdLQMc/TVNoreueOS5zdmbMDj\n9HAmY5oSINDXxuRMiIDPnfhgxQWUUh8GPgBUAt8E3qi1PmFrUGIBSW6ERZamEuLqZnu6IRKR4cw2\ncrjdeAqLmO3swIxGpeEhAeKS8GqtQ8D7lFJVwO7Y5qNa6/qrvEwkuWjUhDZreZ3G7BAuw0lV5sbL\n7nf0tTac0rt7gaLSDApL02ltHGSwf4KcvAvX5XU5XFRnbuLM7Fnudboone6loWOU2spcmyIWC/wy\n8Dngca31rN3BiAvNpbuS6ghZh1eIq5uv0CwJr628paXMdnYQGhzAk5dvdzhrXlwrjMYS3GtKcpVS\nZcB/APlY/3P9i9b6H1YwPLFMXQOTFE70AHAydZzNmdV4nJf23jbX9TM+OsPW3cWkrMPKzFez+8YN\n/LTjFMcOtHPvW7Zc8nxNdjWnBs8xWZRLXkcPpxu7JeFNAlrrN13vMa50TVNKZWNNAykHWoCHtNZS\nsWw55nMbSXnXu/keXhnSbKvYNLZ/A7Zh/YV+EOt+UK51Ngt2xCo0y5JEtvKWljF+4DVmO9ol4U2A\nZO5DDwF/rLXeBtwE/K5S6tIMQSRMffsQxTP9TGVmMONzsOUyyxGZpsmxA1brYe0+uZherLwyh6zc\nFBrO9DE+eumyQzWxOdGDxX4MYPSsTnCEIo6udE37GPCU1roaeDr2s1gG05SiVcIiVZqTxt8DT2it\ntwA7gXPItS4pzFdoloTXVnNDyucaIER8JW3Cq7XuWbA+3ARwFii2N6r1redMA14zzHCxNRT3cglv\nW/MQfd3jbKzOJTM7JdEhJj3DMNi1v4xo1OTEwUsvcoUp+WR6MzibMQ2Au7OZUFhW8loLrnBNKwEe\nBB6N7fYo1vBpsQznO3gl2Vnv5np4TenhtY1SKgO4XWv9FQCtdThWwFSudUkg2NGOKysbZ2rq4juL\nuJlfmkgKVyVE0ia8CymlKrDmAh+wOZR1bbapAbDm76Z5UilJLbpkn1efawRg136ZG3IlVdsKCKR5\nOXO8i+BM6ILnDMOgJquK5swwUcOgZKqX1t5xmyIV8XLRNa1Aa90be6oXKLArrtVLkhthkTm8SWEj\n0K+U+qpS6ohS6l9jS1XKtc5mkYkJIiMjeEuld9duzoxMHKmpkvAmSNInvLG1e78H/GGsV0TYYHQi\nSOZwJwCN2RFUVuWCGwvL8OAkdad7KShJp/Ayy+4Ii9PpYOcNJYRDUc4e777k+ZrsKkJuBxO5mRQG\nB2ls6bchShEvsWva97GuaRe0ZmitTeROfdnOr8MrPbzrncNI+tua9cAF7AG+qLXeA0xy0fBludbZ\nQwpWJQ/DMPCWlhHq6yMaDNodzpoX16JV10sp5ca6MfyG1vqHi+2fl5cW/6CSPIZ4nb+ua4zS6T6C\nfh9jAQd7yrZdcq7Xn28G4I77qtfs+7BSMdx2TxWHXm7l7PFu7n1gKw7H+Rv1W9N28bUz32awxE96\n/zBj9Q3k/fLuKx7rWmNIlGSIIVksuKZ9fcE1rVcpVai17lFKFQF9ix0nGd7TZIohEPDCALjdzoTG\nlUzvgcRgxTA3nxvDTHhcyfA+JIkOoENrfTD28/eAjwM9cq2zN4au16wG9LytVcs65lp6D5IphvGq\nTUyfO0vK5BBppZdOE0xEDNciGWJYrqRNeJVSBvDvwBmt9eeX8pr+fnuHfublpdkaQzzPf/rQObZF\npukuzAXDoNhVesG5gjMhjh9qJzPbT3ZBYM2+DysZQ9XWfM4c6+bQqy1srF5YidmgJLWIsxltbARm\n6+vo6xu7pEd9JWKIN7tjSKaL8lWuaY8DDwOfjX1dtHFvvf+7XhzDxKRVAC4ciiYsrmR7DySGS2NI\nZFx2vw/JdK2LJbTtSqlqrXUdcB9wOvaQa52NMQyes6amBdNzl3zMtfYeJFMM0RxrVH/PKc1M9qXT\nBBMRw3IlSwzLlbQJL3Ar8D7ghFLqaGzbx7XWT9oY07o1qesAaMmNkuXNJteffcHz+mQv4VCUvTdX\nXNBbKa5s+54Szhzr5uThjosSXqjJquLlXGsIed5YFwOjM+Rl+u0IU6ycy17TgM8A31FKfYjYUh32\nhLeKycBIsZBpIB8K2/0+8E2llAdoxFqWyIlc62wV7GgHpxNPQaHdoQjOV2qelXm8cZe0Ca/W+iVW\nwRzj9SAUjuDtaQWgJcekOmvzBb2Npmly6mgnTqfB7v1lTE7P2hXqqpKTn0rxhkw6W0cY6p8kOy8w\n/1x11mae9r7AeGYqxaMDNLQNkZdZYmO04notck27L5GxrDXmfHIjjW0iRqo020prfRzYd5mn5Fpn\nEzMaZbarE29xMYYraW//1xVPcQkYhixNlACSUIpFNXePUzLVR9jlpD/TRXXW5gue72wdZnRoms1b\n8klJ9doU5eq0Y6+VxJ480nnB9srMjTgMB32FXjxmmO4zdXaEJ8SqIqsSCYt8EIS4WKi/D3N2VgpW\nJRGHx4OnoJBge9v5+gMiLiThFYtqbuwmNzTKYH4qpsO4JOE9dbgLsIboiuWpqMohNd1L3akegjPh\n+e0+l4/ytFIasqxtsw31doUoRNIzZfiquIAhPbxCXGSuQrNXEt6k4iktIzo9TXhoyO5Q1jRJeMWi\nhs+cA6A9F3L9OWT7suafGx+doaVhgLzCVPKLkqdoxmrhcDjYuquYcChK/ZneC56rytpMZ7417CjQ\n38HMbPhyhxBi3Tuf2kjPnpBcV4jLmRs2KwlvcplbE1nW440vSXjFVZmmCe1NALTlGlRnXti7e+Z4\nF6Zp9e4ut4qwsNTsLMQwuGRNXpVVyXjAyVSKl9KZPpo7R22KUIhkJ+vwiotJ1ivEQvM9vCWlNkci\nFpprgJCEN74k4RVX1Tc8Tf54D1HDoCfHhVownDkajaJP9ODxOtm8Jd/GKFe3QKqX8socBnon6O85\nX+p9U0Y5TsNJX4GPQGSG1rNNNkYpRPKam/sk6a6wyCdBiIvNdnTgTE3DmZFhdyhiAW+ZVGpOBEl4\nxVXVtwxQNDPIcLafkNtB1YKEt71pmMmJWaq2FuB2O22McvXbuqsYgDPHuua3eZweNmZsoDk3CsDE\nOSlcJcTVSaIjhBAXi87MEOrvw1NaKqPxkowrOweH30+wXRLeeJKEV1xV72mNkygduQaFKflkeNPn\nnzt7whqCu6V28cWyxdWVbcwmNd1L/Zk+Qgvm6lZnbqYzzw2Aq6uZqFTxE+JSsT8LuZET82QirxDz\ngl3WShBz80VF8jAMA29pGbO9PURnZVnPeJGEV1xVqKkBgI485wXVmacmZ2ltGCQnL0BuQapd4a0Z\nDodBzY5CQrMRGs72z2+vzqpkMMNJ0O2iaLKX7sEpG6MUIjlJlWZxIWn4EGIhmb+b3DylZWCazHZ1\nLb6zuCaS8IormpwJkT5oVfXrynNTnVU5/1zdqV6iUZOa2iLpVVkhNTutnvKFxasqMjbgdrrpy/eR\nFRqnuU6GvAhxMUl4hRDiymalQnNSk8JV8ScJr7iixo5hSmf6GEv1MOV3UpW5CbAKxJw72Y3DaVC9\nrcDmKNeOtAwfGzZl09s1xvDAJABuh4vNGRtpy7Vu6AdPnbUzRCGSlFRpFgsZSJVmIc4LdrSDYeAp\nLrE7FHEZsjRR/EnCK66o/XQDvmiIzjwnJalFpHoCALGEbIqNVbn4/G6bo1xb1I5CAPTp82vyWuvx\nWu+z2dpoS1xCJDNJbYQQ4vJM0yTY2YE7Px+H12t3OOIyvCWlYBiS8MaRJLziiia1VRW4M891wfzd\ncyd6AClWFQ8VlTl4vE7qTvXOL7WisjbTl+0m7DDIHuliYjpkc5RCJBdzvpib9PCKWL0q+SgIAUB4\nZITo5KQMZ05iDp8Pd14+wY72Bf+fiZUkCa+4rHAkirenDbDm76rY/N1wOELjuT4CaV5KyrPsDHFN\ncrmdbK7JZ3I8SGfrCAAb0kpxebz05/jIDw7T2NS7yFGEWJ8kxxEWGdIsxJzZTilYtRp4S0uJTkwQ\nGR2xO5Q1SRJecVntfRMUT/Uy43Eyku6iMnOjtb1piNlghMot+TgccnsZD9XbrXnRdaesnnSnw0ll\n5iY68gwcmPScOGNneEIknfP9u3JNEkKIhYLtVsEqj/TwJjUpXBVfkvCKy2o+10pGeJKufBcb0svw\nu/wA1J/pA6Bqa76d4a1pRaUZpGX4aKobIDQbAaB6wTze2YZ6O8MTIglJb54QQlxOUHp4V4W5Bom5\nBgqxsiThFZc1cuYcAJ157vn5u6HZMK0Ng2Rk+WXt3TgyDKv6dWg2QnP9AGAlvN25bkwgpb+NcCRq\nb5BCJCPp4BWADGkW4rxgRweGx4M7L8/uUMRVnO/hbbM5krXJZXcAIjkZ7c0AdOW6uTmW8DbXDxIO\nR6ncmi9r78ZZ9fYCDr/SSt2pHqq3FVCaWowrJcBg5hiFowO0d4+wsTTb7jCFSAqmLEskLiYfBVsp\npZzAIaBDa/1WpVQ28BhQDrQAD2mtZbJinJnhMLPdXfg2lGM4LuzjippRwtEwDsOB03DKfV2cmaaJ\niTn/1cDA6XDOP+/OzcXw+gh2SA9vPEjCKy4xODpD3lg3YafBQI6HTRkVADScjQ1n3iLDmeMtMzuF\ngpJ0OlqGmRwPEkjzUpW5ic68PnJHZmk/dpaNpbfaHaYQSUGKWgqRdP4QOAOkxX7+GPCU1voRpdSf\nx37+mF3BrRczPV0QiTCQ6eLp099iYHqIwZkhZsJBQtELV3xwGA5SXH5S3H4CrhRS3CmkugMUZeXi\njvjI9KST4U0n05tBuiftgmRtPZuNzDI2O249guOMLvje2j7G2OwEY7PjRM0LR+f5nD4Cbj+Z3kyK\nUgvYmZ+B2dlFNBTC4ZZlP1eSJLziEg2N3eTNDtOZ72ZDVjk+l5eZ6RDtTUPk5AfIyg3YHeK6UL2t\ngN7OMerP9LHrxjKqsyo5ln+Y2vppJrSGt0jCK4RFliUS50lPv72UUqXAA8CngI/GNj8I3Bn7/lHg\nOSThjZvR4DjPdbxE/yvPcwdwyNHF8d4RnIaTLF8mWd5MfE4vLoeLqBklYkYIR8NMh2eYDE8xMD10\nPjnrufT4BgapnsB8Epwe+5rhTSfDkzb/fZo7dVUmxpFohInQ1PlEdnac6MAsXUP9F2wbC04wE5m5\n6rHcDhfpnjTK00pxOz0YWO9fxIwwFZ5mMjRF02gLjaPNeLxj7IhG+dIv/padO+/mxsK9eJyexPzS\na5wkvOISfSfPkoW1HNHc/N3mugGiUZNK6d1NmM01ebz0VD2N5+YS3s08kWe1+Lm7WuwNTogkJImO\nOE+6/W30d8CfAukLthVorefW1OsFChIe1ToQjob5Wcsz/KLteWajIe4YsJKxG2rfwK/svoVsXxYO\nY/HyPaZpEozMMhGawPCHaentYTQ4ysjsGKNB6zESHKVnqp/2ia4rHsfAIM2TOp8Ip3vSCbhTLuhF\nDrj9pLisbX6XD4/Ds6JJcigSYjoyw3R4hunwdOyr9f347CQTsd7X8dAk47PjjM9OMBmamp8qc6Xf\nK9UdIMefRbon7fzDa33NWPCzz+lbdLh4KBKiZ6qfnpGfQMPLmF29/Kf3B/ys5VneXvkAe/JrZcj5\ndZKEV1wi1NwAWAnvvkwr4a0/Y/0/JQlv4vhTPJRuzKa9aYjR4WmKMgtwZGQwGhglf7yHgeEpcrNS\n7A5TCNtF5+fwCiHspJR6C9CntT6qlLrrcvtorU2l1JJaJPLy0hbfKc5WSwz9k4N8/uUv0zzcTpY/\ng/dvfYBi/QKjHOWeu9+KO/1afo9cAGryKi/7rGmaTIdmGJoZYWR6lKHpUUZmrK/DC77vneqjfbxz\nyWd1Gg48Tg8elwev043H6cHlcFqNmoaVcBoYGIbVzBk2I4QiYULRMOFImFA0RCgaJhQJE46Gl3ze\ngCeFDF8aZZnFZHjTyPSnk+lLJ9OXEfuaTpY/g3TvyvdcF5PN2O1RTj7xMu/OvpHDNfk8UfcMXzn9\nLRomG/nNve8FVs/nMdlIwisuMB0MkzHQgQn05/vZlFHO1ESQrrYRCorTSc/02x3iulK5JZ/2piEa\nzvax95ZyqrM205XXxZaWGZpP1pN7R63dIYok851nGjh4rm9Fj7mvJp+H7rn8DQ/AP//zF8jPL+BX\nfuWdAPz7v3+ZlJQA73nP+y7Z95/+6e85cOAVDMPg/e//EPfeez8A3/jG13jqqScxDAc33XQLH/nI\n7y0jQhnSLBYywJAeXpvcAjyolHoA8AHpSqmvA71KqUKtdY9SqghY0kWqv388jqEuLi8vbVXE0DnR\nzT8d+zdGZ8e5qfAGfrX6QfwuH03NX8eZmclIELjG32Mp5/eSSoEjlYJACVxm1ptpmsxEZhgNjjMV\nnmYqNMVkaGp+SO/ctunwDKFoiNlIyPoanSUUDjMVGiMcCVtFn6wDxq76VhEoh8OJ23DhcjhxOdz4\nnSmkuZ24HW58Ti9+ly/2sHqRfbGf0zyp1sNtfXU5rpwWzb0P4QkYmpi6pvdyMZGAVYx0prmTN7z9\nvezO3MXXTn+bF1oO0Dncy/++9w+YHFl6Ah8PyfI3sVyS8IoLNLUPURQcoD/LRWnuRtxON+d0B6YJ\nlbL2bsJtrMrleadxQcJ7Mv81trTMMHT6LEjCK5LAvffez9///efmE95nn32av/u7L1yy33PPPU1D\nQx2PPvqfjIwM8xu/8X527dpNXZ3m5Zdf4F/+5VG8Xi9jY2PXFIeM+BJArP1DEl47aK3/F/C/AJRS\ndwJ/orX+NaXUI8DDwGdjX39oX5Rry+D0EP947F8Zn53gHZVv4Z4NdwAQmZokPDREyrbtNkdoLbdo\nJZvX1mmSDElWIjhTUnDl5hLssNZOzk/J5Y/3/jb/ceY/OdJ3gr95/gv89vYP4XZKQavlkoRXXKDz\nxDkqzChdeV6qs6wenYYzfRiGNadUJJbX56J8Uw7N9QMM9k9QnbWZJ2PzeGlttDc4kZQeuqfyqr2x\n8VBVpRgZGWZgYIDh4SHS0tLIy7u0gezkyePcf/8bMQyDrKxsdu3aw9mzZzh27AhvfvODeL1eANLT\n0y957dWYUqZZLBRr+DBNU+a92W/uj/MzwHeUUh8itiyRbRGtITPhIF88/hXGZyd4Z9XbuKvsfDHL\nueVt5tZ3FauDt7SMyWNHCY+O4srIwO1w8YGt78HA4HDfcb6lv8/7t7xLrm3LJAmvuMBUnQas+btv\nzdrM+OgMPZ1jlJRnEkj12hzd+lS5NZ/m+gEaz/az7/YKzLxspj0jZI90MTUTJsUnf8bCfnfffR/P\nPfcLBgcHue++X7rifldKTlciZ5WiVQIAUz4HyUBr/TzwfOz7IeA+eyNae75f/zg9U31Z4t7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PJjyCs/BmOAERX318RpZSz+bxWxbdlsGuFhzpogVYs/ZvTZxyd0PkIMxvjxEwgGOygrK+/psrw7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bDv9Mw+z824JjX51IYJR9giebYeOdjTPYeWq3uf228/7DBfU6uyzWHZuPsOQn7dQXFOzP0BFd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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e22041240>"
+       "<matplotlib.figure.Figure at 0x7f77baf18990>"
       ]
      },
      "metadata": {},
@@ -8031,1222 +4422,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
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-       "ncR23PebsptsSbKbZH8p6xTH67XjZL2bxHHiEne5K7Ikq0uogHQBAaKIMvQyA0y5vz8GsGR1CbgD\n",
-       "vB/ncDTce5n7GgbNzPt+mmIYBkIIIYQQQgghxHilmh1ACCGEEEIIIYS4FFLYCiGEEEIIIYQY16Sw\n",
-       "FUIIIYQQQggxrklhK4QQQgghhBBiXJPCVgghhBBCCCHEuCaFrRBCCCGEEEKIcc1qdoAhmqbtBToH\n",
-       "v63Udf1LZuYRQohLpWnat4CPATbgV8BW4CkgBBwC7tV1XdZcE0JEPE3TbMBvgSzAAXwfqANeBcoG\n",
-       "D/svXdef1TTtK8CdQAD4vq7rr5kQWQgxyUREYatpmhNA1/U1ZmcRQoiRoGnaauAyXdcv1zQtCvgm\n",
-       "8HHg27quv69p2mPAzcCLJsYUQojzdRvg0XX9C5qmJQD7ge8B/6nr+sNDB2malg7cDywCXMAWTdPe\n",
-       "1nV9wIzQQojJIyIKW2Ae4NY07U3Cmb6t6/oOkzMJIcSlWAcc1DTtRSAW+AbwJV3X3x/cv2HwGCls\n",
-       "hRDjwbPAc4O3VcBPuHjVNE27GSgHHgKWAlt1XfcDfk3TKoC5wO6xjyyEmEwiZYxtL/ATXdevBe4G\n",
-       "/qBpWqRkE0KIi5FC+EPfJwm/rj0NKCfs7wHiTMglhBAXTNf1Xl3XezRNiyFc5P4TsBP4uq7rq4BK\n",
-       "4DtADB8OLQPoRl7rhBBjIFJabMuACgBd18s1TWsFpgD1pzvYMAxDUZTT7RJCTF6R9qLQAhzWdT0A\n",
-       "lGma1gdMO2F/DNBxtjuQ1zohxBmY8sKgaVoG8ALwqK7rz2iaFqfr+lAR+xfgEeB9wq9vQ2KA9rPd\n",
-       "r7zWCSHO4IJeGCKlsL2dcDeVezVNm0q4217DmQ5WFAWPp3ussp1WSkqMqRnMPr9kkAyRliElJebc\n",
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-       "IYNgyCAUMgiEQgSD4dvBk75C4W1Bg8DgsSHDwDAMDIPBr8HbhP91OG30+QawWlVsVhW71YJt8Ha0\n",
-       "00ZMlI1Yt53YqPCXOgqFymT/ezwxw1jTNC0NeAu4R9f1jYOb39A07QFd13cBVxPubrwT+IGmaQ7A\n",
-       "CRQSnizvjOS1TjJEyvklQ+RluBCRUtj+BvgfTdOGxp7drut6yMxAYvxoavey9WAjek07zR0+/P4Q\n",
-       "cdF2stNjmD8jhQUzkrFapGe7GFu6rr+madpKTdN2Eh72cQ9QDfxa0zQ7UMqH49VEBOjo6edofRfV\n",
-       "jV0ca+ymqd1La2c/IUMmrr5QdqtKaoKbKUluMlKjmTE9jpwpsdhtFrOjiYv3bcJdiv9V07R/Hdz2\n",
-       "EPAzTdP8hBsk7hzsrvxLYDPh175vy8RRQoixEBGF7WBXvS+YnUOML21dfTz1ps77+8I91hUFUuJc\n",
-       "RLtstHf1s62kiW0lTSTFOvjk6nyWFqYiXZ3EWNJ1/R9Os3n1WOcQZ9bQ2sv2kib2H22hpqnnpH1x\n",
-       "UXZyp8WSkRaD06oS7bYR7bIR7bRht4VbK60WdfDf8GtLuIUzfGPo9lDLZ8gYbBUNDd4OhVtIQ8aH\n",
-       "34f/ZXD7h8dHRTvo7PQNtqqeeDwn3E94n0VVwl8WFXXo9onbFAWLRcGqKifsDx+rDLYuKwzd/vDf\n",
-       "pMQo2tp6CQRDwy3W/kCQAX+Inj4/3b0DdHn9dPYO0NzupanNR52nh11HmoFwroLMeBYVpLJoZgox\n",
-       "bvvYPtnikui6/iDhXigfteI0xz4JPDnqoYQQ4gQRUdgKcaH26B5++3opvv4gWekxXLskg3n5ybgc\n",
-       "4T9pwzCobe5h84EG3t9/nMdfLmHn4Sa+vH7W8DFCiMkpZBjs1T28s7uWsrrw8ECLqjArO4HCrARy\n",
-       "psSSnR6D22kDIqc7ViRkcF5A5xfDMGjv7qeqoYvyuk6O1LRTUh3+evrtMpYVpnHNkgwy0yJuGIEQ\n",
-       "QohxSD7hi3HnlQ+q+cv7lditKvfdOo/5uYmnjOVSFIXMtBhuuyaGaxZP56kNR9hX3sL3/283f/+p\n",
-       "eSTHuUxKL4QwU0lVG89urKCmOdw6Oys7gSvnTmVuXpJc9BphiqKQGOskMdbJIi0VgJYOH7t1D5v2\n",
-       "H2froUa2HmpkaWEqn1iVR0q8vC4LIYS4ePIuLsYNwzB4cXMVr3xQTVKskwc/OZcFRVNOasUwDAOC\n",
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VVdUMbAOqNE37jNF5xODTdZ0tBxqY0n0Uxe8nfsGiU7qvrKveQF1XPYuy5pHn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REI4lBW8xcTUhhWFT1hOSHMPgkxnLfFYe/TIBdRUX4iN5658JRn/vWj25vVrXQo8jIT6ZBTM3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\n7q21PqmU+g/WdbsSgEeAhpnaj2q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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e2167ce80>"
+       "<matplotlib.figure.Figure at 0x7f77ba853450>"
       ]
      },
      "metadata": {},
@@ -9257,7 +4435,7 @@
     "temps = pvsystem.sapm_celltemp(irrad_data['GHI'], 10, 5)\n",
     "\n",
     "sapm_2 = pvsystem.sapm(sandia_module, irrad_data['DNI']*np.cos(np.radians(aoi)),\n",
-    "                     irrad_data['DHI'], temps['tcell'], am, aoi)\n",
+    "                     irrad_data['DHI'], temps['temp_cell'], am, aoi)\n",
     "\n",
     "plot_sapm(sapm_2)"
    ]
@@ -9272,7 +4450,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e1a2c1940>"
+       "<matplotlib.text.Text at 0x7f77ba87e850>"
       ]
      },
      "execution_count": 26,
@@ -9281,416 +4459,9 @@
     },
     {
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DVFZWkpyczM9+9lC/g7yUlBTz6KO/paysjJCQENLS0rnvvu8QHR0zrBgKCg7x4IM/obu7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\netRTxC1kch1wnYhUeU9vw82qTvmWumVKAyvK+aKcLSXK2x4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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e20065ef0>"
+       "<matplotlib.figure.Figure at 0x7f77b38e2650>"
       ]
      },
      "metadata": {},
@@ -9698,8 +4469,8 @@
     }
    ],
    "source": [
-    "sapm_1['Pmp'].plot(label='30 C,  0 m/s')\n",
-    "sapm_2['Pmp'].plot(label=' 5 C, 10 m/s')\n",
+    "sapm_1['p_mp'].plot(label='30 C,  0 m/s')\n",
+    "sapm_2['p_mp'].plot(label=' 5 C, 10 m/s')\n",
     "plt.legend()\n",
     "plt.ylabel('Pmp')\n",
     "plt.title('Comparison of a hot, calm day and a cold, windy day')"
@@ -9735,11 +4506,11 @@
     "def sapm_to_ivframe(sapm_row):\n",
     "    pnt = sapm_row.T.ix[:,0]\n",
     "\n",
-    "    ivframe = {'Isc': (pnt['Isc'], 0),\n",
-    "              'Pmp': (pnt['Imp'], pnt['Vmp']),\n",
-    "              'Ix': (pnt['Ix'], 0.5*pnt['Voc']),\n",
-    "              'Ixx': (pnt['Ixx'], 0.5*(pnt['Voc']+pnt['Vmp'])),\n",
-    "              'Voc': (0, pnt['Voc'])}\n",
+    "    ivframe = {'Isc': (pnt['i_sc'], 0),\n",
+    "              'Pmp': (pnt['i_mp'], pnt['v_mp']),\n",
+    "              'Ix': (pnt['i_x'], 0.5*pnt['v_oc']),\n",
+    "              'Ixx': (pnt['i_xx'], 0.5*(pnt['v_oc']+pnt['v_mp'])),\n",
+    "              'Voc': (0, pnt['v_oc'])}\n",
     "    ivframe = pd.DataFrame(ivframe, index=['current', 'voltage']).T\n",
     "    ivframe = ivframe.sort('voltage')\n",
     "    \n",
@@ -9764,7 +4535,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -9776,28 +4547,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Isc</th>\n",
-       "      <td> 3.943111</td>\n",
-       "      <td>  0.000000</td>\n",
+       "      <td>3.943111</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ix</th>\n",
-       "      <td> 3.850701</td>\n",
-       "      <td> 25.734151</td>\n",
+       "      <td>3.850701</td>\n",
+       "      <td>25.734151</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pmp</th>\n",
-       "      <td> 3.508231</td>\n",
-       "      <td> 40.603933</td>\n",
+       "      <td>3.508231</td>\n",
+       "      <td>40.603933</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ixx</th>\n",
-       "      <td> 2.559264</td>\n",
-       "      <td> 46.036117</td>\n",
+       "      <td>2.559264</td>\n",
+       "      <td>46.036117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Voc</th>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td> 51.468302</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>51.468302</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -9831,7 +4602,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f9e1a3aecc0>"
+       "<matplotlib.legend.Legend at 0x7f77b3964ed0>"
       ]
      },
      "execution_count": 30,
@@ -9840,839 +4611,9 @@
     },
     {
      "data": {
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-       "oigFiqIYrsXxBZqiKJmKoiQ3UdvLFUVJ9y5PUxRlhHd5uqIooy9Rd6yiKOsb2V9fRVHe8C73URTl\n",
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hnemYEHG+d69Wcv1an717v+OBB+5BURScTs9sDNOnP8Dw4SMpKSlm8eKF5OfX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\nYHKZn7y7m9dmvsPrz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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e1a2c6f28>"
+       "<matplotlib.figure.Figure at 0x7f77ba89efd0>"
       ]
      },
      "metadata": {},
@@ -10723,12 +4664,13 @@
    },
    "outputs": [],
    "source": [
-    "IL, I0, Rs, Rsh, nNsVth = pvsystem.calcparams_desoto(S=irrad_data.GHI,\n",
-    "                                                     temp_cell=temps['tcell'],\n",
-    "                                                     alpha_isc=cecmodule['Alpha_sc'],\n",
-    "                                                     module_parameters=cecmodule,\n",
-    "                                                     EgRef=1.121,\n",
-    "                                                     dEgdT=-0.0002677)"
+    "photocurrent, saturation_current, resistance_series, resistance_shunt, nNsVth = (\n",
+    "    pvsystem.calcparams_desoto(irrad_data.GHI,\n",
+    "                                 temp_cell=temps['temp_cell'],\n",
+    "                                 alpha_isc=cecmodule['Alpha_sc'],\n",
+    "                                 module_parameters=cecmodule,\n",
+    "                                 EgRef=1.121,\n",
+    "                                 dEgdT=-0.0002677) )"
    ]
   },
   {
@@ -10741,7 +4683,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f9e20198240>"
+       "<matplotlib.text.Text at 0x7f77b3ba1250>"
       ]
      },
      "execution_count": 32,
@@ -10750,279 +4692,9 @@
     },
     {
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CXK6cU+13OFmppqKERTOr2LG/nYOaFichouIuElBrG5o41dnLtQtqyMvN8TucrHXHtdMA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\nN0e3mamqqiyy+Y1EbolEkmc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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e1a36a908>"
+       "<matplotlib.figure.Figure at 0x7f77b381db90>"
       ]
      },
      "metadata": {},
@@ -11030,8 +4702,8 @@
     }
    ],
    "source": [
-    "IL.plot()\n",
-    "plt.ylabel('Light current (A)')"
+    "photocurrent.plot()\n",
+    "plt.ylabel('Light current I_L (A)')"
    ]
   },
   {
@@ -11044,7 +4716,7 @@
     {
      "data": {
       "text/plain": [
-       "(0, 100)"
+       "<matplotlib.text.Text at 0x7f77b8041f10>"
       ]
      },
      "execution_count": 33,
@@ -11053,259 +4725,9 @@
     },
     {
      "data": {
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pIhmxbd8xEgkid9XeY8mcKh5YtZdNu5sj1+0gMhSpFPdrgFuADwE/M7Oeq/8E8Chwh7tr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\nEZGIUXEXERGJmP8PQzNQFEdQ6SwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e200c3b00>"
+       "<matplotlib.figure.Figure at 0x7f77b801d790>"
       ]
      },
      "metadata": {},
@@ -11313,11 +4735,99 @@
     }
    ],
    "source": [
-    "Rsh.plot()\n",
+    "saturation_current.plot()\n",
+    "plt.ylabel('Saturation current I_0 (A)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.094"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resistance_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 100)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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TvoEh7ntKy+Lk0G3c1snKDbs4al4DUxuqow5HYiCX4v4+4EYzazOzlvBna74D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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f77b816cad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "resistance_shunt.plot()\n",
     "plt.ylabel('Shunt resistance (ohms)')\n",
     "plt.ylim(0,100)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f77b3a8f150>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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B+4F/SWdQInGwavN+WtvamWVVUYciWUaj+yWdulL4E+5+HPg48K/ufiswK71h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\nEZEG+QM7Udwd1wDOQwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f77b33d1050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nNsVth.plot()\n",
+    "plt.ylabel('nNsVth')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -11327,7 +4837,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pvsystem = reload(pvsystem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -11335,692 +4856,320 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>Rsh</th>\n",
-       "      <th>Rs</th>\n",
-       "      <th>nNsVth</th>\n",
-       "      <th>I0</th>\n",
-       "      <th>IL</th>\n",
-       "      <th>VH</th>\n",
-       "      <th>VL</th>\n",
-       "      <th>V1</th>\n",
-       "      <th>V2</th>\n",
-       "      <th>f1</th>\n",
-       "      <th>f2</th>\n",
-       "      <th>SW_Flag</th>\n",
-       "      <th>Imp</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Vmp</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Ix</th>\n",
-       "      <th>Ixx</th>\n",
+       "      <th>i_mp</th>\n",
+       "      <th>i_sc</th>\n",
+       "      <th>i_x</th>\n",
+       "      <th>i_xx</th>\n",
+       "      <th>p_mp</th>\n",
+       "      <th>v_mp</th>\n",
+       "      <th>v_oc</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:00:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:00:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:01:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:01:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:02:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:02:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:03:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:03:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:04:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:04:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:05:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:05:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:06:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:06:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:07:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:07:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:08:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:08:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:09:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:09:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:10:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:10:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:11:00-07:00</th>\n",
        "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:11:30-07:00</th>\n",
        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:12:00-07:00</th>\n",
        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:12:30-07:00</th>\n",
        "      <td>NaN</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:13:00-07:00</th>\n",
        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
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+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:13:30-07:00</th>\n",
        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:14:00-07:00</th>\n",
        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 00:14:30-07:00</th>\n",
        "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -12031,895 +5180,393 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:45:30-07:00</th>\n",
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-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:46:00-07:00</th>\n",
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        "    <tr>\n",
        "      <th>2014-04-01 23:48:00-07:00</th>\n",
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        "    <tr>\n",
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        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:51:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:52:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:52:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:53:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:53:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:54:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
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-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:54:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:55:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:55:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:56:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:56:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:57:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
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-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:57:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:58:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:58:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:59:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-01 23:59:30-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-04-02 00:00:00-07:00</th>\n",
        "      <td>NaN</td>\n",
-       "      <td> inf</td>\n",
-       "      <td> 0.094</td>\n",
-       "      <td> 0.441271</td>\n",
-       "      <td> 5.330515e-11</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0</td>\n",
-       "      <td> 0.022756</td>\n",
-       "      <td> 0.014064</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td> False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td> 0.019739</td>\n",
-       "      <td> 0.022756</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.022756</td>\n",
+       "      <td>0.019739</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2881 rows × 19 columns</p>\n",
+       "<p>2881 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                           Isc  Rsh     Rs    nNsVth            I0  IL  \\\n",
-       "2014-04-01 00:00:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:00:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:01:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:01:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:02:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:02:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:03:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:03:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:04:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:04:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:05:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:05:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:06:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:06:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:07:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:07:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:08:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:08:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:09:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:09:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:10:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:10:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:11:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:11:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:12:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:12:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:13:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:13:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:14:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 00:14:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "...                        ...  ...    ...       ...           ...  ..   \n",
-       "2014-04-01 23:45:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:46:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:46:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:47:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:47:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:48:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:48:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:49:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:49:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:50:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:50:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:51:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:51:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:52:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:52:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:53:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:53:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:54:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:54:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:55:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:55:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:56:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:56:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:57:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:57:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:58:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:58:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:59:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-01 23:59:30-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
-       "2014-04-02 00:00:00-07:00  NaN  inf  0.094  0.441271  5.330515e-11   0   \n",
+       "                           i_mp  i_sc  i_x  i_xx  p_mp      v_mp      v_oc\n",
+       "2014-04-01 00:00:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:00:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:01:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:01:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:02:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:02:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:03:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:03:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:04:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:04:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:05:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:05:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:06:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:06:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:07:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:07:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:08:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:08:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:09:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:09:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:10:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:10:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:11:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:11:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:12:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:12:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:13:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:13:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:14:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 00:14:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "...                         ...   ...  ...   ...   ...       ...       ...\n",
+       "2014-04-01 23:45:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:46:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:46:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:47:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:47:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:48:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:48:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:49:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:49:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:50:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:50:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:51:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:51:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:52:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:52:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:53:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:53:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:54:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:54:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:55:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:55:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:56:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:56:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:57:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:57:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:58:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:58:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:59:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-01 23:59:30-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
+       "2014-04-02 00:00:00-07:00   NaN   NaN  NaN   NaN   NaN  0.022756  0.019739\n",
        "\n",
-       "                                 VH  VL        V1        V2  f1  f2 SW_Flag  \\\n",
-       "2014-04-01 00:00:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:00:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:01:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:01:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:02:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:02:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:03:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:03:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:04:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:04:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:05:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:05:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:06:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:06:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:07:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:07:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:08:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:08:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:09:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:09:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:10:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:10:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:11:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:11:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:12:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:12:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:13:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:13:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:14:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 00:14:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "...                             ...  ..       ...       ...  ..  ..     ...   \n",
-       "2014-04-01 23:45:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:46:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:46:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:47:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:47:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:48:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:48:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:49:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:49:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:50:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:50:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:51:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:51:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:52:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:52:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:53:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:53:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:54:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:54:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:55:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:55:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:56:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:56:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:57:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:57:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:58:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:58:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:59:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-01 23:59:30-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "2014-04-02 00:00:00-07:00  0.022756   0  0.022756  0.014064 NaN NaN   False   \n",
-       "\n",
-       "                           Imp       Voc       Vmp  Pmp  Ix  Ixx  \n",
-       "2014-04-01 00:00:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:00:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:01:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:01:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:02:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:02:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:03:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:03:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:04:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:04:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:05:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:05:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:06:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:06:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:07:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:07:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:08:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:08:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:09:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:09:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:10:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:10:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:11:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:11:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:12:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:12:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:13:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:13:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:14:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 00:14:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "...                        ...       ...       ...  ...  ..  ...  \n",
-       "2014-04-01 23:45:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:46:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:46:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:47:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:47:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:48:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:48:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:49:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:49:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:50:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:50:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:51:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:51:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:52:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:52:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:53:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:53:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:54:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:54:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:55:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:55:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:56:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:56:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:57:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:57:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:58:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:58:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:59:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-01 23:59:30-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "2014-04-02 00:00:00-07:00  NaN  0.019739  0.022756  NaN NaN  NaN  \n",
-       "\n",
-       "[2881 rows x 19 columns]"
+       "[2881 rows x 7 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sdDFOut = pvsystem.singlediode(module=cecmodule,\n",
-    "                               IL=IL,\n",
-    "                               I0=I0,\n",
-    "                               Rs=Rs,\n",
-    "                               Rsh=Rsh,\n",
-    "                               nNsVth=nNsVth)\n",
-    "sdDFOut"
+    "single_diode_out = pvsystem.singlediode(cecmodule, photocurrent, saturation_current,\n",
+    "                                        resistance_series, resistance_shunt, nNsVth)\n",
+    "single_diode_out"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
@@ -12927,264 +5574,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e1a2f9b70>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b3560a90>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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EteSs+EFFcS6Ty/LYe7SFvoEh13EkANTcRc7T0zfI/mOtTJuYT0lhtus4IoA3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\nCZn/H3Vb0NcFs8yaAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e200c34a8>"
+       "<matplotlib.figure.Figure at 0x7f77b8129ed0>"
       ]
      },
      "metadata": {},
@@ -13192,36 +5593,100 @@
     }
    ],
    "source": [
-    "sdDFOut.Pmp.plot()"
+    "single_diode_out['i_sc'].plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b33d1690>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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S+jHFPrlXjVcbJlJKqbNp9ZUfKK2fm/7+Nb2+zihOobmR4rQnKM55/hVwHLAc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\n1DCGuyRJDfP/AdFVcLyKKcxXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f77b39d9990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "single_diode_out['v_oc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b38ba610>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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qyhkYGsEe7nQdigSc8+JurT1hrd3q3z4F7ARmAPcBD/ovexB4h5sIJZPsOdJN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\niRgVdxERkYhRcRcREYmY/w8/2eV/Tyk4ogAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f77b347e550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "single_diode_out['p_mp'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/pvlib/atmosphere.py b/pvlib/atmosphere.py
index c5b40ebffb..4e6b4ae624 100644
--- a/pvlib/atmosphere.py
+++ b/pvlib/atmosphere.py
@@ -22,8 +22,8 @@ def pres2alt(pressure):
 
     Parameters
     ----------
-    Pressure : scalar or Series
-        Atomspheric pressure (Pascals)
+    pressure : scalar or Series
+        Atmospheric pressure (Pascals)
 
     Returns
     -------
@@ -100,7 +100,7 @@ def alt2pres(altitude):
 
 
 
-def absoluteairmass(AMrelative, pressure=101325.):
+def absoluteairmass(airmass_relative, pressure=101325.):
     '''
     Determine absolute (pressure corrected) airmass from relative 
     airmass and pressure
@@ -117,7 +117,7 @@ def absoluteairmass(AMrelative, pressure=101325.):
     Parameters
     ----------
 
-    AMrelative : scalar or Series	
+    airmass_relative : scalar or Series	
         The airmass at sea-level. 
 
     pressure : scalar or Series
@@ -136,13 +136,12 @@ def absoluteairmass(AMrelative, pressure=101325.):
 
     '''
 
-    AMa = AMrelative * pressure / 101325.
-
-    return AMa
+    airmass_absolute = airmass_relative * pressure / 101325.
 
+    return airmass_absolute
 
 
-def relativeairmass(z, model='kastenyoung1989'):
+def relativeairmass(zenith, model='kastenyoung1989'):
     '''
     Gives the relative (not pressure-corrected) airmass
 
@@ -155,17 +154,20 @@ def relativeairmass(z, model='kastenyoung1989'):
     Parameters
     ----------
 
-    z : float or DataFrame 
+    zenith : float or Series 
         Zenith angle of the sun in degrees.  
         Note that some models use the apparent (refraction corrected)
-        zenith angle, and some models use the true (not refraction-corrected)
-        zenith angle. See model descriptions to determine which type of zenith
-        angle is required. Apparent zenith angles must be calculated at sea level.
+        zenith angle, and some models use the true
+        (not refraction-corrected) zenith angle.
+        See model descriptions to determine which type of zenith
+        angle is required.
+        Apparent zenith angles must be calculated at sea level.
   
     model : String 
         Available models include the following:
         
-        * 'simple' - secant(apparent zenith angle) - Note that this gives -inf at zenith=90
+        * 'simple' - secant(apparent zenith angle) -
+          Note that this gives -inf at zenith=90
         * 'kasten1966' - See reference [1] - requires apparent sun zenith
         * 'youngirvine1967' - See reference [2] - requires true sun zenith
         * 'kastenyoung1989' - See reference [3] - requires apparent sun zenith
@@ -175,7 +177,7 @@ def relativeairmass(z, model='kastenyoung1989'):
 
     Returns
     -------
-    AM : float or DataFrame 
+    airmass_relative : float or Series 
         Relative airmass at sea level.  Will return NaN values for any 
         zenith angle greater than 90 degrees.
 
@@ -207,27 +209,38 @@ def relativeairmass(z, model='kastenyoung1989'):
     Sandia Report, (2012).
     '''
     
+    z = zenith
     zenith_rad = np.radians(z)
     
     model = model.lower()
     
     if 'kastenyoung1989' == model:
-        AM = 1.0 / (np.cos(zenith_rad) + 0.50572*(((6.07995 + (90 - z)) ** - 1.6364)))
+        AM = ( 1.0 / (np.cos(zenith_rad) +
+            0.50572*(((6.07995 + (90 - z)) ** - 1.6364))) )
     elif 'kasten1966' == model:
         AM = 1.0 / (np.cos(zenith_rad) + 0.15*((93.885 - z) ** - 1.253))
     elif 'simple' == model:
         AM = 1.0 / np.cos(zenith_rad)
     elif 'pickering2002' == model:
-        AM = 1.0 / (np.sin(np.radians(90 - z + 244.0 / (165 + 47.0 * (90 - z) ** 1.1))))
+        AM = ( 1.0 / (np.sin(np.radians(90 - z +
+            244.0 / (165 + 47.0 * (90 - z) ** 1.1)))) )
     elif 'youngirvine1967' == model:
-        AM = (1.0 / np.cos(zenith_rad)) * (1 - 0.0012*( (1.0 / np.cos(zenith_rad)) ** 2) - 1)
+        AM = ( (1.0 / np.cos(zenith_rad)) *
+            (1 - 0.0012*( (1.0 / np.cos(zenith_rad)) ** 2) - 1) )
     elif 'young1994' == model:
-        AM = (1.002432*((np.cos(zenith_rad)) ** 2) + 0.148386*(np.cos(zenith_rad)) + 0.0096467) / (np.cos(zenith_rad) ** 3 + 0.149864*(np.cos(zenith_rad) ** 2) + 0.0102963*(np.cos(zenith_rad)) + 0.000303978)
+        AM = ( (1.002432*((np.cos(zenith_rad)) ** 2) +
+            0.148386*(np.cos(zenith_rad)) + 0.0096467) /
+            (np.cos(zenith_rad) ** 3 +
+            0.149864*(np.cos(zenith_rad) ** 2) +
+            0.0102963*(np.cos(zenith_rad)) + 0.000303978) )
     elif 'gueymard1993' == model:
-        AM = 1.0 / (np.cos(zenith_rad) + 0.00176759*(z)*((94.37515 - z) ** - 1.21563))
+        AM = ( 1.0 / (np.cos(zenith_rad) +
+            0.00176759*(z)*((94.37515 - z) ** - 1.21563)) )
     else:
-        pvl_logger.warning("{} is not a valid model type for relative airmass. The 'kastenyoung1989' model was used.".format(model))
-        AM = 1.0 / (np.cos(zenith_rad) + 0.50572*(((6.07995 + (90 - z)) ** - 1.6364)))
+        pvl_logger.warning("{} is not a valid model type for relative airmass. The 'kastenyoung1989' model was used."
+                           .format(model))
+        AM = ( 1.0 / (np.cos(zenith_rad) +
+            0.50572*(((6.07995 + (90 - z)) ** - 1.6364))) )
     
     try:
         AM[z > 90] = np.nan
diff --git a/pvlib/clearsky.py b/pvlib/clearsky.py
index 1c076b2e00..f1c9fe0f2f 100644
--- a/pvlib/clearsky.py
+++ b/pvlib/clearsky.py
@@ -49,13 +49,13 @@ def ineichen(time, location, linke_turbidity=None,
         Sets the solar position algorithm. 
         See solarposition.get_solarposition()
     
-    zenith_data : None or pandas.Series
+    zenith_data : None or Series
         If None, ephemeris data will be calculated using ``solarposition_method``.
     
     airmass_model : string
         See pvlib.airmass.relativeairmass().
     
-    airmass_data : None or pandas.Series
+    airmass_data : None or Series
         If None, absolute air mass data will be calculated using 
         ``airmass_model`` and location.alitude.
     
@@ -65,14 +65,14 @@ def ineichen(time, location, linke_turbidity=None,
 
     Returns
     --------
-    DataFrame with the following columns: ``GHI, DNI, DHI``.
+    DataFrame with the following columns: ``ghi, dni, dhi``.
 
     Notes
     -----
     If you are using this function
     in a loop, it may be faster to load LinkeTurbidities.mat outside of
-    the loop and feed it in as a variable, rather than
-    having the function open the file each time it is called.
+    the loop and feed it in as a keyword argument, rather than
+    having the function open and process the file each time it is called.
 
     References
     ----------
@@ -96,7 +96,7 @@ def ineichen(time, location, linke_turbidity=None,
     '''
     # Initial implementation of this algorithm by Matthew Reno.
     # Ported to python by Rob Andrews
-    # Added functionality by Will Holmgren
+    # Added functionality by Will Holmgren (@wholmgren)
     
     I0 = irradiance.extraradiation(time.dayofyear)
     
@@ -161,7 +161,7 @@ def ineichen(time, location, linke_turbidity=None,
     # alt2pres) using Kasten and Young's 1989 formula for airmass.
     
     if airmass_data is None:
-        AMabsolute = atmosphere.absoluteairmass(AMrelative=atmosphere.relativeairmass(ApparentZenith, airmass_model),
+        AMabsolute = atmosphere.absoluteairmass(airmass_relative=atmosphere.relativeairmass(ApparentZenith, airmass_model),
                                                 pressure=atmosphere.alt2pres(location.altitude))
     else:
         AMabsolute = airmass_data
@@ -195,7 +195,9 @@ def ineichen(time, location, linke_turbidity=None,
     
     cos_zenith = tools.cosd(ApparentZenith)
     
-    clearsky_GHI = cg1 * I0 * cos_zenith * np.exp(-cg2*AMabsolute*(fh1 + fh2*(TL - 1))) * np.exp(0.01*AMabsolute**1.8)
+    clearsky_GHI = ( cg1 * I0 * cos_zenith *
+                     np.exp(-cg2*AMabsolute*(fh1 + fh2*(TL - 1))) *
+                     np.exp(0.01*AMabsolute**1.8) )
     clearsky_GHI[clearsky_GHI < 0] = 0
     
     # BncI == "normal beam clear sky radiation"
@@ -204,25 +206,24 @@ def ineichen(time, location, linke_turbidity=None,
     logger.debug('b={}'.format(b))
     
     # "empirical correction" SE 73, 157 & SE 73, 312.
-    BncI_2 = clearsky_GHI * ( 1 - (0.1 - 0.2*np.exp(-TL))/(0.1 + 0.882/fh1) ) / cos_zenith
-    #return BncI, BncI_2
-    clearsky_DNI = np.minimum(BncI, BncI_2) # Will H: use np.minimum explicitly
+    BncI_2 = ( clearsky_GHI *
+               ( 1 - (0.1 - 0.2*np.exp(-TL))/(0.1 + 0.882/fh1) ) /
+               cos_zenith )
+
+    clearsky_DNI = np.minimum(BncI, BncI_2)
     
     clearsky_DHI = clearsky_GHI - clearsky_DNI*cos_zenith
     
-    df_out = pd.DataFrame({'GHI':clearsky_GHI, 'DNI':clearsky_DNI, 
-                           'DHI':clearsky_DHI})
+    df_out = pd.DataFrame({'ghi':clearsky_GHI, 'dni':clearsky_DNI, 
+                           'dhi':clearsky_DHI})
     df_out.fillna(0, inplace=True)
-    #df_out['BncI'] = BncI
-    #df_out['BncI_2'] = BncI
     
     return df_out
-    
-    
-    
-def haurwitz(ApparentZenith):
+
+
+def haurwitz(apparent_zenith):
     '''
-    Determine clear sky GHI from Haurwitz model
+    Determine clear sky GHI from Haurwitz model.
    
     Implements the Haurwitz clear sky model for global horizontal
     irradiance (GHI) as presented in [1, 2]. A report on clear
@@ -232,7 +233,7 @@ def haurwitz(ApparentZenith):
 
     Parameters
     ----------
-    ApparentZenith : DataFrame
+    apparent_zenith : Series
         The apparent (refraction corrected) sun zenith angle
         in degrees.
 
@@ -258,13 +259,13 @@ def haurwitz(ApparentZenith):
      Laboratories, SAND2012-2389, 2012.
     '''
 
-    cos_zenith = tools.cosd(ApparentZenith)
+    cos_zenith = tools.cosd(apparent_zenith)
 
     clearsky_GHI = 1098.0 * cos_zenith * np.exp(-0.059/cos_zenith)
 
     clearsky_GHI[clearsky_GHI < 0] = 0
     
-    df_out = pd.DataFrame({'GHI':clearsky_GHI})
+    df_out = pd.DataFrame({'ghi':clearsky_GHI})
     
     return df_out
 
@@ -274,5 +275,5 @@ def _linearly_scale(inputmatrix, inputmin, inputmax, outputmin, outputmax):
     
     inputrange = inputmax - inputmin
     outputrange = outputmax - outputmin
-    OutputMatrix = (inputmatrix - inputmin) * outputrange / inputrange + outputmin
+    OutputMatrix = (inputmatrix-inputmin) * outputrange/inputrange + outputmin
     return OutputMatrix
diff --git a/pvlib/irradiance.py b/pvlib/irradiance.py
index 27cff77b15..4496f56275 100644
--- a/pvlib/irradiance.py
+++ b/pvlib/irradiance.py
@@ -172,7 +172,7 @@ def _doy_to_timestamp(doy, epoch='2013-12-31'):
     return pd.Timestamp('2013-12-31') + datetime.timedelta(days=float(doy))
 
 
-def aoi_projection(surf_tilt, surf_az, sun_zen, sun_az):
+def aoi_projection(surface_tilt, surface_azimuth, solar_zenith, solar_azimuth):
     """
     Calculates the dot product of the solar vector and the surface normal.
 
@@ -181,13 +181,13 @@ def aoi_projection(surf_tilt, surf_az, sun_zen, sun_az):
     Parameters
     ==========
 
-    surf_tilt : float or Series.
+    surface_tilt : float or Series.
         Panel tilt from horizontal.
-    surf_az : float or Series.
+    surface_azimuth : float or Series.
         Panel azimuth from north.
-    sun_zen : float or Series.
+    solar_zenith : float or Series.
         Solar zenith angle.
-    sun_az : float or Series.
+    solar_azimuth : float or Series.
         Solar azimuth angle.
 
     Returns
@@ -196,8 +196,8 @@ def aoi_projection(surf_tilt, surf_az, sun_zen, sun_az):
     """
 
     projection = (
-        tools.cosd(surf_tilt) * tools.cosd(sun_zen) + tools.sind(surf_tilt) *
-        tools.sind(sun_zen) * tools.cosd(sun_az - surf_az))
+        tools.cosd(surface_tilt) * tools.cosd(solar_zenith) + tools.sind(surface_tilt) *
+        tools.sind(solar_zenith) * tools.cosd(solar_azimuth - surface_azimuth))
 
     try:
         projection.name = 'aoi_projection'
@@ -207,7 +207,7 @@ def aoi_projection(surf_tilt, surf_az, sun_zen, sun_az):
     return projection
 
 
-def aoi(surf_tilt, surf_az, sun_zen, sun_az):
+def aoi(surface_tilt, surface_azimuth, solar_zenith, solar_azimuth):
     """
     Calculates the angle of incidence of the solar vector on a surface.
     This is the angle between the solar vector and the surface normal.
@@ -217,13 +217,13 @@ def aoi(surf_tilt, surf_az, sun_zen, sun_az):
     Parameters
     ==========
 
-    surf_tilt : float or Series.
+    surface_tilt : float or Series.
         Panel tilt from horizontal.
-    surf_az : float or Series.
+    surface_azimuth : float or Series.
         Panel azimuth from north.
-    sun_zen : float or Series.
+    solar_zenith : float or Series.
         Solar zenith angle.
-    sun_az : float or Series.
+    solar_azimuth : float or Series.
         Solar azimuth angle.
 
     Returns
@@ -231,7 +231,7 @@ def aoi(surf_tilt, surf_az, sun_zen, sun_az):
     float or Series. Angle of incidence in degrees.
     """
 
-    projection = aoi_projection(surf_tilt, surf_az, sun_zen, sun_az)
+    projection = aoi_projection(surface_tilt, surface_azimuth, solar_zenith, solar_azimuth)
     aoi_value = np.rad2deg(np.arccos(projection))
 
     try:
@@ -242,7 +242,8 @@ def aoi(surf_tilt, surf_az, sun_zen, sun_az):
     return aoi_value
 
 
-def poa_horizontal_ratio(surf_tilt, surf_az, sun_zen, sun_az):
+def poa_horizontal_ratio(surface_tilt, surface_azimuth,
+                         solar_zenith, solar_azimuth):
     """
     Calculates the ratio of the beam components of the
     plane of array irradiance and the horizontal irradiance.
@@ -252,13 +253,13 @@ def poa_horizontal_ratio(surf_tilt, surf_az, sun_zen, sun_az):
     Parameters
     ==========
 
-    surf_tilt : float or Series.
+    surface_tilt : float or Series.
         Panel tilt from horizontal.
-    surf_az : float or Series.
+    surface_azimuth : float or Series.
         Panel azimuth from north.
-    sun_zen : float or Series.
+    solar_zenith : float or Series.
         Solar zenith angle.
-    sun_az : float or Series.
+    solar_azimuth : float or Series.
         Solar azimuth angle.
 
     Returns
@@ -267,12 +268,13 @@ def poa_horizontal_ratio(surf_tilt, surf_az, sun_zen, sun_az):
     horizontal plane irradiance
     """
 
-    cos_poa_zen = aoi_projection(surf_tilt, surf_az, sun_zen, sun_az)
+    cos_poa_zen = aoi_projection(surface_tilt, surface_azimuth,
+                                 solar_zenith, solar_azimuth)
 
-    cos_sun_zen = tools.cosd(sun_zen)
+    cos_solar_zenith = tools.cosd(solar_zenith)
 
     # ratio of titled and horizontal beam irradiance
-    ratio = cos_poa_zen / cos_sun_zen
+    ratio = cos_poa_zen / cos_solar_zenith
 
     try:
         ratio.name = 'poa_ratio'
@@ -282,37 +284,39 @@ def poa_horizontal_ratio(surf_tilt, surf_az, sun_zen, sun_az):
     return ratio
 
 
-def beam_component(surf_tilt, surf_az, sun_zen, sun_az, DNI):
+def beam_component(surface_tilt, surface_azimuth,
+                   solar_zenith, solar_azimuth, dni):
     """
     Calculates the beam component of the plane of array irradiance.
 
     Parameters
     ----------
-    surf_tilt : float or Series.
+    surface_tilt : float or Series.
         Panel tilt from horizontal.
-    surf_az : float or Series.
+    surface_azimuth : float or Series.
         Panel azimuth from north.
-    sun_zen : float or Series.
+    solar_zenith : float or Series.
         Solar zenith angle.
-    sun_az : float or Series.
+    solar_azimuth : float or Series.
         Solar azimuth angle.
-    DNI : float or Series
+    dni : float or Series
         Direct Normal Irradiance
 
     Returns
     -------
     Series
     """
-    beam = DNI * aoi_projection(surf_tilt, surf_az, sun_zen, sun_az)
+    beam = dni * aoi_projection(surface_tilt, surface_azimuth,
+                                solar_zenith, solar_azimuth)
     beam[beam < 0] = 0
 
     return beam
 
 
 # ToDo: how to best structure this function? wholmgren 2014-11-03
-def total_irrad(surf_tilt, surf_az,
-                sun_zen, sun_az,
-                DNI, GHI, DHI, DNI_ET=None, AM=None,
+def total_irrad(surface_tilt, surface_azimuth,
+                solar_zenith, solar_azimuth,
+                dni, ghi, dhi, dni_extra=None, airmass=None,
                 albedo=.25, surface_type=None,
                 model='isotropic',
                 model_perez='allsitescomposite1990'):
@@ -326,23 +330,23 @@ def total_irrad(surf_tilt, surf_az,
 
     Parameters
     ----------
-    surf_tilt : float or Series.
+    surface_tilt : float or Series.
         Panel tilt from horizontal.
-    surf_az : float or Series.
+    surface_azimuth : float or Series.
         Panel azimuth from north.
-    sun_zen : float or Series.
+    solar_zenith : float or Series.
         Solar zenith angle.
-    sun_az : float or Series.
+    solar_azimuth : float or Series.
         Solar azimuth angle.
-    DNI : float or Series
+    dni : float or Series
         Direct Normal Irradiance
-    GHI : float or Series
+    ghi : float or Series
         Global horizontal irradiance
-    DHI : float or Series
+    dhi : float or Series
         Diffuse horizontal irradiance
-    DNI_ET : float or Series
+    dni_extra : float or Series
         Extraterrestrial direct normal irradiance
-    AM : float or Series
+    airmass : float or Series
         Airmass
     albedo : float
         Surface albedo
@@ -366,27 +370,28 @@ def total_irrad(surf_tilt, surf_az,
 
     pvl_logger.debug('planeofarray.total_irrad()')
 
-    beam = beam_component(surf_tilt, surf_az, sun_zen, sun_az, DNI)
+    beam = beam_component(surface_tilt, surface_azimuth,
+                          solar_zenith, solar_azimuth, dni)
 
     model = model.lower()
     if model == 'isotropic':
-        sky = isotropic(surf_tilt, DHI)
+        sky = isotropic(surface_tilt, dhi)
     elif model == 'klutcher':
-        sky = klucher(surf_tilt, surf_az, DHI, GHI, sun_zen, sun_az)
+        sky = klucher(surface_tilt, surface_azimuth, dhi, ghi, solar_zenith, solar_azimuth)
     elif model == 'haydavies':
-        sky = haydavies(surf_tilt, surf_az, DHI, DNI, DNI_ET, sun_zen, sun_az)
+        sky = haydavies(surface_tilt, surface_azimuth, dhi, dni, dni_extra, solar_zenith, solar_azimuth)
     elif model == 'reindl':
-        sky = reindl(surf_tilt, surf_az, DHI, DNI, GHI, DNI_ET, sun_zen,
-                     sun_az)
+        sky = reindl(surface_tilt, surface_azimuth, dhi, dni, ghi, dni_extra, solar_zenith,
+                     solar_azimuth)
     elif model == 'king':
-        sky = king(surf_tilt, DHI, GHI, sun_zen)
+        sky = king(surface_tilt, dhi, ghi, solar_zenith)
     elif model == 'perez':
-        sky = perez(surf_tilt, surf_az, DHI, DNI, DNI_ET, sun_zen, sun_az, AM,
+        sky = perez(surface_tilt, surface_azimuth, dhi, dni, dni_extra, solar_zenith, solar_azimuth, AM,
                     modelt=model_perez)
     else:
         raise ValueError('invalid model selection {}'.format(model))
 
-    ground = grounddiffuse(surf_tilt, GHI, albedo, surface_type)
+    ground = grounddiffuse(surface_tilt, ghi, albedo, surface_type)
 
     total = beam + sky + ground
 
@@ -399,7 +404,7 @@ def total_irrad(surf_tilt, surf_az,
 
 
 # ToDo: keep this or not? wholmgren, 2014-11-03
-def globalinplane(AOI, DNI, In_Plane_SkyDiffuse, GR):
+def globalinplane(aoi, dni, poa_sky_diffuse, poa_ground_diffuse):
     '''
     Determine the three components on in-plane irradiance
 
@@ -410,28 +415,28 @@ def globalinplane(AOI, DNI, In_Plane_SkyDiffuse, GR):
     Parameters
     ----------
 
-    AOI : float or Series
-          Angle of incidence of solar rays with respect
-          to the module surface, from :func:`aoi`.
+    aoi : float or Series
+        Angle of incidence of solar rays with respect
+        to the module surface, from :func:`aoi`.
 
-    DNI : float or Series
-          Direct normal irradiance (W/m^2), as measured
-          from a TMY file or calculated with a clearsky model.
+    dni : float or Series
+        Direct normal irradiance (W/m^2), as measured
+        from a TMY file or calculated with a clearsky model.
 
-    In_Plane_SkyDiffuse :  float or Series
-          Diffuse irradiance (W/m^2) in the plane of the modules, as
-          calculated by a diffuse irradiance translation function
+    poa_sky_diffuse :  float or Series
+        Diffuse irradiance (W/m^2) in the plane of the modules, as
+        calculated by a diffuse irradiance translation function
 
-    GR : float or Series
-          a scalar or DataFrame of ground reflected irradiance (W/m^2),
-          as calculated by a albedo model (eg. :func:`grounddiffuse`)
+    poa_ground_diffuse : float or Series
+        Ground reflected irradiance (W/m^2) in the plane of the modules,
+        as calculated by an albedo model (eg. :func:`grounddiffuse`)
 
     Returns
     -------
     DataFrame with the following keys:
-        * ``E`` : Total in-plane irradiance (W/m^2)
-        * ``Eb`` : Total in-plane beam irradiance (W/m^2)
-        * ``Ediff`` : Total in-plane diffuse irradiance (W/m^2)
+        * ``poa_global`` : Total in-plane irradiance (W/m^2)
+        * ``poa_direct`` : Total in-plane beam irradiance (W/m^2)
+        * ``poa_diffuse`` : Total in-plane diffuse irradiance (W/m^2)
 
     Notes
     ------
@@ -439,14 +444,16 @@ def globalinplane(AOI, DNI, In_Plane_SkyDiffuse, GR):
     :math:`< 0^{\circ}` is set to zero.
     '''
 
-    Eb = pd.Series(DNI * np.cos(np.radians(AOI))).clip_lower(0)
-    E = Eb + In_Plane_SkyDiffuse + GR
-    Ediff = In_Plane_SkyDiffuse + GR
+    poa_direct = pd.Series(dni * np.cos(np.radians(aoi))).clip_lower(0)
+    poa_global = poa_direct + poa_sky_diffuse + poa_ground_diffuse
+    poa_diffuse = poa_sky_diffuse + poa_ground_diffuse
 
-    return pd.DataFrame({'E': E, 'Eb': Eb, 'Ediff': Ediff})
+    return pd.DataFrame({'poa_global': poa_global,
+                         'poa_direct': poa_direct,
+                         'poa_diffuse': poa_diffuse})
 
 
-def grounddiffuse(surf_tilt, ghi, albedo=.25, surface_type=None):
+def grounddiffuse(surface_tilt, ghi, albedo=.25, surface_type=None):
     '''
     Estimate diffuse irradiance from ground reflections given
     irradiance, albedo, and surface tilt
@@ -456,7 +463,7 @@ def grounddiffuse(surf_tilt, ghi, albedo=.25, surface_type=None):
 
     Parameters
     ----------
-    surf_tilt : float or DataFrame
+    surface_tilt : float or DataFrame
         Surface tilt angles in decimal degrees.
         SurfTilt must be >=0 and <=180. The tilt angle is defined as
         degrees from horizontal (e.g. surface facing up = 0, surface facing
@@ -506,7 +513,7 @@ def grounddiffuse(surf_tilt, ghi, albedo=.25, surface_type=None):
         pvl_logger.info('surface_type={} mapped to albedo={}'
                         .format(surface_type, albedo))
 
-    diffuse_irrad = ghi * albedo * (1 - np.cos(np.radians(surf_tilt))) * 0.5
+    diffuse_irrad = ghi * albedo * (1 - np.cos(np.radians(surface_tilt))) * 0.5
 
     try:
         diffuse_irrad.name = 'diffuse_ground'
@@ -516,7 +523,7 @@ def grounddiffuse(surf_tilt, ghi, albedo=.25, surface_type=None):
     return diffuse_irrad
 
 
-def isotropic(surf_tilt, DHI):
+def isotropic(surface_tilt, dhi):
     r'''
     Determine diffuse irradiance from the sky on a
     tilted surface using the isotropic sky model.
@@ -534,13 +541,13 @@ def isotropic(surf_tilt, DHI):
     Parameters
     ----------
 
-    surf_tilt : float or Series
+    surface_tilt : float or Series
         Surface tilt angle in decimal degrees.
-        surf_tilt must be >=0 and <=180. The tilt angle is defined as
+        surface_tilt must be >=0 and <=180. The tilt angle is defined as
         degrees from horizontal (e.g. surface facing up = 0, surface facing
         horizon = 90)
 
-    DHI : float or Series
+    dhi : float or Series
         Diffuse horizontal irradiance in W/m^2.
         DHI must be >=0.
 
@@ -571,12 +578,13 @@ def isotropic(surf_tilt, DHI):
 
     pvl_logger.debug('diffuse_sky.isotropic()')
 
-    sky_diffuse = DHI * (1 + tools.cosd(surf_tilt)) * 0.5
+    sky_diffuse = dhi * (1 + tools.cosd(surface_tilt)) * 0.5
 
     return sky_diffuse
 
 
-def klucher(surf_tilt, surf_az, DHI, GHI, sun_zen, sun_az):
+def klucher(surface_tilt, surface_azimuth, dhi, ghi,
+            solar_zenith, solar_azimuth):
     r'''
     Determine diffuse irradiance from the sky on a tilted surface
     using Klucher's 1979 model
@@ -602,34 +610,34 @@ def klucher(surf_tilt, surf_az, DHI, GHI, sun_zen, sun_az):
     Parameters
     ----------
 
-    surf_tilt : float or Series
+    surface_tilt : float or Series
         Surface tilt angles in decimal degrees.
-        surf_tilt must be >=0 and <=180. The tilt angle is defined as
+        surface_tilt must be >=0 and <=180. The tilt angle is defined as
         degrees from horizontal (e.g. surface facing up = 0, surface facing
         horizon = 90)
 
-    surf_az : float or Series
+    surface_azimuth : float or Series
         Surface azimuth angles in decimal degrees.
-        surf_az must be >=0 and <=360. The Azimuth convention is defined
+        surface_azimuth must be >=0 and <=360. The Azimuth convention is defined
         as degrees east of north (e.g. North = 0, South=180 East = 90,
         West = 270).
 
-    DHI : float or Series
+    dhi : float or Series
         diffuse horizontal irradiance in W/m^2.
         DHI must be >=0.
 
-    GHI : float or Series
+    ghi : float or Series
         Global  irradiance in W/m^2.
         DNI must be >=0.
 
-    sun_zen : float or Series
+    solar_zenith : float or Series
         apparent (refraction-corrected) zenith
         angles in decimal degrees.
-        sun_zen must be >=0 and <=180.
+        solar_zenith must be >=0 and <=180.
 
-    sun_az : float or Series
+    solar_azimuth : float or Series
         Sun azimuth angles in decimal degrees.
-        sun_az must be >=0 and <=360. The Azimuth convention is defined
+        solar_azimuth must be >=0 and <=360. The Azimuth convention is defined
         as degrees east of north (e.g. North = 0, East = 90, West = 270).
 
     Returns
@@ -657,20 +665,21 @@ def klucher(surf_tilt, surf_az, DHI, GHI, sun_zen, sun_az):
     pvl_logger.debug('diffuse_sky.klucher()')
 
     # zenith angle with respect to panel normal.
-    cos_tt = aoi_projection(surf_tilt, surf_az, sun_zen, sun_az)
+    cos_tt = aoi_projection(surface_tilt, surface_azimuth,
+                            solar_zenith, solar_azimuth)
 
-    F = 1 - ((DHI / GHI) ** 2)
+    F = 1 - ((ghi / ghi) ** 2)
     try:
         # fails with single point input
         F.fillna(0, inplace=True)
     except AttributeError:
         F = 0
 
-    term1 = 0.5 * (1 + tools.cosd(surf_tilt))
-    term2 = 1 + F * (tools.sind(0.5 * surf_tilt) ** 3)
-    term3 = 1 + F * (cos_tt ** 2) * (tools.sind(sun_zen) ** 3)
+    term1 = 0.5 * (1 + tools.cosd(surface_tilt))
+    term2 = 1 + F * (tools.sind(0.5 * surface_tilt) ** 3)
+    term3 = 1 + F * (cos_tt ** 2) * (tools.sind(solar_zenith) ** 3)
 
-    sky_diffuse = DHI * term1 * term2 * term3
+    sky_diffuse = dhi * term1 * term2 * term3
 
     return sky_diffuse
 
@@ -758,8 +767,8 @@ def haydavies(surface_tilt, surface_azimuth, dhi, dni, dni_extra,
     if projection_ratio is None:
         cos_tt = aoi_projection(surface_tilt, surface_azimuth,
                                 solar_zenith, solar_azimuth)
-        cos_sun_zen = tools.cosd(solar_zenith)
-        Rb = cos_tt / cos_sun_zen
+        cos_solar_zenith = tools.cosd(solar_zenith)
+        Rb = cos_tt / cos_solar_zenith
     else:
         Rb = projection_ratio
 
@@ -776,7 +785,8 @@ def haydavies(surface_tilt, surface_azimuth, dhi, dni, dni_extra,
     return sky_diffuse
 
 
-def reindl(surf_tilt, surf_az, DHI, DNI, GHI, DNI_ET, sun_zen, sun_az):
+def reindl(surface_tilt, surface_azimuth, dhi, dni, ghi, dni_extra,
+           solar_zenith, solar_azimuth):
     r'''
     Determine diffuse irradiance from the sky on a
     tilted surface using Reindl's 1990 model
@@ -796,35 +806,35 @@ def reindl(surf_tilt, surf_az, DHI, DNI, GHI, DNI_ET, sun_zen, sun_az):
     Parameters
     ----------
 
-    surf_tilt : float or Series.
+    surface_tilt : float or Series.
         Surface tilt angles in decimal degrees.
         The tilt angle is defined as
         degrees from horizontal (e.g. surface facing up = 0, surface facing
         horizon = 90)
 
-    surf_az : float or Series.
+    surface_azimuth : float or Series.
         Surface azimuth angles in decimal degrees.
         The Azimuth convention is defined
         as degrees east of north (e.g. North = 0, South=180 East = 90,
         West = 270).
 
-    DHI : float or Series.
+    dhi : float or Series.
         diffuse horizontal irradiance in W/m^2.
 
-    DNI : float or Series.
+    dni : float or Series.
         direct normal irradiance in W/m^2.
 
-    GHI: float or Series.
+    ghi: float or Series.
         Global irradiance in W/m^2.
 
-    DNI_ET : float or Series.
+    dni_extra : float or Series.
         extraterrestrial normal irradiance in W/m^2.
 
-    sun_zen : float or Series.
+    solar_zenith : float or Series.
         apparent (refraction-corrected) zenith
         angles in decimal degrees.
 
-    sun_az : float or Series.
+    solar_azimuth : float or Series.
         Sun azimuth angles in decimal degrees.
         The Azimuth convention is defined
         as degrees east of north (e.g. North = 0, East = 90, West = 270).
@@ -832,7 +842,7 @@ def reindl(surf_tilt, surf_az, DHI, DNI, GHI, DNI_ET, sun_zen, sun_az):
     Returns
     -------
 
-    SkyDiffuse : float or Series.
+    poa_sky_diffuse : float or Series.
 
         The diffuse component of the solar radiation  on an
         arbitrarily tilted surface defined by the Reindl model as given in
@@ -846,7 +856,7 @@ def reindl(surf_tilt, surf_az, DHI, DNI, GHI, DNI_ET, sun_zen, sun_az):
     Notes
     -----
 
-    The POAskydiffuse calculation is generated from the Loutzenhiser et al.
+    The poa_sky_diffuse calculation is generated from the Loutzenhiser et al.
     (2007) paper, equation 8. Note that I have removed the beam and ground
     reflectance portion of the equation and this generates ONLY the diffuse
     radiation from the sky and circumsolar, so the form of the equation
@@ -868,32 +878,33 @@ def reindl(surf_tilt, surf_az, DHI, DNI, GHI, DNI_ET, sun_zen, sun_az):
 
     pvl_logger.debug('diffuse_sky.reindl()')
 
-    cos_tt = aoi_projection(surf_tilt, surf_az, sun_zen, sun_az)
+    cos_tt = aoi_projection(surface_tilt, surface_azimuth,
+                            solar_zenith, solar_azimuth)
 
-    cos_sun_zen = tools.cosd(sun_zen)
+    cos_solar_zenith = tools.cosd(solar_zenith)
 
     # ratio of titled and horizontal beam irradiance
-    Rb = cos_tt / cos_sun_zen
+    Rb = cos_tt / cos_solar_zenith
 
     # Anisotropy Index
-    AI = DNI / DNI_ET
+    AI = dni / dni_extra
 
     # DNI projected onto horizontal
-    HB = DNI * cos_sun_zen
+    HB = dni * cos_solar_zenith
     HB[HB < 0] = 0
 
     # these are actually the () and [] sub-terms of the second term of eqn 8
     term1 = 1 - AI
-    term2 = 0.5 * (1 + tools.cosd(surf_tilt))
-    term3 = 1 + np.sqrt(HB / GHI) * (tools.sind(0.5 * surf_tilt) ** 3)
+    term2 = 0.5 * (1 + tools.cosd(surface_tilt))
+    term3 = 1 + np.sqrt(HB / ghi) * (tools.sind(0.5 * surface_tilt) ** 3)
 
-    sky_diffuse = DHI * (AI * Rb + term1 * term2 * term3)
+    sky_diffuse = dhi * (AI * Rb + term1 * term2 * term3)
     sky_diffuse[sky_diffuse < 0] = 0
 
     return sky_diffuse
 
 
-def king(surf_tilt, DHI, GHI, sun_zen):
+def king(surface_tilt, dhi, ghi, solar_zenith):
     '''
     Determine diffuse irradiance from the sky on a tilted surface using the
     King model.
@@ -908,43 +919,42 @@ def king(surf_tilt, DHI, GHI, sun_zen):
     Parameters
     ----------
 
-    surf_tilt : float or Series
-          Surface tilt angles in decimal degrees.
-          The tilt angle is defined as
-          degrees from horizontal (e.g. surface facing up = 0, surface facing
-          horizon = 90)
+    surface_tilt : float or Series
+        Surface tilt angles in decimal degrees.
+        The tilt angle is defined as
+        degrees from horizontal (e.g. surface facing up = 0, surface facing
+        horizon = 90)
 
-    DHI : float or Series
-          diffuse horizontal irradiance in W/m^2.
+    dhi : float or Series
+        Diffuse horizontal irradiance in W/m^2.
 
-    GHI : float or Series
-          global horizontal irradiance in W/m^2.
+    ghi : float or Series
+        Global horizontal irradiance in W/m^2.
 
-    sun_zen : float or Series
-          apparent (refraction-corrected) zenith
-          angles in decimal degrees.
+    solar_zenith : float or Series
+        Apparent (refraction-corrected) zenith
+        angles in decimal degrees.
 
     Returns
     --------
-
-    SkyDiffuse : float or Series
-
-            the diffuse component of the solar radiation  on an
-            arbitrarily tilted surface as given by a model developed by
-            David L. King at Sandia National Laboratories.
+    poa_sky_diffuse : float or Series
+        The diffuse component of the solar radiation on an
+        arbitrarily tilted surface as given by a model developed by
+        David L. King at Sandia National Laboratories.
     '''
 
     pvl_logger.debug('diffuse_sky.king()')
 
-    sky_diffuse = (DHI * ((1 + tools.cosd(surf_tilt))) / 2 + GHI *
-                   ((0.012 * sun_zen - 0.04)) *
-                   ((1 - tools.cosd(surf_tilt))) / 2)
+    sky_diffuse = (dhi * ((1 + tools.cosd(surface_tilt))) / 2 + ghi *
+                   ((0.012 * solar_zenith - 0.04)) *
+                   ((1 - tools.cosd(surface_tilt))) / 2)
     sky_diffuse[sky_diffuse < 0] = 0
 
     return sky_diffuse
 
 
-def perez(surf_tilt, surf_az, DHI, DNI, DNI_ET, sun_zen, sun_az, AM,
+def perez(surface_tilt, surface_azimuth, dhi, dni, dni_extra,
+          solar_zenith, solar_azimuth, airmass,
           modelt='allsitescomposite1990'):
     '''
     Determine diffuse irradiance from the sky on a tilted surface using one of
@@ -962,79 +972,75 @@ def perez(surf_tilt, surf_az, DHI, DNI, DNI_ET, sun_zen, sun_az, AM,
     Parameters
     ----------
 
-    surf_tilt : float or Series
-          Surface tilt angles in decimal degrees.
-          surf_tilt must be >=0 and <=180. The tilt angle is defined as
-          degrees from horizontal (e.g. surface facing up = 0, surface facing
-          horizon = 90)
-
-    surf_az : float or Series
-          Surface azimuth angles in decimal degrees.
-          surf_az must be >=0 and <=360. The Azimuth convention is defined
-          as degrees east of north (e.g. North = 0, South=180 East = 90,
-          West = 270).
-
-    DHI : float or Series
-          diffuse horizontal irradiance in W/m^2.
-          DHI must be >=0.
-
-    DNI : float or Series
-          direct normal irradiance in W/m^2.
-          DNI must be >=0.
-
-    DNI_ET : float or Series
-          extraterrestrial normal irradiance in W/m^2.
-           DNI_ET must be >=0.
-
-    sun_zen : float or Series
-          apparent (refraction-corrected) zenith
-          angles in decimal degrees.
-          sun_zen must be >=0 and <=180.
-
-    sun_az : float or Series
-          Sun azimuth angles in decimal degrees.
-          sun_az must be >=0 and <=360. The Azimuth convention is defined
-          as degrees east of north (e.g. North = 0, East = 90, West = 270).
-
-    AM : float or Series
-          relative (not pressure-corrected) airmass
-          values. If AM is a DataFrame it must be of the same size as all other
-          DataFrame inputs. AM must be >=0 (careful using the 1/sec(z) model of
-          AM generation)
-
-    Other Parameters
-    ----------------
+    surface_tilt : float or Series
+        Surface tilt angles in decimal degrees.
+        surface_tilt must be >=0 and <=180. The tilt angle is defined as
+        degrees from horizontal (e.g. surface facing up = 0, surface facing
+        horizon = 90)
 
-    model : string (optional, default='allsitescomposite1990')
+    surface_azimuth : float or Series
+        Surface azimuth angles in decimal degrees.
+        surface_azimuth must be >=0 and <=360. The Azimuth convention is defined
+        as degrees east of north (e.g. North = 0, South=180 East = 90,
+        West = 270).
 
-          a character string which selects the desired set of Perez
-          coefficients. If model is not provided as an input, the default,
-          '1990' will be used.
-          All possible model selections are:
+    dhi : float or Series
+        Diffuse horizontal irradiance in W/m^2.
+        DHI must be >=0.
 
-          * '1990'
-          * 'allsitescomposite1990' (same as '1990')
-          * 'allsitescomposite1988'
-          * 'sandiacomposite1988'
-          * 'usacomposite1988'
-          * 'france1988'
-          * 'phoenix1988'
-          * 'elmonte1988'
-          * 'osage1988'
-          * 'albuquerque1988'
-          * 'capecanaveral1988'
-          * 'albany1988'
+    dni : float or Series
+        Direct normal irradiance in W/m^2.
+        DNI must be >=0.
+
+    dni_extra : float or Series
+        Extraterrestrial normal irradiance in W/m^2.
+
+    solar_zenith : float or Series
+        apparent (refraction-corrected) zenith
+        angles in decimal degrees.
+        solar_zenith must be >=0 and <=180.
+
+    solar_azimuth : float or Series
+        Sun azimuth angles in decimal degrees.
+        solar_azimuth must be >=0 and <=360. The Azimuth convention is defined
+        as degrees east of north (e.g. North = 0, East = 90, West = 270).
+
+    airmass : float or Series
+        relative (not pressure-corrected) airmass
+        values. If AM is a DataFrame it must be of the same size as all other
+        DataFrame inputs. AM must be >=0 (careful using the 1/sec(z) model of
+        AM generation)
+
+    model : string (optional, default='allsitescomposite1990')
+
+        A string which selects the desired set of Perez
+        coefficients. If model is not provided as an input, the default,
+        '1990' will be used.
+        All possible model selections are:
+
+        * '1990'
+        * 'allsitescomposite1990' (same as '1990')
+        * 'allsitescomposite1988'
+        * 'sandiacomposite1988'
+        * 'usacomposite1988'
+        * 'france1988'
+        * 'phoenix1988'
+        * 'elmonte1988'
+        * 'osage1988'
+        * 'albuquerque1988'
+        * 'capecanaveral1988'
+        * 'albany1988'
 
     Returns
     --------
 
     float or Series
 
-          the diffuse component of the solar radiation  on an
-          arbitrarily tilted surface defined by the Perez model as given in
-          reference [3].
-          SkyDiffuse is the diffuse component ONLY and does not include the
-          ground reflected irradiance or the irradiance due to the beam.
+        The diffuse component of the solar radiation  on an
+        arbitrarily tilted surface defined by the Perez model as given in
+        reference [3].
+        SkyDiffuse is the diffuse component ONLY and does not include the
+        ground reflected irradiance or the irradiance due to the beam.
 
 
     References
@@ -1058,11 +1064,11 @@ def perez(surf_tilt, surf_az, DHI, DNI, DNI_ET, sun_zen, sun_az, AM,
 
     pvl_logger.debug('diffuse_sky.perez()')
 
-    kappa = 1.041  # for sun_zen in radians
-    z = np.radians(sun_zen)  # convert to radians
+    kappa = 1.041  # for solar_zenith in radians
+    z = np.radians(solar_zenith)  # convert to radians
 
     # epsilon is the sky's "clearness"
-    eps = ((DHI + DNI) / DHI + kappa * (z ** 3)) / (1 + kappa * (z ** 3))
+    eps = ((dhi + dni) / dhi + kappa * (z ** 3)) / (1 + kappa * (z ** 3))
 
     # Perez et al define clearness bins according to the following rules.
     # 1 = overcast ... 8 = clear
@@ -1084,17 +1090,17 @@ def perez(surf_tilt, surf_az, DHI, DNI, DNI_ET, sun_zen, sun_az, AM,
     ebin = ebin.dropna().astype(int)
 
     # This is added because in cases where the sun is below the horizon
-    # (var.sun_zen > 90) but there is still diffuse horizontal light
+    # (var.solar_zenith > 90) but there is still diffuse horizontal light
     # (var.DHI>0), it is possible that the airmass (var.AM) could be NaN, which
     # messes up later calculations. Instead, if the sun is down, and there is
     # still var.DHI, we set the airmass to the airmass value on the horizon
     # (approximately 37-38).
-    # var.AM(var.sun_zen >=90 & var.DHI >0) = 37;
+    # var.AM(var.solar_zenith >=90 & var.DHI >0) = 37;
 
     # var.DNI_ET[var.DNI_ET==0] = .00000001 #very hacky, fix this
 
     # delta is the sky's "brightness"
-    delta = DHI * AM / DNI_ET
+    delta = dhi * airmass / dni_extra
 
     # keep only valid times
     delta = delta[ebin.index]
@@ -1112,19 +1118,20 @@ def perez(surf_tilt, surf_az, DHI, DNI, DNI_ET, sun_zen, sun_az, AM,
     F2[F2 < 0] = 0
     F2 = F2.astype(float)
 
-    A = aoi_projection(surf_tilt, surf_az, sun_zen, sun_az)
+    A = aoi_projection(surface_tilt, surface_azimuth,
+                       solar_zenith, solar_azimuth)
     A[A < 0] = 0
 
-    B = tools.cosd(sun_zen)
+    B = tools.cosd(solar_zenith)
     B[B < tools.cosd(85)] = tools.cosd(85)
 
     # Calculate Diffuse POA from sky dome
 
-    term1 = 0.5 * (1 - F1) * (1 + tools.cosd(surf_tilt))
+    term1 = 0.5 * (1 - F1) * (1 + tools.cosd(surface_tilt))
     term2 = F1 * A[ebin.index] / B[ebin.index]
-    term3 = F2 * tools.sind(surf_tilt)
+    term3 = F2 * tools.sind(surface_tilt)
 
-    sky_diffuse = DHI[ebin.index] * (term1 + term2 + term3)
+    sky_diffuse = dhi[ebin.index] * (term1 + term2 + term3)
     sky_diffuse[sky_diffuse < 0] = 0
 
     return sky_diffuse
@@ -1294,7 +1301,7 @@ def _get_perez_coefficients(perezmodelt):
     return F1coeffs, F2coeffs
 
 
-def disc(GHI, zenith, times, pressure=101325):
+def disc(ghi, zenith, times, pressure=101325):
     '''
     Estimate Direct Normal Irradiance from Global Horizontal Irradiance 
     using the DISC model.
@@ -1306,10 +1313,10 @@ def disc(GHI, zenith, times, pressure=101325):
     Parameters
     ----------
 
-    GHI : Series
+    ghi : Series
         Global horizontal irradiance in W/m^2.
 
-    zenith : Series
+    solar_zenith : Series
         True (not refraction - corrected) solar zenith 
         angles in decimal degrees. 
 
@@ -1321,12 +1328,12 @@ def disc(GHI, zenith, times, pressure=101325):
     Returns   
     -------
     DataFrame with the following keys:
-        * ``DNI_gen_DISC``: The modeled direct normal irradiance 
+        * ``dni``: The modeled direct normal irradiance 
           in W/m^2 provided by the
           Direct Insolation Simulation Code (DISC) model. 
-        * ``Kt_gen_DISC``: Ratio of global to extraterrestrial 
+        * ``kt``: Ratio of global to extraterrestrial 
           irradiance on a horizontal plane.
-        * ``AM``: Airmass
+        * ``airmass``: Airmass
 
     References
     ----------
@@ -1366,7 +1373,7 @@ def disc(GHI, zenith, times, pressure=101325):
     
     AM = 1.0 / ( np.cos(np.radians(Ztemp)) + 0.15*( (93.885 - Ztemp)**(-1.253) ) ) * (pressure / 101325)
     
-    Kt = GHI / I0h
+    Kt = ghi / I0h
     Kt[Kt < 0] = 0
     Kt[Kt > 2] = np.NaN
 
@@ -1382,16 +1389,16 @@ def disc(GHI, zenith, times, pressure=101325):
     Knc = 0.866 - 0.122*(AM) + 0.0121*(AM ** 2) - 0.000653*(AM ** 3) + 1.4e-05*(AM ** 4)
     Kn = Knc - delKn
     
-    DNI = Kn * I0
+    dni = Kn * I0
 
-    DNI[zenith > 87] = np.NaN
-    DNI[(GHI < 0) | (DNI < 0)] = 0
+    dni[zenith > 87] = np.NaN
+    dni[(ghi < 0) | (dni < 0)] = 0
 
-    DFOut = pd.DataFrame({'DNI_gen_DISC':DNI})
-    DFOut['Kt_gen_DISC'] = Kt
-    DFOut['AM'] = AM
+    dfout = pd.DataFrame({'dni':dni})
+    dfout['kt'] = Kt
+    dfout['airmass'] = AM
 
-    return DFOut
+    return dfout
     
 
 def dirint(ghi, zenith, times, pressure=101325, use_delta_kt_prime=True, 
@@ -1443,7 +1450,7 @@ def dirint(ghi, zenith, times, pressure=101325, use_delta_kt_prime=True,
 
     Returns
     -------
-    DNI : pd.Series.
+    dni : pd.Series.
         The modeled direct normal irradiance in W/m^2 provided by the
         DIRINT model.
 
@@ -1465,7 +1472,7 @@ def dirint(ghi, zenith, times, pressure=101325, use_delta_kt_prime=True,
     pvl_logger.debug('clearsky.dirint')
     
     disc_out = disc(ghi, zenith, times)
-    kt = disc_out['Kt_gen_DISC']
+    kt = disc_out['kt']
     
     # Absolute Airmass, per the DISC model
     # Note that we calculate the AM pressure correction slightly differently
@@ -1545,14 +1552,14 @@ def dirint(ghi, zenith, times, pressure=101325, use_delta_kt_prime=True,
     # assignment and Python-style array lookup.
     dirint_coeffs = coeffs[kt_prime_bin-1, zenith_bin-1,
                            delta_kt_prime_bin-1, w_bin-1]
-    
-    dni = disc_out['DNI_gen_DISC'] * dirint_coeffs
 
-    dni.name = 'DNI_DIRINT'
-    
+    dni = disc_out['dni'] * dirint_coeffs
+
+    dni.name = 'dni'
+
     return dni
 
-    
+
 def _get_dirint_coeffs():
     """
     A place to stash the dirint coefficients.
@@ -1562,8 +1569,7 @@ def _get_dirint_coeffs():
     np.array with shape ``(6, 6, 7, 5)``.
     Ordering is ``[kt_prime_bin, zenith_bin, delta_kt_prime_bin, w_bin]``
     """
-    
-    
+
     # To allow for maximum copy/paste from the MATLAB 1-indexed code, 
     # we create and assign values to an oversized array.
     # Then, we return the [1:, 1:, :, :] slice.
diff --git a/pvlib/pvsystem.py b/pvlib/pvsystem.py
index 14946d96d0..83529dc012 100644
--- a/pvlib/pvsystem.py
+++ b/pvlib/pvsystem.py
@@ -260,8 +260,8 @@ def physicaliam(K, L, n, aoi):
     return IAM
 
 
-def calcparams_desoto(S, temp_cell, alpha_isc, module_parameters, EgRef,
-                      dEgdT, M=1, Sref=1000, Tref=25):
+def calcparams_desoto(poa_global, temp_cell, alpha_isc, module_parameters,
+                      EgRef, dEgdT, M=1, irrad_ref=1000, temp_ref=25):
     '''
     Applies the temperature and irradiance corrections to 
     inputs for singlediode.
@@ -275,7 +275,7 @@ def calcparams_desoto(S, temp_cell, alpha_isc, module_parameters, EgRef,
 
     Parameters
     ----------
-    S : float or Series
+    poa_global : float or Series
         The irradiance (in W/m^2) absorbed by the module.
 
     temp_cell : float or Series
@@ -317,9 +317,7 @@ def calcparams_desoto(S, temp_cell, alpha_isc, module_parameters, EgRef,
         DataFrame of dEgdT values corresponding to each input condition (this
         may be useful if dEgdT is a function of temperature).
 
-    Other Parameters
-    ----------------
-    M : float or DataFrame (optional, Default=1)
+    M : float or Series (optional, default=1)
         An optional airmass modifier, if omitted, M is given a value of 1,
         which assumes absolute (pressure corrected) airmass = 1.5. In this
         code, M is equal to M/Mref as described in [1] (i.e. Mref is assumed
@@ -329,33 +327,33 @@ def calcparams_desoto(S, temp_cell, alpha_isc, module_parameters, EgRef,
         >>> M = np.polyval([-0.000126, 0.002816, -0.024459, 0.086257, 0.918093],
         ...                AMa) # doctest: +SKIP
 
-        M may be a DataFrame.
+        M may be a Series.
 
-    Sref : float (optional, Default=1000)
-        Optional reference irradiance in W/m^2. If omitted, a value of
-        1000 is used.
+    irrad_ref : float (optional, default=1000)
+        Reference irradiance in W/m^2.
 
-    Tref : float (Optional, Default=25)
-        Optional reference cell temperature in C. If omitted, a value of
-        25 C is used.
+    temp_ref : float (optional, default=25)
+        Reference cell temperature in C.
 
     Returns
     -------
-    IL : float or DataFrame
+    Tuple of the following results:
+    
+    photocurrent : float or Series
         Light-generated current in amperes at irradiance=S and
         cell temperature=Tcell.
         
-    I0 : float or DataFrame
+    saturation_current : float or Series
         Diode saturation curent in amperes at irradiance
         S and cell temperature Tcell.
     
-    Rs : float
+    resistance_series : float
         Series resistance in ohms at irradiance S and cell temperature Tcell.
 
-    Rsh : float or DataFrame
+    resistance_shunt : float or Series
         Shunt resistance in ohms at irradiance S and cell temperature Tcell.
     
-    nNsVth : float or DataFrame
+    nNsVth : float or Series
         Modified diode ideality factor at irradiance S and cell temperature
         Tcell. Note that in source [1] nNsVth = a (equation 2). nNsVth is the
         product of the usual diode ideality factor (n), the number of
@@ -456,17 +454,17 @@ def calcparams_desoto(S, temp_cell, alpha_isc, module_parameters, EgRef,
     Rs_ref = module_parameters['R_s']
 
     k = 8.617332478e-05
-    Tref_K = Tref + 273.15
+    Tref_K = temp_ref + 273.15
     Tcell_K = temp_cell + 273.15
 
     E_g = EgRef * (1 + dEgdT*(Tcell_K - Tref_K))
 
     nNsVth = a_ref * (Tcell_K / Tref_K)
 
-    IL = S / Sref * M * (IL_ref + alpha_isc * (Tcell_K - Tref_K))
+    IL = (poa_global/irrad_ref) * M * (IL_ref + alpha_isc * (Tcell_K - Tref_K))
     I0 = ( I0_ref * ((Tcell_K / Tref_K) ** 3) *
            (np.exp(EgRef / (k*(Tref_K)) - (E_g / (k*(Tcell_K))))) )
-    Rsh = Rsh_ref * (Sref / S)
+    Rsh = Rsh_ref * (irrad_ref / poa_global)
     Rs = Rs_ref
 
     return IL, I0, Rs, Rsh, nNsVth
@@ -607,16 +605,16 @@ def sapm(module, poa_direct, poa_diffuse, temp_cell, airmass_absolute, aoi):
     -------
     A DataFrame with the columns:
 
-        * Isc : Short-circuit current (A)
-        * Imp : Current at the maximum-power point (A)
-        * Voc : Open-circuit voltage (V)
-        * Vmp : Voltage at maximum-power point (V)
-        * Pmp : Power at maximum-power point (W)
-        * Ix : Current at module V = 0.5Voc, defines 4th point on I-V
+        * i_sc : Short-circuit current (A)
+        * I_mp : Current at the maximum-power point (A)
+        * v_oc : Open-circuit voltage (V)
+        * v_mp : Voltage at maximum-power point (V)
+        * p_mp : Power at maximum-power point (W)
+        * i_x : Current at module V = 0.5Voc, defines 4th point on I-V
           curve for modeling curve shape
-        * Ixx : Current at module V = 0.5(Voc+Vmp), defines 5th point on
+        * i_xx : Current at module V = 0.5(Voc+Vmp), defines 5th point on
           I-V curve for modeling curve shape
-        * Ee : Effective irradiance
+        * effective_irradiance : Effective irradiance
 
     Notes
     -----
@@ -687,34 +685,34 @@ def sapm(module, poa_direct, poa_diffuse, temp_cell, airmass_absolute, aoi):
 
     dfout = pd.DataFrame(index=Ee.index)
 
-    dfout['Isc'] = (
+    dfout['i_sc'] = (
         module['Isco'] * Ee * (1 + module['Aisc']*(temp_cell - T0)) )
 
-    dfout['Imp'] = ( module['Impo'] *
+    dfout['i_mp'] = ( module['Impo'] *
         (module['C0']*Ee + module['C1']*(Ee**2)) *
         (1 + module['Aimp']*(temp_cell - T0)) )
 
-    dfout['Voc'] = (( module['Voco'] +
+    dfout['v_oc'] = (( module['Voco'] +
         module['#Series']*delta*np.log(Ee) + Bvoco*(temp_cell - T0) )
         .clip_lower(0))
 
-    dfout['Vmp'] = ( module['Vmpo'] +
+    dfout['v_mp'] = ( module['Vmpo'] +
         module['C2']*module['#Series']*delta*np.log(Ee) +
         module['C3']*module['#Series']*((delta*np.log(Ee)) ** 2) +
         Bvmpo*(temp_cell - T0) ).clip_lower(0)
 
-    dfout['Pmp'] = dfout['Imp'] * dfout['Vmp']
+    dfout['p_mp'] = dfout['i_mp'] * dfout['v_mp']
 
-    dfout['Ix'] = ( module['IXO'] *
+    dfout['i_x'] = ( module['IXO'] *
         (module['C4']*Ee + module['C5']*(Ee**2)) *
         (1 + module['Aisc']*(temp_cell - T0)) )
 
     # the Ixx calculation in King 2004 has a typo (mixes up Aisc and Aimp)
-    dfout['Ixx'] = ( module['IXXO'] *
+    dfout['i_xx'] = ( module['IXXO'] *
         (module['C6']*Ee + module['C7']*(Ee**2)) *
         (1 + module['Aisc']*(temp_cell - T0)) )
 
-    dfout['Ee'] = Ee
+    dfout['effective_irradiance'] = Ee
 
     return dfout
 
@@ -768,7 +766,8 @@ def sapm_celltemp(irrad, wind, temp, model='open_rack_cell_glassback'):
 
     Returns
     --------
-    dict with keys tcell and tmodule. Values in degrees C.
+    DataFrame with columns 'temp_cell' and 'temp_module'.
+    Values in degrees C.
 
     References
     ----------
@@ -799,14 +798,15 @@ def sapm_celltemp(irrad, wind, temp, model='open_rack_cell_glassback'):
 
     E0 = 1000. # Reference irradiance
     
-    tmodule = irrad*np.exp(a + b*wind) + temp
+    temp_module = pd.Series(irrad*np.exp(a + b*wind) + temp)
 
-    tcell = tmodule + (irrad / E0)*(deltaT)
+    temp_cell = temp_module + (irrad / E0)*(deltaT)
 
-    return {'tcell':tcell, 'tmodule':tmodule}
+    return pd.DataFrame({'temp_cell': temp_cell, 'temp_module': temp_module})
     
     
-def singlediode(module, IL, I0, Rs, Rsh, nNsVth, **kwargs):
+def singlediode(module, photocurrent, saturation_current,
+                resistance_series, resistance_shunt, nNsVth):
     '''
     Solve the single-diode model to obtain a photovoltaic IV curve.
 
@@ -821,65 +821,56 @@ def singlediode(module, IL, I0, Rs, Rsh, nNsVth, **kwargs):
     are described later. Returns a DataFrame which contains
     the 5 points on the I-V curve specified in SAND2004-3535 [3]. 
     If all IL, I0, Rs, Rsh, and nNsVth are scalar, a single curve
-    will be returned, if any are DataFrames (of the same length), multiple IV
+    will be returned, if any are Series (of the same length), multiple IV
     curves will be calculated.
     
     The input parameters can be calculated using calcparams_desoto from 
-    meterological data. 
+    meteorological data. 
     
     Parameters
     ----------
     module : DataFrame
         A DataFrame defining the SAPM performance parameters.
 
-    IL : float or DataFrame
+    photocurrent : float or Series
         Light-generated current (photocurrent) in amperes under desired IV
-        curve conditions. 
+        curve conditions. Often abbreviated ``I_L``.
 
-    I0 : float or DataFrame
+    saturation_current : float or Series
         Diode saturation current in amperes under desired IV curve
-        conditions. 
+        conditions. Often abbreviated ``I_0``.
 
-    Rs : float or DataFrame
-        Series resistance in ohms under desired IV curve conditions. 
+    resistance_series : float or Series
+        Series resistance in ohms under desired IV curve conditions.
+        Often abbreviated ``Rs``.
 
-    Rsh : float or DataFrame
-        Shunt resistance in ohms under desired IV curve conditions. May
-        be a scalar or DataFrame, but DataFrames must be of same length as all
-        other input DataFrames.
+    resistance_shunt : float or Series
+        Shunt resistance in ohms under desired IV curve conditions.
+        Often abbreviated ``Rsh``.
 
-    nNsVth : float or DataFrame
+    nNsVth : float or Series
         The product of three components. 1) The usual diode ideal 
         factor (n), 2) the number of cells in series (Ns), and 3) the cell 
         thermal voltage under the desired IV curve conditions (Vth).
         The thermal voltage of the cell (in volts) may be calculated as 
-        k*Tcell/q, where k is Boltzmann's constant (J/K), Tcell is the
-        temperature of the p-n junction in Kelvin, and q is the elementary 
-        charge of an electron (coulombs). 
-        
-    Other Parameters
-    ----------------
-    NumPoints : integer
-        Number of points in the desired IV curve (optional). Must be a finite 
-        scalar value. Non-integer values will be rounded to the next highest
-        integer (ceil). If ceil(NumPoints) is < 2, no IV curves will be produced
-        (i.e. Result.V and Result.I will not be generated). The default
-        value is 0, resulting in no calculation of IV points other than
-        those specified in [3].
+        ``k*temp_cell/q``, where k is Boltzmann's constant (J/K),
+        temp_cell is the temperature of the p-n junction in Kelvin,
+        and q is the charge of an electron (coulombs). 
 
     Returns
     -------
-    A DataFrame with the following fields. All fields have the
-    same number of rows as the largest input DataFrame:
+    If ``photocurrent`` is a Series, a DataFrame with the following columns.
+    All columns have the same number of rows as the largest input DataFrame.
     
-    * Result.Isc -  short circuit current in amperes.
-    * Result.Voc -  open circuit voltage in volts.
-    * Result.Imp -  current at maximum power point in amperes. 
-    * Result.Vmp -  voltage at maximum power point in volts.
-    * Result.Pmp -  power at maximum power point in watts.
-    * Result.Ix -  current, in amperes, at V = 0.5*Voc.
-    * Result.Ixx -  current, in amperes, at V = 0.5*(Voc+Vmp).
-
+    If ``photocurrent`` is a scalar, a dict with the following keys.
+    
+    * i_sc -  short circuit current in amperes.
+    * v_oc -  open circuit voltage in volts.
+    * i_mp -  current at maximum power point in amperes. 
+    * v_mp -  voltage at maximum power point in volts.
+    * p_mp -  power at maximum power point in watts.
+    * i_x -  current, in amperes, at ``v = 0.5*v_oc``.
+    * i_xx -  current, in amperes, at ``V = 0.5*(v_oc+v_mp)``.
 
     Notes
     -----
@@ -904,39 +895,37 @@ def singlediode(module, IL, I0, Rs, Rsh, nNsVth, **kwargs):
     sapm
     calcparams_desoto
     '''
+    pvl_logger.debug('pvsystem.singlediode')
+    
+    # Find short circuit current using Lambert W
+    i_sc = i_from_v(resistance_shunt, resistance_series, nNsVth, 0.01,
+                    saturation_current, photocurrent)
+    
+    params = {'r_sh': resistance_shunt,
+              'r_s': resistance_series,
+              'nNsVth': nNsVth,
+              'i_0': saturation_current,
+              'i_l': photocurrent}
+
+    __, v_oc = _golden_sect_DataFrame(params, 0, module['V_oc_ref']*1.6,
+                                      _v_oc_optfcn)
 
-    # Find Isc using Lambert W
-    Isc = I_from_V(Rsh=Rsh, Rs=Rs, nNsVth=nNsVth, V=0.01, I0=I0, IL=IL)
-
-    # If passed a dataframe, output a dataframe, if passed a list or scalar,
-    # return a dict 
-    if isinstance(Rsh, pd.Series):
-        DFOut = pd.DataFrame({'Isc': Isc})
-        DFOut.index = Rsh.index
-    else:
-        DFOut = {'Isc': Isc}
-
-    DFOut['Rsh'] = Rsh
-    DFOut['Rs'] = Rs
-    DFOut['nNsVth'] = nNsVth
-    DFOut['I0'] = I0
-    DFOut['IL'] = IL
-
-    __, Voc_return = _golden_sect_DataFrame(DFOut, 0, module.V_oc_ref*1.6,
-                                            _Voc_optfcn)
-    Voc = Voc_return.copy()
-
-    Pmp, Vmax = _golden_sect_DataFrame(DFOut, 0, module.V_oc_ref*1.14,
-                                       _pwr_optfcn)
-    Imax = I_from_V(Rsh=Rsh, Rs=Rs, nNsVth=nNsVth, V=Vmax, I0=I0, IL=IL)
+    p_mp, v_mp = _golden_sect_DataFrame(params, 0, module['V_oc_ref']*1.14,
+                                        _pwr_optfcn)
+    
     # Invert the Power-Current curve. Find the current where the inverted power
-    # is minimized. This is Imax. Start the optimization at Voc/2
+    # is minimized. This is i_mp. Start the optimization at v_oc/2
+    i_mp = i_from_v(resistance_shunt, resistance_series, nNsVth, v_mp,
+                    saturation_current, photocurrent)
 
     # Find Ix and Ixx using Lambert W
-    Ix = I_from_V(Rsh=Rsh, Rs=Rs, nNsVth=nNsVth, V=.5*Voc, I0=I0, IL=IL)
-    Ixx = I_from_V(Rsh=Rsh, Rs=Rs, nNsVth=nNsVth, V=0.5*(Voc+Vmax), I0=I0,
-                   IL=IL)
+    i_x = i_from_v(resistance_shunt, resistance_series, nNsVth,
+                    0.5*v_oc, saturation_current, photocurrent)
+
+    i_xx = i_from_v(resistance_shunt, resistance_series, nNsVth,
+                    0.5*(v_oc+v_mp), saturation_current, photocurrent)
 
+    # @wholmgren: need to move this stuff to a different function
 #     If the user says they want a curve of with number of points equal to
 #     NumPoints (must be >=2), then create a voltage array where voltage is
 #     zero in the first column, and Voc in the last column. Number of columns
@@ -951,30 +940,37 @@ def singlediode(module, IL, I0, Rs, Rsh, nNsVth, **kwargs):
 #        Result.I = I_from_V(Rsh*s, Rs*s, nNsVth*s, Result.V, I0*s, IL*s);
 #     end
     
-    DFOut['Imp'] = Imax
-    DFOut['Voc'] = Voc
-    DFOut['Vmp'] = Vmax
-    DFOut['Pmp'] = Pmp
-    DFOut['Ix'] = Ix
-    DFOut['Ixx'] = Ixx
+    dfout = {}
+    dfout['i_sc'] = i_sc
+    dfout['i_mp'] = i_mp
+    dfout['v_oc'] = v_oc
+    dfout['v_mp'] = v_mp
+    dfout['p_mp'] = p_mp
+    dfout['i_x'] = i_x
+    dfout['i_xx'] = i_xx
+    
+    try:
+        dfout = pd.DataFrame(dfout, index=photocurrent.index)
+    except AttributeError:
+        pass
 
-    return DFOut
+    return dfout
 
 
 # Created April,2014
 # Author: Rob Andrews, Calama Consulting
 
-def _golden_sect_DataFrame(df, VL, VH, func):
+def _golden_sect_DataFrame(params, VL, VH, func):
     '''
     Vectorized golden section search for finding MPPT 
     from a dataframe timeseries.
 
     Parameters
     ----------
-    df : DataFrame
-        Dataframe containing a timeseries of inputs to the function
-        to be optimized.
-        Each row should represent an independant optimization
+    params : dict
+        Dictionary containing scalars or arrays
+        of inputs to the function to be optimized.
+        Each row should represent an independent optimization.
 
     VL: float
         Lower bound of the optimization
@@ -983,7 +979,7 @@ def _golden_sect_DataFrame(df, VL, VH, func):
         Upper bound of the optimization
 
     func: function
-        Function to be optimized must be in the form f(dataframe, x)
+        Function to be optimized must be in the form f(array-like, x)
 
     Returns
     -------
@@ -997,92 +993,137 @@ def _golden_sect_DataFrame(df, VL, VH, func):
     -----
     This funtion will find the MAXIMUM of a function
     '''
-
-    df['VH']=VH
-    df['VL']=VL
+    
+    df = params
+    df['VH'] = VH
+    df['VL'] = VL
       
-    err=df['VH']-df['VL']
-    errflag=True
-    iterations=0
+    err = df['VH'] - df['VL']
+    errflag = True
+    iterations = 0
+    
     while errflag:
 
-        phi=(np.sqrt(5)-1)/2*(df['VH']-df['VL'])
-        df['V1']=df['VL']+phi
-        df['V2']=df['VH']-phi
+        phi = (np.sqrt(5)-1)/2*(df['VH']-df['VL'])
+        df['V1'] = df['VL'] + phi
+        df['V2'] = df['VH'] - phi
         
-        df['f1']=func(df,'V1')
-        df['f2']=func(df,'V2')
-        df['SW_Flag']=df['f1']>df['f2']
+        df['f1'] = func(df, 'V1')
+        df['f2'] = func(df, 'V2')
+        df['SW_Flag'] = df['f1'] > df['f2']
         
-        df['VL']=df['V2']*df['SW_Flag']+df['VL']*(~df['SW_Flag'])
-        df['VH']=df['V1']*~df['SW_Flag']+df['VH']*(df['SW_Flag'])
+        df['VL'] = df['V2']*df['SW_Flag'] + df['VL']*(~df['SW_Flag'])
+        df['VH'] = df['V1']*~df['SW_Flag'] + df['VH']*(df['SW_Flag'])
         
-        err=(df['V1']-df['V2'])
-        if isinstance(df,pd.DataFrame):
-            errflag=all(abs(err)>.01)
-        else:
-            errflag=(abs(err)>.01)
+        err = df['V1'] - df['V2']
+        try:
+            errflag = (abs(err)>.01).all()
+        except ValueError:
+            errflag = (abs(err)>.01)
 
-        iterations=iterations+1
+        iterations += 1
 
-        if iterations >50:
+        if iterations > 50:
             raise Exception("EXCEPTION:iterations exeeded maximum (50)")
 
-    return func(df,'V1') , df['V1']
+    return func(df, 'V1'), df['V1']
 
 
 def _pwr_optfcn(df, loc):
     '''
-    Function to find power from I_from_V.
+    Function to find power from ``i_from_v``.
     '''
 
-    I = I_from_V(Rsh=df['Rsh'], Rs=df['Rs'], nNsVth=df['nNsVth'], V=df[loc],
-                 I0=df['I0'], IL=df['IL'])
+    I = i_from_v(df['r_sh'], df['r_s'], df['nNsVth'],
+                 df[loc], df['i_0'], df['i_l'])
     return I*df[loc]
 
 
-def _Voc_optfcn(df, loc):
+def _v_oc_optfcn(df, loc):
     '''
-    Function to find V_oc from I_from_V.
+    Function to find the open circuit voltage from ``i_from_v``.
     '''
-    I = -abs(I_from_V(Rsh=df['Rsh'], Rs=df['Rs'], nNsVth=df['nNsVth'],
-                      V=df[loc], I0=df['I0'], IL=df['IL']))
+    I = -abs(i_from_v(df['r_sh'], df['r_s'], df['nNsVth'],
+                      df[loc], df['i_0'], df['i_l']))
     return I
 
 
-def I_from_V(Rsh, Rs, nNsVth, V, I0, IL):
+def i_from_v(resistance_shunt, resistance_series, nNsVth, voltage,
+             saturation_current, photocurrent):
     '''
-    Calculates I from V per Eq 2 Jain and Kapoor 2004
-    uses Lambert W implemented in wapr_vec.m
-    Rsh, nVth, V, I0, IL can all be DataFrames
-    Rs can be a DataFrame, but should be a scalar.
+    Calculates current from voltage per Eq 2 Jain and Kapoor 2004 [1].
+
+    Parameters
+    ----------
+    resistance_series : float or Series
+        Series resistance in ohms under desired IV curve conditions.
+        Often abbreviated ``Rs``.
+
+    resistance_shunt : float or Series
+        Shunt resistance in ohms under desired IV curve conditions.
+        Often abbreviated ``Rsh``.
+
+    saturation_current : float or Series
+        Diode saturation current in amperes under desired IV curve
+        conditions. Often abbreviated ``I_0``.
+
+    nNsVth : float or Series
+        The product of three components. 1) The usual diode ideal
+        factor (n), 2) the number of cells in series (Ns), and 3) the cell
+        thermal voltage under the desired IV curve conditions (Vth).
+        The thermal voltage of the cell (in volts) may be calculated as
+        ``k*temp_cell/q``, where k is Boltzmann's constant (J/K),
+        temp_cell is the temperature of the p-n junction in Kelvin,
+        and q is the charge of an electron (coulombs).
+
+    photocurrent : float or Series
+        Light-generated current (photocurrent) in amperes under desired IV
+        curve conditions. Often abbreviated ``I_L``.
+
+    Returns
+    -------
+    current : np.array
+
+    References
+    ----------
+    [1] A. Jain, A. Kapoor, "Exact analytical solutions of the parameters of
+    real solar cells using Lambert W-function", Solar Energy Materials
+    and Solar Cells, 81 (2004) 269-277.
     '''
     try:
         from scipy.special import lambertw
     except ImportError:
         raise ImportError('This function requires scipy')
     
-    argW = (Rs*I0*Rsh * np.exp(Rsh*(Rs*(IL+I0)+V) /
-            (nNsVth*(Rs+Rsh))) / (nNsVth*(Rs + Rsh)) )
-    inputterm = lambertw(argW)
+    Rsh = resistance_shunt
+    Rs = resistance_series
+    I0 = saturation_current
+    IL = photocurrent
+    V = voltage
+
+    argW = (Rs*I0*Rsh *
+            np.exp( Rsh*(Rs*(IL+I0)+V) / (nNsVth*(Rs+Rsh)) ) /
+            (nNsVth*(Rs + Rsh)) )
+    lambertwterm = lambertw(argW)
+    pvl_logger.debug('argW: {}, lambertwterm{}'.format(argW, lambertwterm))
 
     # Eqn. 4 in Jain and Kapoor, 2004
-    I = -V/(Rs + Rsh) - (nNsVth/Rs) * inputterm + Rsh*(IL + I0)/(Rs + Rsh)
+    I = -V/(Rs + Rsh) - (nNsVth/Rs)*lambertwterm + Rsh*(IL + I0)/(Rs + Rsh)
     
     return I.real
 
 
-def snlinverter(inverter, Vmp, Pmp):
+def snlinverter(inverter, v_dc, p_dc):
     '''
     Converts DC power and voltage to AC power using 
     Sandia's Grid-Connected PV Inverter model.
 
-    Determine the AC power output of an inverter given the DC voltage, DC
+    Determines the AC power output of an inverter given the DC voltage, DC
     power, and appropriate Sandia Grid-Connected Photovoltaic Inverter
-    Model parameters. The output, ACPower, is clipped at the maximum power
+    Model parameters. The output, ac_power, is clipped at the maximum power
     output, and gives a negative power during low-input power conditions,
     but does NOT account for maximum power point tracking voltage windows
-    nor maximum current or voltage limits on the inverter. 
+    nor maximum current or voltage limits on the inverter.
 
     Parameters
     ----------
@@ -1122,22 +1163,22 @@ def snlinverter(inverter, Vmp, Pmp):
                  maintain circuitry required to sense PV array voltage (W)
         ======   ============================================================
 
-    Vdc : float or DataFrame
+    v_dc : float or Series
         DC voltages, in volts, which are provided as input to the inverter. 
         Vdc must be >= 0.
-    Pdc : float or DataFrame
+    p_dc : float or Series
         A scalar or DataFrame of DC powers, in watts, which are provided
         as input to the inverter. Pdc must be >= 0.
 
     Returns
     -------
-    ACPower : float or DataFrame
+    ac_power : float or Series
         Modeled AC power output given the input 
-        DC voltage, Vdc, and input DC power, Pdc. When ACPower would be 
+        DC voltage, Vdc, and input DC power, Pdc. When ac_power would be 
         greater than Pac0, it is set to Pac0 to represent inverter 
-        "clipping". When ACPower would be less than Ps0 (startup power
-        required), then ACPower is set to -1*abs(Pnt) to represent nightly 
-        power losses. ACPower is not adjusted for maximum power point
+        "clipping". When ac_power would be less than Ps0 (startup power
+        required), then ac_power is set to -1*abs(Pnt) to represent nightly 
+        power losses. ac_power is not adjusted for maximum power point
         tracking (MPPT) voltage windows or maximum current limits of the
         inverter.
 
@@ -1164,11 +1205,18 @@ def snlinverter(inverter, Vmp, Pmp):
     C3 = inverter['C3']
     Pnt = inverter['Pnt']
 
-    A = Pdco*((1 + C1*((Vmp - Vdco))))
-    B = Pso*((1 + C2*((Vmp - Vdco))))
-    C = C0*((1 + C3*((Vmp - Vdco))))
-    ACPower = ((Paco / (A - B)) - C*((A - B)))*((Pmp - B)) + C*((Pmp - B) ** 2)
-    ACPower[ACPower > Paco] = Paco
-    ACPower[ACPower < Pso] = - 1.0 * abs(Pnt)
+    A = Pdco * (1 + C1*(v_dc - Vdco))
+    B = Pso * (1 + C2*(v_dc - Vdco))
+    C = C0 * (1 + C3*(v_dc - Vdco))
+    
+    # ensures that function works with scalar or Series input
+    p_dc = pd.Series(p_dc)
+    
+    ac_power = ( Paco/(A-B) - C*(A-B) ) * (p_dc-B) + C*((p_dc-B)**2)
+    ac_power[ac_power > Paco] = Paco
+    ac_power[ac_power < Pso] = - 1.0 * abs(Pnt)
+    
+    if len(ac_power) == 1:
+        ac_power = ac_power.ix[0]
 
-    return ACPower
+    return ac_power
diff --git a/pvlib/test/test_atmosphere.py b/pvlib/test/test_atmosphere.py
index 4517628db4..f211361242 100644
--- a/pvlib/test/test_atmosphere.py
+++ b/pvlib/test/test_atmosphere.py
@@ -33,29 +33,29 @@ def test_pres2alt():
 def test_alt2press():
     atmosphere.pres2alt(1000)
 
+
 # two functions combined will generate unique unit tests for each model
 def test_airmasses():
     models = ['simple', 'kasten1966', 'youngirvine1967', 'kastenyoung1989',
               'gueymard1993', 'young1994', 'pickering2002', 'invalid']
     for model in models:
-        yield run_airmass, ephem_data['zenith'], model
-    
-def run_airmass(zenith, model):
+        yield run_airmass, model, ephem_data['zenith']
+
+
+def run_airmass(model, zenith):
     atmosphere.relativeairmass(zenith, model)
-    
-    
+
     
 def test_absoluteairmass():
     relative_am = atmosphere.relativeairmass(ephem_data['zenith'], 'simple')
     atmosphere.absoluteairmass(relative_am)
     atmosphere.absoluteairmass(relative_am, pressure=100000)
-    
-    
+
 
 def test_absoluteairmass_numeric():
     atmosphere.absoluteairmass(2)
-    
-    
+
+
 def test_absoluteairmass_nan():
     np.testing.assert_equal(np.nan, atmosphere.absoluteairmass(np.nan))
-    
+
diff --git a/pvlib/test/test_clearsky.py b/pvlib/test/test_clearsky.py
index ad4c2c6fed..0afd8675c1 100644
--- a/pvlib/test/test_clearsky.py
+++ b/pvlib/test/test_clearsky.py
@@ -46,9 +46,9 @@ def test_ineichen_airmass():
 
 def test_ineichen_keys():
     clearsky_data = clearsky.ineichen(times, tus, linke_turbidity=3)
-    assert 'GHI' in clearsky_data.columns
-    assert 'DNI' in clearsky_data.columns
-    assert 'DHI' in clearsky_data.columns
+    assert 'ghi' in clearsky_data.columns
+    assert 'dni' in clearsky_data.columns
+    assert 'dhi' in clearsky_data.columns
 
 # test the haurwitz clear sky implementation
 def test_haurwitz():
@@ -56,4 +56,4 @@ def test_haurwitz():
 
 def test_haurwitz_keys():
     clearsky_data = clearsky.haurwitz(ephem_data['zenith'])
-    assert 'GHI' in clearsky_data.columns
+    assert 'ghi' in clearsky_data.columns
diff --git a/pvlib/test/test_irradiance.py b/pvlib/test/test_irradiance.py
index 4b8fc14f27..9df3cc208a 100644
--- a/pvlib/test/test_irradiance.py
+++ b/pvlib/test/test_irradiance.py
@@ -30,7 +30,7 @@
 
 dni_et = irradiance.extraradiation(times.dayofyear)
 
-ghi = irrad_data['GHI']
+ghi = irrad_data['ghi']
 
 
 # the test functions. these are almost all functional tests.
@@ -99,7 +99,7 @@ def test_isotropic_float():
 
 
 def test_isotropic_series():
-    irradiance.isotropic(40, irrad_data['DHI'])
+    irradiance.isotropic(40, irrad_data['dhi'])
 
 
 def test_klucher_series_float():
@@ -107,58 +107,58 @@ def test_klucher_series_float():
 
 
 def test_klucher_series():
-    irradiance.klucher(40, 180, irrad_data['DHI'], irrad_data['GHI'],
+    irradiance.klucher(40, 180, irrad_data['dhi'], irrad_data['ghi'],
                        ephem_data['apparent_zenith'],
                        ephem_data['apparent_azimuth'])
 
 
 def test_haydavies():
-    irradiance.haydavies(40, 180, irrad_data['DHI'], irrad_data['DNI'],
+    irradiance.haydavies(40, 180, irrad_data['dhi'], irrad_data['dni'],
                          dni_et,
                          ephem_data['apparent_zenith'],
                          ephem_data['apparent_azimuth'])
 
 
 def test_reindl():
-    irradiance.reindl(40, 180, irrad_data['DHI'], irrad_data['DNI'],
-                      irrad_data['GHI'], dni_et,
+    irradiance.reindl(40, 180, irrad_data['dhi'], irrad_data['dni'],
+                      irrad_data['ghi'], dni_et,
                       ephem_data['apparent_zenith'],
                       ephem_data['apparent_azimuth'])
 
 
 def test_king():
-    irradiance.king(40, irrad_data['DHI'], irrad_data['GHI'],
+    irradiance.king(40, irrad_data['dhi'], irrad_data['ghi'],
                     ephem_data['apparent_zenith'])
 
 
 def test_perez():
     AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
-    irradiance.perez(40, 180, irrad_data['DHI'], irrad_data['DNI'],
+    irradiance.perez(40, 180, irrad_data['dhi'], irrad_data['dni'],
                      dni_et, ephem_data['apparent_zenith'],
                      ephem_data['apparent_azimuth'], AM)
 
 
 def test_globalinplane():
-    AOI = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
+    aoi = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
                          ephem_data['apparent_azimuth'])
-    AM = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
+    airmass = atmosphere.relativeairmass(ephem_data['apparent_zenith'])
     gr_sand = irradiance.grounddiffuse(40, ghi, surface_type='sand')
     diff_perez = irradiance.perez(
-        40, 180, irrad_data['DHI'], irrad_data['DNI'], dni_et,
-        ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'], AM)
+        40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
+        ephem_data['apparent_zenith'], ephem_data['apparent_azimuth'], airmass)
     irradiance.globalinplane(
-        AOI=AOI, DNI=irrad_data['DNI'], In_Plane_SkyDiffuse=diff_perez,
-        GR=gr_sand)
+        aoi=aoi, dni=irrad_data['dni'], poa_sky_diffuse=diff_perez,
+        poa_ground_diffuse=gr_sand)
 
 
-# test DISC
 def test_disc_keys():
     clearsky_data = clearsky.ineichen(times, tus, linke_turbidity=3)
-    disc_data = irradiance.disc(clearsky_data['GHI'], ephem_data['zenith'], 
+    disc_data = irradiance.disc(clearsky_data['ghi'], ephem_data['zenith'], 
                               ephem_data.index)
-    assert 'DNI_gen_DISC' in disc_data.columns
-    assert 'Kt_gen_DISC' in disc_data.columns
-    assert 'AM' in disc_data.columns
+    assert 'dni' in disc_data.columns
+    assert 'kt' in disc_data.columns
+    assert 'airmass' in disc_data.columns
+
 
 def test_disc_value():
     times = pd.DatetimeIndex(['2014-06-24T12-0700','2014-06-24T18-0700'])
@@ -166,13 +166,14 @@ def test_disc_value():
     zenith = pd.Series([10.567, 72.469], index=times)
     pressure = 93193.
     disc_data = irradiance.disc(ghi, zenith, times, pressure=pressure)
-    assert_almost_equal(disc_data['DNI_gen_DISC'].values,
+    assert_almost_equal(disc_data['dni'].values,
                         np.array([830.46, 676.09]), 1)
 
+
 def test_dirint():
     clearsky_data = clearsky.ineichen(times, tus, linke_turbidity=3)
     pressure = 93193.
-    dirint_data = irradiance.dirint(clearsky_data['GHI'], ephem_data['zenith'], 
+    dirint_data = irradiance.dirint(clearsky_data['ghi'], ephem_data['zenith'], 
                                     ephem_data.index, pressure=pressure)
 
 def test_dirint_value():
diff --git a/pvlib/test/test_pvsystem.py b/pvlib/test/test_pvsystem.py
index 79b03058eb..518ca381e4 100644
--- a/pvlib/test/test_pvsystem.py
+++ b/pvlib/test/test_pvsystem.py
@@ -9,6 +9,7 @@
 import pandas as pd
 
 from nose.tools import assert_equals, assert_almost_equals
+from pandas.util.testing import assert_series_equal, assert_frame_equal
 
 from pvlib import tmy
 from pvlib import pvsystem
@@ -104,16 +105,17 @@ def test_retrieve_sam_network():
 def test_sapm():
     modules = sam_data['sandiamod']
     module = modules.Canadian_Solar_CS5P_220M___2009_
-    
-    sapm = pvsystem.sapm(module, irrad_data.DNI, irrad_data.DHI, 25, am, aoi)
-    
-    sapm = pvsystem.sapm(module.to_dict(), irrad_data.DNI,
-                         irrad_data.DHI, 25, am, aoi)
+
+    sapm = pvsystem.sapm(module, irrad_data['dni'],
+                         irrad_data['dhi'], 25, am, aoi)
+
+    sapm = pvsystem.sapm(module.to_dict(), irrad_data['dni'],
+                         irrad_data['dhi'], 25, am, aoi)
 
 
 def test_calcparams_desoto():
     cecmodule = sam_data['cecmod'].Example_Module 
-    pvsystem.calcparams_desoto(S=irrad_data.GHI,
+    pvsystem.calcparams_desoto(irrad_data['ghi'],
                                temp_cell=25,
                                alpha_isc=cecmodule['Alpha_sc'],
                                module_parameters=cecmodule,
@@ -121,32 +123,80 @@ def test_calcparams_desoto():
                                dEgdT=-0.0002677)
 
 
-def test_singlediode():  
+def test_i_from_v():
+    output = pvsystem.i_from_v(20, .1, .5, 40, 6e-7, 7)
+    assert_almost_equals(-299.746389916, output, 5)
+
+
+def test_singlediode_series():  
     cecmodule = sam_data['cecmod'].Example_Module 
-    IL, I0, Rs, Rsh, nNsVth = pvsystem.calcparams_desoto(S=irrad_data.GHI,
+    IL, I0, Rs, Rsh, nNsVth = pvsystem.calcparams_desoto(
+                                         irrad_data['ghi'],
                                          temp_cell=25,
                                          alpha_isc=cecmodule['Alpha_sc'],
                                          module_parameters=cecmodule,
                                          EgRef=1.121,
                                          dEgdT=-0.0002677)                       
-    pvsystem.singlediode(module=cecmodule, IL=IL, I0=I0, Rs=Rs, Rsh=Rsh,
-                         nNsVth=nNsVth)
+    out = pvsystem.singlediode(cecmodule, IL, I0, Rs, Rsh, nNsVth)
+    assert isinstance(out, pd.DataFrame)
+
+
+def test_singlediode_series():  
+    cecmodule = sam_data['cecmod'].Example_Module                       
+    out = pvsystem.singlediode(cecmodule, 7, 6e-7, .1, 20, .5)
+    expected = {'i_xx': 4.2549732697234193,
+                'i_mp': 6.1390251797935704,
+                'v_oc': 8.1147298764528042,
+                'p_mp': 38.194165464983037,
+                'i_x': 6.7556075876880621,
+                'i_sc': 6.9646747613963198,
+                'v_mp': 6.221535886625464}
+    assert isinstance(out, dict)
+    for k, v in out.items():
+        assert_almost_equals(expected[k], v, 5)
 
 
 def test_sapm_celltemp():
     default = pvsystem.sapm_celltemp(900, 5, 20)
-    assert_almost_equals(43.509, default['tcell'], 3)
-    assert_almost_equals(40.809, default['tmodule'], 3)
-    assert_equals(default, pvsystem.sapm_celltemp(900, 5, 20,
-                                                  [-3.47, -.0594, 3]))
+    assert_almost_equals(43.509, default.ix[0, 'temp_cell'], 3)
+    assert_almost_equals(40.809, default.ix[0, 'temp_module'], 3)
+    assert_frame_equal(default, pvsystem.sapm_celltemp(900, 5, 20,
+                                                       [-3.47, -.0594, 3]))
+
+
+def test_sapm_celltemp_with_index():
+    times = pd.DatetimeIndex(start='2015-01-01', end='2015-01-02', freq='12H')
+    temps = pd.Series([0, 10, 5], index=times)
+    irrads = pd.Series([0, 500, 0], index=times)
+    winds = pd.Series([10, 5, 0], index=times)
+
+    pvtemps = pvsystem.sapm_celltemp(irrads, winds, temps)
+    
+    expected = pd.DataFrame({'temp_cell':[0., 23.06066166, 5.],
+                             'temp_module':[0., 21.56066166, 5.]},
+                            index=times)
+    
+    assert_frame_equal(expected, pvtemps)
 
     
 def test_snlinverter():
     inverters = sam_data['sandiainverter']
     testinv = 'ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_'
-    vdcs = pd.Series(np.linspace(0,50,51))
-    idcs = pd.Series(np.linspace(0,11,110))
+    vdcs = pd.Series(np.linspace(0,50,3))
+    idcs = pd.Series(np.linspace(0,11,3))
+    pdcs = idcs * vdcs
+
+    pacs = pvsystem.snlinverter(inverters[testinv], vdcs, pdcs)
+    assert_series_equal(pacs, pd.Series([-0.020000, 132.004308, 250.000000]))
+
+
+def test_snlinverter_float():
+    inverters = sam_data['sandiainverter']
+    testinv = 'ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_'
+    vdcs = 25.
+    idcs = 5.5
     pdcs = idcs * vdcs
 
     pacs = pvsystem.snlinverter(inverters[testinv], vdcs, pdcs)
+    assert_almost_equals(pacs, 132.004308, 5)