Merge pull request #32 from mguidon/odeimaiz-feature/mattward-srv

Odeimaiz feature/mattward srv

Author: odeimaiz

services/dy-2Dgraph/use-cases/mattward/notebook/mattward.ipynb

@@ -125,13 +125,13 @@
    "metadata": {
     "hide_input": false,
     "init_cell": true,
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a115ec48129d4fdeb52de4be6436c4e0",
+       "model_id": "98292748760d48df89e696132a9a86fa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -218,11 +218,11 @@
     "import pandas as pd\n",
     "from mpl_toolkits.mplot3d import axes3d\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "%matplotlib notebook\n",
     "\n",
     "\n",
-    "def create_predicted_compound_nerve_action(cv_path, t_path, ist_path, tst_path, qst_path, vpred_path, fixed_tst, plot_vs_qst, plot_vs_tCNAP):\n",
+    "def create_predicted_compound_nerve_action(cv_path, t_path, ist_path, tst_path, qst_path, vpred_path, lpred_path, fixed_tst, plot_vs_qst, plot_vs_tCNAP):\n",
     "    data_cv = pd.read_csv(cv_path, sep=',', header=None)\n",
     "    data_tcnap = pd.read_csv(t_path, sep=',', header=None)\n",
     "    data_ist = None\n",
@@ -233,16 +233,18 @@
     "        data_tst = pd.read_csv(tst_path, sep=',', header=None)\n",
     "    data_CAP = pd.read_csv(qst_path, sep=',', header=None)\n",
     "    data_vpred = pd.read_csv(vpred_path, sep=',', header=None)\n",
-    "\n",
+    "    data_lpred = pd.read_csv(lpred_path, sep=',', header=None)\n",
     "    dpi = 96\n",
     "    height = 1024\n",
     "    width = 800\n",
     "    fontsize = 16\n",
     "    \n",
+    "    fig = plt.figure(figsize=(width / dpi, height / dpi), dpi=dpi)\n",
+    "    fig.suptitle(r'Predicted Compound Nerve Action Potentials')\n",
+    "    gs = fig.add_gridspec(2, 1)\n",
+    "    ax1 = fig.add_subplot(gs[0, 0], projection='3d')\n",
+    "    ax2 = fig.add_subplot(gs[1, 0])\n",
     "    \n",
-    "    #plt.title(r'Predicted Compound Nerve Action Potentials', fontsize=20)\n",
-    "    fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(width / dpi, height / dpi), dpi=dpi, subplot_kw={'projection': '3d'})\n",
-    "    ax1.set_title(r'Predicted Compound Nerve Action Potentials', fontsize=20)\n",
     "    data_cv[data_cv>100] = None\n",
     "    x_axis = data_cv\n",
     "    x_label = r'CV (m/s)'\n",
@@ -264,7 +266,7 @@
     "        y_axis = data_CAP\n",
     "        y_label = r'$Q_{st}$ (nC)'\n",
     "       \n",
-    "    \n",
+    "\n",
     "    ax1.set_xlabel(x_label)\n",
     "    ax1.set_xscale(x_scale)\n",
     "    ax1.set_xlim(x_lim)\n",
@@ -273,8 +275,12 @@
     "    ax1.plot_wireframe(x_axis, y_axis, data_vpred, rstride=0, cstride=5)\n",
     "    ax1.grid(which='both')\n",
     "\n",
-    "#     plt.subplot(2,1,2)\n",
-    "    plt.show()\n"
+    "    ax2.set_xscale(x_scale)\n",
+    "    ax2.set_xlabel(x_label)\n",
+    "    ax2.set_ylabel(y_label)\n",
+    "    pcm = ax2.pcolormesh(np.nan_to_num(x_axis), y_axis, data_lpred)\n",
+    "    cbar = plt.colorbar(pcm, orientation='horizontal')\n",
+    "    cbar.ax.set_xlabel(\"%Activation\")\n"
    ]
   },
   {
@@ -309,7 +315,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "da207f7973b640eaa15469521f1229f1",
+       "model_id": "7ce21833358f4427becea66e7f84b4c5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -416,8 +422,7 @@
     "    with out2:\n",
     "        print(\"Current clicked.\", charge_phase_cb.value, time_cb.value, start_ist.value, end_ist.value, step_size_current.value, fixed_tst.value)\n",
     "        !execute_cnap.sh $model_id $sweep_param $start_ist.value $end_ist.value $step_size_current.value $fixed_tst.value        \n",
-    "        create_predicted_compound_nerve_action(cv_path='/home/jovyan/outputs/CV_plot.csv', t_path='/home/jovyan/outputs/t_plot.csv',  ist_path='/home/jovyan/outputs/Ist_plot.csv', tst_path='/home/jovyan/outputs/tst_plot.csv', qst_path='/home/jovyan/outputs/CAP_plot.csv', vpred_path='/home/jovyan/outputs/V_pred_plot.csv', fixed_tst=True, plot_vs_qst=charge_phase_cb.value, plot_vs_tCNAP=time_cb.value), \n",
-    "        move_files_to_output()\n",
+    "        create_predicted_compound_nerve_action(cv_path='/home/jovyan/outputs/CV_plot.csv', t_path='/home/jovyan/outputs/t_plot.csv',  ist_path='/home/jovyan/outputs/Ist_plot.csv', tst_path='/home/jovyan/outputs/tst_plot.csv', qst_path='/home/jovyan/outputs/CAP_plot.csv', vpred_path='/home/jovyan/outputs/V_pred_plot.csv', lpred_path='/home/jovyan/outputs/Lpred_plot.csv', fixed_tst=True, plot_vs_qst=charge_phase_cb.value, plot_vs_tCNAP=time_cb.value), \n",
     "predict_CNAPs_current.on_click(on_current_clicked)\n",
     "\n",
     "\n",
@@ -429,15 +434,21 @@
     "    with out2:\n",
     "        print(\"Time clicked.\", charge_phase_cb.value, time_cb.value, start_tst.value, end_tst.value, step_size_duration.value, fixed_ist.value)\n",
     "        !execute_cnap.sh $model_id $sweep_param $start_ist.value $end_ist.value $step_size_current.value $fixed_tst.value        \n",
-    "        create_predicted_compound_nerve_action(cv_path='/home/jovyan/outputs/CV_plot.csv', t_path='/home/jovyan/outputs/t_plot.csv',  ist_path='/home/jovyan/outputs/Ist_plot.csv', tst_path='/home/jovyan/outputs/tst_plot.csv', qst_path='/home/jovyan/outputs/CAP_plot.csv', vpred_path='/home/jovyan/outputs/V_pred_plot.csv', fixed_tst=False, plot_vs_qst=charge_phase_cb.value, plot_vs_tCNAP=time_cb.value), \n",
-    "        move_files_to_output()\n",
+    "        create_predicted_compound_nerve_action(cv_path='/home/jovyan/outputs/CV_plot.csv', t_path='/home/jovyan/outputs/t_plot.csv',  ist_path='/home/jovyan/outputs/Ist_plot.csv', tst_path='/home/jovyan/outputs/tst_plot.csv', qst_path='/home/jovyan/outputs/CAP_plot.csv', vpred_path='/home/jovyan/outputs/V_pred_plot.csv', lpred_path='/home/jovyan/outputs/Lpred_plot.csv', fixed_tst=False, plot_vs_qst=charge_phase_cb.value, plot_vs_tCNAP=time_cb.value), \n",
     "        \n",
     "predict_CNAPs_time.on_click(on_time_clicked)\n",
     "\n",
     "\n",
     "\n",
     "vertical_box"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {