diff --git a/doc/pub/week35/ipynb/week35.ipynb b/doc/pub/week35/ipynb/week35.ipynb
index 5da4560be..de001d307 100644
--- a/doc/pub/week35/ipynb/week35.ipynb
+++ b/doc/pub/week35/ipynb/week35.ipynb
@@ -400,10 +400,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -946,10 +943,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# matrix inversion to find beta\n",
@@ -968,10 +962,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
@@ -988,10 +979,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "Masses['Eapprox']  = ytilde\n",
@@ -1021,10 +1009,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def R2(y_data, y_model):\n",
@@ -1041,10 +1026,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(R2(Energies,ytilde))"
@@ -1060,10 +1042,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def MSE(y_data,y_model):\n",
@@ -1083,10 +1062,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def RelativeError(y_data,y_model):\n",
@@ -1121,10 +1097,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -1177,10 +1150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# equivalently in numpy\n",
@@ -1252,10 +1222,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1275,10 +1242,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.datasets import load_boston\n",
@@ -1300,10 +1264,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
@@ -1321,10 +1282,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# check for missing values in all the columns\n",
@@ -1341,10 +1299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# set the size of the figure\n",
@@ -1365,10 +1320,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# compute the pair wise correlation for all columns  \n",
@@ -1388,10 +1340,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure(figsize=(20, 5))\n",
@@ -1419,10 +1368,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n",
@@ -1439,10 +1385,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -1466,10 +1409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
@@ -1508,10 +1448,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# plotting the y_test vs y_pred\n",
@@ -1638,12 +1575,418 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.749765</td>\n",
+       "      <td>0.342680</td>\n",
+       "      <td>1.153036</td>\n",
+       "      <td>-0.252436</td>\n",
+       "      <td>0.981321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.514219</td>\n",
+       "      <td>0.221180</td>\n",
+       "      <td>-1.070043</td>\n",
+       "      <td>-0.189496</td>\n",
+       "      <td>0.255001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.458027</td>\n",
+       "      <td>0.435163</td>\n",
+       "      <td>-0.583595</td>\n",
+       "      <td>0.816847</td>\n",
+       "      <td>0.672721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.104411</td>\n",
+       "      <td>-0.531280</td>\n",
+       "      <td>1.029733</td>\n",
+       "      <td>-0.438136</td>\n",
+       "      <td>-1.118318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.618982</td>\n",
+       "      <td>1.541605</td>\n",
+       "      <td>-0.251879</td>\n",
+       "      <td>-0.842436</td>\n",
+       "      <td>0.184519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.937082</td>\n",
+       "      <td>0.731000</td>\n",
+       "      <td>1.361556</td>\n",
+       "      <td>-0.326238</td>\n",
+       "      <td>0.055676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.222400</td>\n",
+       "      <td>-1.443217</td>\n",
+       "      <td>-0.756352</td>\n",
+       "      <td>0.816454</td>\n",
+       "      <td>0.750445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.455947</td>\n",
+       "      <td>1.189622</td>\n",
+       "      <td>-1.690617</td>\n",
+       "      <td>-1.356399</td>\n",
+       "      <td>-1.232435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.544439</td>\n",
+       "      <td>-0.668172</td>\n",
+       "      <td>0.007315</td>\n",
+       "      <td>-0.612939</td>\n",
+       "      <td>1.299748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.733096</td>\n",
+       "      <td>-0.983310</td>\n",
+       "      <td>0.357508</td>\n",
+       "      <td>-1.613579</td>\n",
+       "      <td>1.470714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4\n",
+       "0 -1.749765  0.342680  1.153036 -0.252436  0.981321\n",
+       "1  0.514219  0.221180 -1.070043 -0.189496  0.255001\n",
+       "2 -0.458027  0.435163 -0.583595  0.816847  0.672721\n",
+       "3 -0.104411 -0.531280  1.029733 -0.438136 -1.118318\n",
+       "4  1.618982  1.541605 -0.251879 -0.842436  0.184519\n",
+       "5  0.937082  0.731000  1.361556 -0.326238  0.055676\n",
+       "6  0.222400 -1.443217 -0.756352  0.816454  0.750445\n",
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+       "8 -0.544439 -0.668172  0.007315 -0.612939  1.299748\n",
+       "9 -1.733096 -0.983310  0.357508 -1.613579  1.470714"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0   -0.175300\n",
+      "1    0.083527\n",
+      "2   -0.044334\n",
+      "3   -0.399836\n",
+      "4    0.331939\n",
+      "dtype: float64\n",
+      "0    1.069584\n",
+      "1    0.965548\n",
+      "2    1.018232\n",
+      "3    0.793167\n",
+      "4    0.918992\n",
+      "dtype: float64\n"
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+       "      <th></th>\n",
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+       "      <th>2</th>\n",
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+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.574465</td>\n",
+       "      <td>0.259153</td>\n",
+       "      <td>1.197370</td>\n",
+       "      <td>0.147400</td>\n",
+       "      <td>0.649382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.689519</td>\n",
+       "      <td>0.137652</td>\n",
+       "      <td>-1.025709</td>\n",
+       "      <td>0.210340</td>\n",
+       "      <td>-0.076938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.282727</td>\n",
+       "      <td>0.351636</td>\n",
+       "      <td>-0.539261</td>\n",
+       "      <td>1.216683</td>\n",
+       "      <td>0.340782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.070889</td>\n",
+       "      <td>-0.614808</td>\n",
+       "      <td>1.074067</td>\n",
+       "      <td>-0.038300</td>\n",
+       "      <td>-1.450257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.794282</td>\n",
+       "      <td>1.458078</td>\n",
+       "      <td>-0.207545</td>\n",
+       "      <td>-0.442600</td>\n",
+       "      <td>-0.147420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.112383</td>\n",
+       "      <td>0.647473</td>\n",
+       "      <td>1.405890</td>\n",
+       "      <td>0.073598</td>\n",
+       "      <td>-0.276263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.397700</td>\n",
+       "      <td>-1.526744</td>\n",
+       "      <td>-0.712018</td>\n",
+       "      <td>1.216290</td>\n",
+       "      <td>0.418506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.280647</td>\n",
+       "      <td>1.106095</td>\n",
+       "      <td>-1.646283</td>\n",
+       "      <td>-0.956563</td>\n",
+       "      <td>-1.564374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.369139</td>\n",
+       "      <td>-0.751699</td>\n",
+       "      <td>0.051649</td>\n",
+       "      <td>-0.213103</td>\n",
+       "      <td>0.967809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.557795</td>\n",
+       "      <td>-1.066837</td>\n",
+       "      <td>0.401842</td>\n",
+       "      <td>-1.213743</td>\n",
+       "      <td>1.138775</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4\n",
+       "0 -1.574465  0.259153  1.197370  0.147400  0.649382\n",
+       "1  0.689519  0.137652 -1.025709  0.210340 -0.076938\n",
+       "2 -0.282727  0.351636 -0.539261  1.216683  0.340782\n",
+       "3  0.070889 -0.614808  1.074067 -0.038300 -1.450257\n",
+       "4  1.794282  1.458078 -0.207545 -0.442600 -0.147420\n",
+       "5  1.112383  0.647473  1.405890  0.073598 -0.276263\n",
+       "6  0.397700 -1.526744 -0.712018  1.216290  0.418506\n",
+       "7 -0.280647  1.106095 -1.646283 -0.956563 -1.564374\n",
+       "8 -0.369139 -0.751699  0.051649 -0.213103  0.967809\n",
+       "9 -1.557795 -1.066837  0.401842 -1.213743  1.138775"
+      ]
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <th>6</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1    2    3    4\n",
+       "0  0.0  0.0  0.0  0.0  0.0\n",
+       "1  0.0  0.0  0.0  0.0  0.0\n",
+       "2  0.0  0.0  0.0  0.0  0.0\n",
+       "3  0.0  0.0  0.0  0.0  0.0\n",
+       "4  0.0  0.0  0.0  0.0  0.0\n",
+       "5  0.0  0.0  0.0  0.0  0.0\n",
+       "6  0.0  0.0  0.0  0.0  0.0\n",
+       "7  0.0  0.0  0.0  0.0  0.0\n",
+       "8  0.0  0.0  0.0  0.0  0.0\n",
+       "9  0.0  0.0  0.0  0.0  0.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import sklearn.linear_model as skl\n",
     "from sklearn.metrics import mean_squared_error\n",
@@ -1712,10 +2055,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.random.seed()\n",
@@ -1738,10 +2078,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -1792,10 +2129,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -2358,10 +2692,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -3062,12 +3393,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.22501987939529708\n",
+      "3.4265364730355325\n",
+      "[[1.06251574 3.06513214]\n",
+      " [3.06513214 9.90295583]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -3096,12 +3435,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.080263294546264\n",
+      "2.011002230765845\n",
+      "[[1.        0.7136592]\n",
+      " [0.7136592 1.       ]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 100\n",
@@ -3143,12 +3490,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.31787985 -1.14338084]\n",
+      " [ 0.04529807 -0.18207237]\n",
+      " [ 1.11176315  3.45031813]\n",
+      " [-1.91097714 -5.2907413 ]\n",
+      " [-0.19918861  1.18654759]\n",
+      " [ 0.84454053  2.34712647]\n",
+      " [ 0.82299467  1.93582947]\n",
+      " [-1.64434822 -5.13289015]\n",
+      " [ 1.10923753  3.75337971]\n",
+      " [ 0.13855987 -0.92411672]]\n",
+      "          0         1\n",
+      "0 -0.317880 -1.143381\n",
+      "1  0.045298 -0.182072\n",
+      "2  1.111763  3.450318\n",
+      "3 -1.910977 -5.290741\n",
+      "4 -0.199189  1.186548\n",
+      "5  0.844541  2.347126\n",
+      "6  0.822995  1.935829\n",
+      "7 -1.644348 -5.132890\n",
+      "8  1.109238  3.753380\n",
+      "9  0.138560 -0.924117\n",
+      "          0         1\n",
+      "0  1.000000  0.968568\n",
+      "1  0.968568  1.000000\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -3176,12 +3551,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     0         1         2         3         4         5         6         7   \\\n",
+      "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+      "1   0.0  0.063613  0.068251  0.071169  0.070546  0.069425  0.067581  0.065899   \n",
+      "2   0.0  0.068251  0.073991  0.077286  0.077098  0.076290  0.073977  0.072493   \n",
+      "3   0.0  0.071169  0.077286  0.083456  0.083469  0.082851  0.081789  0.080312   \n",
+      "4   0.0  0.070546  0.077098  0.083469  0.083869  0.083597  0.082364  0.081188   \n",
+      "5   0.0  0.069425  0.076290  0.082851  0.083597  0.083643  0.082304  0.081413   \n",
+      "6   0.0  0.067581  0.073977  0.081789  0.082364  0.082304  0.082005  0.080988   \n",
+      "7   0.0  0.065899  0.072493  0.080312  0.081188  0.081413  0.080988  0.080245   \n",
+      "8   0.0  0.064203  0.070945  0.078766  0.079908  0.080389  0.079865  0.079371   \n",
+      "9   0.0  0.062545  0.069397  0.077214  0.078591  0.079301  0.078699  0.078431   \n",
+      "10  0.0  0.061809  0.068087  0.076517  0.077496  0.077870  0.078063  0.077474   \n",
+      "11  0.0  0.060077  0.066459  0.074794  0.076005  0.076605  0.076675  0.076314   \n",
+      "12  0.0  0.058442  0.064904  0.073149  0.074564  0.075366  0.075332  0.075177   \n",
+      "13  0.0  0.056909  0.063432  0.071590  0.073188  0.074169  0.074047  0.074077   \n",
+      "14  0.0  0.055478  0.062046  0.070123  0.071881  0.073022  0.072826  0.073022   \n",
+      "\n",
+      "          8         9         10        11        12        13        14  \n",
+      "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
+      "1   0.064203  0.062545  0.061809  0.060077  0.058442  0.056909  0.055478  \n",
+      "2   0.070945  0.069397  0.068087  0.066459  0.064904  0.063432  0.062046  \n",
+      "3   0.078766  0.077214  0.076517  0.074794  0.073149  0.071590  0.070123  \n",
+      "4   0.079908  0.078591  0.077496  0.076005  0.074564  0.073188  0.071881  \n",
+      "5   0.080389  0.079301  0.077870  0.076605  0.075366  0.074169  0.073022  \n",
+      "6   0.079865  0.078699  0.078063  0.076675  0.075332  0.074047  0.072826  \n",
+      "7   0.079371  0.078431  0.077474  0.076314  0.075177  0.074077  0.073022  \n",
+      "8   0.078726  0.077994  0.076758  0.075809  0.074861  0.073933  0.073033  \n",
+      "9   0.077994  0.077452  0.075974  0.075216  0.074443  0.073672  0.072915  \n",
+      "10  0.076758  0.075974  0.075342  0.074315  0.073305  0.072325  0.071384  \n",
+      "11  0.075809  0.075216  0.074315  0.073486  0.072655  0.071838  0.071044  \n",
+      "12  0.074861  0.074443  0.073305  0.072655  0.071987  0.071318  0.070658  \n",
+      "13  0.073933  0.073672  0.072325  0.071838  0.071318  0.070783  0.070245  \n",
+      "14  0.073033  0.072915  0.071384  0.071044  0.070658  0.070245  0.069818  \n"
+     ]
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -3471,10 +3883,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.random.rand(100)\n",
@@ -3496,10 +3905,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -3694,10 +4100,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from mpl_toolkits.mplot3d import Axes3D\n",
@@ -3815,10 +4218,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def FrankeFunction(x,y):\n",
@@ -3876,7 +4276,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }