diff --git a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png
index f923e900b..2b1379081 100644
Binary files a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png and b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png differ
diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index 1ea08fa22..9ad6b53e9 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -896,7 +896,16 @@
    "execution_count": 2,
    "id": "af203786",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.26308787  1.13549158 -0.38961136  1.14547226 -0.12244053 -0.90615052\n",
+      " -0.86514806 -0.28072716 -1.74251207  0.34150908]\n"
+     ]
+    }
+   ],
    "source": [
     "n = 10\n",
     "x = np.random.normal(size=n)\n",
@@ -917,7 +926,15 @@
    "execution_count": 3,
    "id": "afeb7fbd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.array([1, 2, 3])\n",
@@ -938,7 +955,15 @@
    "execution_count": 4,
    "id": "774b9f59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8]))\n",
@@ -964,7 +989,15 @@
    "execution_count": 5,
    "id": "e50d9d86",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 2]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from math import log\n",
@@ -988,7 +1021,15 @@
    "execution_count": 6,
    "id": "2939cbb6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8], dtype = np.float64))\n",
@@ -1008,7 +1049,15 @@
    "execution_count": 7,
    "id": "c5a3c7de",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
@@ -1028,7 +1077,15 @@
    "execution_count": 8,
    "id": "4257fe2f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
@@ -1052,7 +1109,17 @@
    "execution_count": 9,
    "id": "d65874c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.38629436 1.94591015 2.07944154]\n",
+      " [1.09861229 2.30258509 2.39789527]\n",
+      " [1.38629436 1.60943791 1.94591015]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1072,7 +1139,15 @@
    "execution_count": 10,
    "id": "e92e4468",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.09861229 1.38629436]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1093,7 +1168,15 @@
    "execution_count": 11,
    "id": "534fd820",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.09861229 2.30258509 2.39789527]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1114,7 +1197,24 @@
    "execution_count": 12,
    "id": "8ecff438",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1136,7 +1236,24 @@
    "execution_count": 13,
    "id": "a0725d66",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1158,7 +1275,34 @@
    "execution_count": 14,
    "id": "ffa25ac1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.11833235 0.97168087 0.51108098 0.55183233 0.25342454 0.06807213\n",
+      "  0.57524476 0.19248643 0.41798071 0.07388678]\n",
+      " [0.41086978 0.75202815 0.33196421 0.16708428 0.16608737 0.29586216\n",
+      "  0.09944873 0.05277619 0.47712039 0.51731953]\n",
+      " [0.71338175 0.41548522 0.65654583 0.64709545 0.35227217 0.88797877\n",
+      "  0.5737164  0.81603041 0.31729969 0.82999556]\n",
+      " [0.47037752 0.92176294 0.2823057  0.07829819 0.65495048 0.36686303\n",
+      "  0.27656187 0.60957578 0.69888079 0.668314  ]\n",
+      " [0.24924292 0.21878186 0.40947806 0.19907785 0.9566047  0.52848094\n",
+      "  0.25434325 0.59920581 0.39336845 0.23717584]\n",
+      " [0.59463514 0.15383958 0.65652548 0.45028046 0.16413801 0.88895409\n",
+      "  0.2719153  0.93751885 0.51389771 0.61062516]\n",
+      " [0.88609214 0.93589998 0.60777444 0.10998959 0.60805073 0.52485563\n",
+      "  0.57517889 0.89619395 0.65701361 0.83664686]\n",
+      " [0.93536673 0.668859   0.03717754 0.47723192 0.13950965 0.75354895\n",
+      "  0.66283949 0.10722441 0.0417946  0.24890667]\n",
+      " [0.25666182 0.88349025 0.73006765 0.30795969 0.86898117 0.55923736\n",
+      "  0.35818128 0.87468503 0.11364813 0.86332561]\n",
+      " [0.09205753 0.36857883 0.34677484 0.45563071 0.47895131 0.7425919\n",
+      "  0.6911375  0.49646786 0.65869839 0.29348232]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1247,7 +1391,21 @@
    "execution_count": 15,
    "id": "a9fc9d7e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.016702121542687232\n",
+      "3.9180630130818526\n",
+      "0.051686343335408516\n",
+      "[[ 1.0747946   3.28558848  3.20209898]\n",
+      " [ 3.28558848 10.9851108   9.5799141 ]\n",
+      " [ 3.20209898  9.5799141  15.11959546]]\n",
+      "[23.76779023  0.07048992  3.34122072]\n"
+     ]
+    }
+   ],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -1271,7 +1429,32 @@
    "execution_count": 16,
    "id": "9c6bf314",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 0. 0. 0.]\n",
+      " [0. 1. 0. 0.]\n",
+      " [0. 0. 1. 0.]\n",
+      " [0. 0. 0. 1.]]\n",
+      "  (0, 0)\t1.0\n",
+      "  (1, 1)\t1.0\n",
+      "  (2, 2)\t1.0\n",
+      "  (3, 3)\t1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -1316,7 +1499,79 @@
    "execution_count": 17,
    "id": "4cfb6b53",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>First Name</th>\n",
+       "      <th>Last Name</th>\n",
+       "      <th>Place of birth</th>\n",
+       "      <th>Date of Birth T.A.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Frodo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bilbo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aragorn II</td>\n",
+       "      <td>Elessar</td>\n",
+       "      <td>Eriador</td>\n",
+       "      <td>2931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Samwise</td>\n",
+       "      <td>Gamgee</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2980</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   First Name Last Name Place of birth  Date of Birth T.A.\n",
+       "0       Frodo   Baggins          Shire                2968\n",
+       "1       Bilbo   Baggins          Shire                2890\n",
+       "2  Aragorn II   Elessar        Eriador                2931\n",
+       "3     Samwise    Gamgee          Shire                2980"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "from IPython.display import display\n",
@@ -1345,7 +1600,79 @@
    "execution_count": 18,
    "id": "61a0f2fb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>First Name</th>\n",
+       "      <th>Last Name</th>\n",
+       "      <th>Place of birth</th>\n",
+       "      <th>Date of Birth T.A.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Frodo</th>\n",
+       "      <td>Frodo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilbo</th>\n",
+       "      <td>Bilbo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aragorn</th>\n",
+       "      <td>Aragorn II</td>\n",
+       "      <td>Elessar</td>\n",
+       "      <td>Eriador</td>\n",
+       "      <td>2931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sam</th>\n",
+       "      <td>Samwise</td>\n",
+       "      <td>Gamgee</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2980</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
+       "Frodo         Frodo   Baggins          Shire                2968\n",
+       "Bilbo         Bilbo   Baggins          Shire                2890\n",
+       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
+       "Sam         Samwise    Gamgee          Shire                2980"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
     "display(data_pandas)"
@@ -1364,7 +1691,21 @@
    "execution_count": 19,
    "id": "311a5e3e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "First Name            Aragorn II\n",
+       "Last Name                Elessar\n",
+       "Place of birth           Eriador\n",
+       "Date of Birth T.A.          2931\n",
+       "Name: Aragorn, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "display(data_pandas.loc['Aragorn'])"
    ]
@@ -1382,7 +1723,20 @@
    "execution_count": 20,
    "id": "9f4ea5fc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'append'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_27009/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m      7\u001b[0m               \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m               \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m               \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6200\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6201\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         ):\n\u001b[1;32m   6203\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
+     ]
+    }
+   ],
    "source": [
     "new_hobbit = {'First Name': [\"Peregrin\"],\n",
     "              'Last Name': [\"Took\"],\n",
@@ -1404,7 +1758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "24324844",
    "metadata": {},
    "outputs": [],
@@ -1434,7 +1788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "c47e2a3e",
    "metadata": {},
    "outputs": [],
@@ -1469,7 +1823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "40a9171b",
    "metadata": {},
    "outputs": [],
@@ -1561,7 +1915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "e4aeced9",
    "metadata": {},
    "outputs": [],
@@ -1698,7 +2052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "8b269629",
    "metadata": {},
    "outputs": [],
@@ -1742,7 +2096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "b9536a0a",
    "metadata": {},
    "outputs": [],
@@ -2106,7 +2460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 21,
    "id": "5b86a2f2",
    "metadata": {},
    "outputs": [],
@@ -2156,7 +2510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 22,
    "id": "ca868b5c",
    "metadata": {},
    "outputs": [
@@ -2166,7 +2520,7 @@
        "'\"\\nfrom pylab import plt, mpl\\nplt.style.use(\\'seaborn\\')\\nmpl.rcParams[\\'font.family\\'] = \\'serif\\'\\n\\ndef MakePlot(x,y, styles, labels, axlabels):\\n    plt.figure(figsize=(10,6))\\n    for i in range(len(x)):\\n        plt.plot(x[i], y[i], styles[i], label = labels[i])\\n        plt.xlabel(axlabels[0])\\n        plt.ylabel(axlabels[1])\\n    plt.legend(loc=0)\\n'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2203,7 +2557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 23,
    "id": "edef3728",
    "metadata": {},
    "outputs": [
@@ -2213,7 +2567,7 @@
        "'                                                                                                                         \\nThis is taken from the data file of the mass 2016 evaluation.                                                               \\nAll files are 3436 lines long with 124 character per line.                                                                  \\n       Headers are 39 lines long.                                                                                           \\n   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     \\n   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     \\n   These formats are reflected in the pandas widths variable below, see the statement                                       \\n   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            \\n   Pandas has also a variable header, with length 39 in this case.                                                          \\n'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2244,7 +2598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 24,
    "id": "0a2099c5",
    "metadata": {},
    "outputs": [],
@@ -2288,7 +2642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 25,
    "id": "c36ad284",
    "metadata": {},
    "outputs": [
@@ -2334,7 +2688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 36,
    "id": "1ad1fff9",
    "metadata": {},
    "outputs": [],
@@ -2358,7 +2712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 37,
    "id": "1ea1c99f",
    "metadata": {},
    "outputs": [],
@@ -2378,7 +2732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 38,
    "id": "4bb96bf8",
    "metadata": {},
    "outputs": [
@@ -2391,7 +2745,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2436,7 +2790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 39,
    "id": "c3072d47",
    "metadata": {},
    "outputs": [
@@ -2444,6 +2798,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
@@ -2464,7 +2850,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 2 Axes>"
       ]
@@ -2476,10 +2862,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[6.90732417e+00 1.97094475e+01 1.57990613e+01 6.05984191e-01]\n",
-      " [2.08450110e-01 1.86438494e-01 7.06379681e-02 2.67760777e-01]\n",
-      " [2.03157147e+01 3.53757381e-01 1.66436883e-01 1.76975172e-01]\n",
-      " [7.53767420e+01 3.13860106e+01 9.45323854e+01 1.01940852e+02]]\n"
+      "[[2.45926087e+02 9.09631786e+02 5.49989401e+02 6.44682919e+02\n",
+      "  1.00152026e+03 2.98229636e+02]\n",
+      " [1.60487177e+01 1.11327058e+01 1.89892178e+01 2.91355329e+01\n",
+      "  1.84191515e+03 1.91127240e+01]\n",
+      " [5.15218523e+00 1.33283018e+01 2.01038393e+01 1.32049628e+00\n",
+      "  4.70650883e+00 1.38520020e+01]\n",
+      " [2.05834021e-01 1.33912893e-01 2.11723352e-01 2.47339004e-01\n",
+      "  6.80883632e-02 3.49468006e-01]\n",
+      " [1.26217089e-01 1.70682500e-01 1.56794335e-01 9.39134042e-01\n",
+      "  1.64027461e+01 2.59164091e-01]\n",
+      " [1.66844981e+02 8.39153402e+01 1.70546784e+02 4.85399838e+00\n",
+      "  1.74079354e+01 1.25607419e+02]]\n"
      ]
     }
    ],
@@ -2494,8 +2888,8 @@
     "n_hidden_neurons = 50\n",
     "epochs = 100\n",
     "# store models for later use\n",
-    "eta_vals = np.logspace(-3, 0, 4)\n",
-    "lmbd_vals = np.logspace(-3, 0, 4)\n",
+    "eta_vals = np.logspace(-5, 0, 6)\n",
+    "lmbd_vals = np.logspace(-5, 0, 6)\n",
     "# store the models for later use\n",
     "DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
     "train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
@@ -2953,7 +3347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "379ac7d7",
    "metadata": {},
    "outputs": [],
@@ -3424,7 +3818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "ba423f48",
    "metadata": {},
    "outputs": [],
@@ -3445,7 +3839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "c23bf717",
    "metadata": {},
    "outputs": [],
@@ -3464,7 +3858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "f5828edf",
    "metadata": {},
    "outputs": [],
@@ -3496,7 +3890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "id": "83def2e2",
    "metadata": {},
    "outputs": [],
@@ -3515,7 +3909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "e8a11207",
    "metadata": {},
    "outputs": [],
@@ -3533,7 +3927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "fd4633e8",
    "metadata": {},
    "outputs": [],
@@ -3555,7 +3949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "88c19226",
    "metadata": {},
    "outputs": [],
@@ -3987,7 +4381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "id": "5fec1708",
    "metadata": {},
    "outputs": [],
@@ -4113,7 +4507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "id": "32b65d12",
    "metadata": {},
    "outputs": [],
@@ -4274,7 +4668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "id": "74dd9421",
    "metadata": {},
    "outputs": [],
@@ -4370,7 +4764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "id": "52031830",
    "metadata": {},
    "outputs": [],