PerformanceAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performances between Groups\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import math\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      Mouse ID         Genotype Gender                     Line TgSWDI  \\\n0          170          Tg-SwDI      M                  Tg-SwDI    Pos   \n1          171          Tg-SwDI      M                  Tg-SwDI    Pos   \n2          224          Tg-SwDI      M          Tg-SwDI x ThCre    Pos   \n3          225          Tg-SwDI      M          Tg-SwDI x ThCre    Pos   \n4          233          Tg-SwDI      M          Tg-SwDI x ThCre    Pos   \n5          172          Tg-SwDI      F                      NaN    Pos   \n6          173          Tg-SwDI      F                  Tg-SwDI    Pos   \n7          174          Tg-SwDI      F                  Tg-SwDI    Pos   \n8          175          Tg-SwDI      F                  Tg-SwDI    Pos   \n9          231          Tg-SwDI      F                  Tg-SwDI    Pos   \n10         254          Tg-SwDI      M          Tg-SwDI x ThCre    Pos   \n11         255          Tg-SwDI      M          Tg-SwDI x ThCre    Pos   \n12         260          Tg-SwDI      M          Tg-SwDI x ThCre    Pos   \n13         261          Tg-SwDI      M          Tg-SwDI x ThCre    Pos   \n14         282          Tg-SwDI      F                  Tg-SwDI    Pos   \n15         284          Tg-SwDI      F                  Tg-SwDI    Pos   \n16         285          Tg-SwDI      F                  Tg-SwDI    Pos   \n17         286          Tg-SwDI      F                  Tg-SwDI    Pos   \n18         177  Tg-SwDI x ThCre      M  Tg-SwDI x (ThCre x ChR)    Pos   \n19         178  Tg-SwDI x ThCre      M  Tg-SwDI x (ThCre x ChR)    Pos   \n20         221  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n21         234  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n22         181  Tg-SwDI x ThCre      F  Tg-SwDI x (ThCre x ChR)    Pos   \n23         182  Tg-SwDI x ThCre      F  Tg-SwDI x (ThCre x ChR)    Pos   \n24         228  Tg-SwDI x ThCre      F          Tg-SwDI x ThCre    Pos   \n25         230  Tg-SwDI x ThCre      F          Tg-SwDI x ThCre    Pos   \n26         232  Tg-SwDI x ThCre      F          Tg-SwDI x ThCre    Pos   \n27         253  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n28         256  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n29         259  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n30         263  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n31         264  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n32         184  Tg-SwDI x ThCre      F  Tg-SwDI x (ThCre x ChR)    Pos   \n33         236  Tg-SwDI x ThCre      F  Tg-SwDI x (ThCre x ChR)    Pos   \n34         238  Tg-SwDI x ThCre      F  Tg-SwDI x (ThCre x ChR)    Pos   \n35         276  Tg-SwDI x ThCre      F          Tg-SwDI x ThCre    Pos   \n36         265  Tg-SwDI x ThCre      F          Tg-SwDI x ThCre    Pos   \n37         270  Tg-SwDI x ThCre      F          Tg-SwDI x ThCre    Pos   \n38         272  Tg-SwDI x ThCre      F          Tg-SwDI x ThCre    Pos   \n39         190            ThCre      M                    ThCre    Pos   \n40         193            ThCre      M                    ThCre    Pos   \n41         194            ThCre      M                    ThCre    Pos   \n42         196            ThCre      M                    ThCre    Pos   \n43         207            ThCre      M                    ThCre    Pos   \n44         202            ThCre      M                    ThCre    Pos   \n45         203            ThCre      M                    ThCre    Pos   \n46          71            ThCre      F          Th-Cre x DREADD    Pos   \n47          99            ThCre      F           Th-Cre x Gcamp    Pos   \n48         131            ThCre      F       Th-Cre x C57BL/6NJ    Pos   \n49         210            ThCre      F                    ThCre    Pos   \n50         211            ThCre      F                    ThCre    Pos   \n51         NaN              NaN    NaN                      NaN    NaN   \n52         NaN              NaN    NaN                      NaN    NaN   \n53  Disceased:              NaN    NaN                      NaN    NaN   \n54         116              NaN      M                  Tg-SwDI    POS   \n56         222  Tg-SwDI x ThCre      M          Tg-SwDI x ThCre    Pos   \n\n   Genotype 2 Genotype 3         Father              Mother         DOB  \\\n0         NaN        NaN  Tg-SwDI (116)       Tg-SwDI (117)    8/2/2019   \n1         NaN        NaN  Tg-SwDI (116)       Tg-SwDI (117)    8/2/2019   \n2         Neg        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n3         Neg        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n4         Neg        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n5         NaN        NaN  Tg-SwDI (116)       Tg-SwDI (117)    8/2/2019   \n6         NaN        NaN  Tg-SwDI (116)       Tg-SwDI (117)    8/2/2019   \n7         NaN        NaN  Tg-SwDI (116)       Tg-SwDI (117)    8/2/2019   \n8         NaN        NaN  Tg-SwDI (116)       Tg-SwDI (117)    8/2/2019   \n9         NaN        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n10        Neg        NaN   Tg-SwDI(116)        ThCre(71/99)   10/2/2019   \n11        Neg        NaN   Tg-SwDI(116)        ThCre(71/99)   10/2/2019   \n12        Neg        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n13        Neg        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n14        NaN        NaN   Tg-SwDI(116)                 117  10/29/2019   \n15        NaN        NaN   Tg-SwDI(116)                 117   9/15/2019   \n16        NaN        NaN   Tg-SwDI(116)                 117   9/15/2019   \n17        NaN        NaN   Tg-SwDI(116)                 117   9/15/2019   \n18        Pos        Neg  Tg-SwDI (116)    ThCre x ChR (89)    7/7/2019   \n19        Pos        Neg  Tg-SwDI (116)    ThCre x ChR (89)    7/7/2019   \n20        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n21        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n22        Pos        Neg  Tg-SwDI (116)    ThCre x ChR (89)    7/7/2019   \n23        Pos        Neg  Tg-SwDI (116)    ThCre x ChR (89)    7/7/2019   \n24        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n25        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n26        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n27        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   10/2/2019   \n28        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   10/2/2019   \n29        Pos        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n30        Pos        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n31        Pos        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n32        Pos        Pos  Tg-SwDI (116)    ThCre x ChR (89)    7/7/2019   \n33        Pos        Neg   Tg-SwDI(116)  Th-Cre x ChR2 (88)   8/23/2019   \n34        Pos        Neg   Tg-SwDI(116)  Th-Cre x ChR2 (88)   8/23/2019   \n35        Pos        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n36        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   10/2/2019   \n37        Pos        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n38        Pos        NaN   Tg-SwDI(116)      ThCre(210/211)  10/21/2019   \n39        NaN        NaN     ThCre (58)      C57 (56 or 65)   7/18/2019   \n40        NaN        NaN     ThCre (58)      C57 (56 or 65)   7/18/2019   \n41        NaN        NaN     ThCre (58)      C57 (56 or 65)   7/18/2019   \n42        NaN        NaN     ThCre (58)      C57 (56 or 65)   7/18/2019   \n43        NaN        NaN      ThCre (3)      C57 (43 or 44)   7/23/2019   \n44        NaN        NaN      ThCre (3)      C57 (43 or 44)   7/23/2019   \n45        NaN        NaN      ThCre (3)      C57 (43 or 44)   7/23/2019   \n46        Neg        NaN      Th-Cre(1)           DREAD(13)   3/20/2019   \n47        Neg        NaN            NaN                 NaN    5/1/2019   \n48        NaN        NaN            NaN                 NaN   7/15/2019   \n49        NaN        NaN     ThCre (58)      C57 (56 or 65)   7/18/2019   \n50        NaN        NaN     ThCre (58)      C57 (56 or 65)   7/18/2019   \n51        NaN        NaN            NaN                 NaN         NaN   \n52        NaN        NaN            NaN                 NaN         NaN   \n53        NaN        NaN            NaN                 NaN         NaN   \n54        NaN        NaN            Jax                 Jax         NaN   \n56        Pos        NaN   Tg-SwDI(116)        ThCre(71/99)   8/24/2019   \n\n   Maze Configuration Treatment Group Experimental Status  \n0                ABFG              --             Testing  \n1                ABFG              --             Testing  \n2                ABFG              --             Testing  \n3                ABFG              --             Testing  \n4                ABFG              --             Testing  \n5                BCEG              --             Testing  \n6                BCEG              --             Testing  \n7                BCEG              --             Testing  \n8                BCEG              --             Testing  \n9                BCEG              --             Testing  \n10               BDFH            MAMP             Testing  \n11               BDFH            MAMP             Testing  \n12               BDFH            MAMP             Testing  \n13               BDFH            MAMP             Testing  \n14               ACDF            MAMP             Testing  \n15               ACDF            MAMP             Testing  \n16               ACDF            MAMP             Testing  \n17               ACDF            MAMP             Testing  \n18               ACDF   High Dose CNO             Testing  \n19               ACDF   High Dose CNO             Testing  \n20               ACDF   High Dose CNO                 NaN  \n21               ACDF   High Dose CNO                 NaN  \n22               BDFH   High Dose CNO             Testing  \n23               BDFH   High Dose CNO             Testing  \n24               BDFH   High Dose CNO             Testing  \n25               BDFH   High Dose CNO             Testing  \n26               BDFH   High Dose CNO             Testing  \n27               ABFG    Low Dose CNO                 NaN  \n28               ABFG    Low Dose CNO                 NaN  \n29               ABFG    Low Dose CNO             Testing  \n30               ABFG    Low Dose CNO                 NaN  \n31               ABFG    Low Dose CNO             Testing  \n32               BDFH    Low Dose CNO             Testing  \n33               BDFH    Low Dose CNO             Testing  \n34               BDFH    Low Dose CNO                 NaN  \n35               BDFH    Low Dose CNO                 NaN  \n36               BDFH    Low Dose CNO                 NaN  \n37               BDFH    Low Dose CNO                 NaN  \n38               BDFH    Low Dose CNO                 NaN  \n39               BCEG              --             Testing  \n40               BCEG              --             Testing  \n41               BCEG              --             Testing  \n42               BCEG              --             Testing  \n43               BCEG              --             Testing  \n44               ABFG              --             Testing  \n45               ABFG              --             Testing  \n46               ABFG              --             Testing  \n47               ABFG              --             Testing  \n48               ABFG              --             Testing  \n49               ABFG              --             Testing  \n50               ABFG              --             Testing  \n51                NaN             NaN                 NaN  \n52                NaN             NaN                 NaN  \n53                NaN             NaN                 NaN  \n54               ABFG             NaN                 NaN  \n56               ACDF   High Dose CNO                 NaN  ",
      "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>Mouse ID</th>\n      <th>Genotype</th>\n      <th>Gender</th>\n      <th>Line</th>\n      <th>TgSWDI</th>\n      <th>Genotype 2</th>\n      <th>Genotype 3</th>\n      <th>Father</th>\n      <th>Mother</th>\n      <th>DOB</th>\n      <th>Maze Configuration</th>\n      <th>Treatment Group</th>\n      <th>Experimental Status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>170</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>171</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>224</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>225</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>233</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>172</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>NaN</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>173</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>174</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>175</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>231</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>254</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>10/2/2019</td>\n      <td>BDFH</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>255</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>10/2/2019</td>\n      <td>BDFH</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>260</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>BDFH</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>261</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>BDFH</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>282</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>117</td>\n      <td>10/29/2019</td>\n      <td>ACDF</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>284</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>117</td>\n      <td>9/15/2019</td>\n      <td>ACDF</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>285</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>117</td>\n      <td>9/15/2019</td>\n      <td>ACDF</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>286</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>117</td>\n      <td>9/15/2019</td>\n      <td>ACDF</td>\n      <td>MAMP</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>177</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x (ThCre x ChR)</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>Tg-SwDI (116)</td>\n      <td>ThCre x ChR (89)</td>\n      <td>7/7/2019</td>\n      <td>ACDF</td>\n      <td>High Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>178</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x (ThCre x ChR)</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>Tg-SwDI (116)</td>\n      <td>ThCre x ChR (89)</td>\n      <td>7/7/2019</td>\n      <td>ACDF</td>\n      <td>High Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>221</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ACDF</td>\n      <td>High Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>234</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ACDF</td>\n      <td>High Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>181</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x (ThCre x ChR)</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>Tg-SwDI (116)</td>\n      <td>ThCre x ChR (89)</td>\n      <td>7/7/2019</td>\n      <td>BDFH</td>\n      <td>High Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>182</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x (ThCre x ChR)</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>Tg-SwDI (116)</td>\n      <td>ThCre x ChR (89)</td>\n      <td>7/7/2019</td>\n      <td>BDFH</td>\n      <td>High Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>228</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>BDFH</td>\n      <td>High Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>230</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>BDFH</td>\n      <td>High Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>232</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>BDFH</td>\n      <td>High Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>253</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>10/2/2019</td>\n      <td>ABFG</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>256</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>10/2/2019</td>\n      <td>ABFG</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>259</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>ABFG</td>\n      <td>Low Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>263</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>ABFG</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>264</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>ABFG</td>\n      <td>Low Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>184</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x (ThCre x ChR)</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Tg-SwDI (116)</td>\n      <td>ThCre x ChR (89)</td>\n      <td>7/7/2019</td>\n      <td>BDFH</td>\n      <td>Low Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>236</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x (ThCre x ChR)</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>Tg-SwDI(116)</td>\n      <td>Th-Cre x ChR2 (88)</td>\n      <td>8/23/2019</td>\n      <td>BDFH</td>\n      <td>Low Dose CNO</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>238</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x (ThCre x ChR)</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>Tg-SwDI(116)</td>\n      <td>Th-Cre x ChR2 (88)</td>\n      <td>8/23/2019</td>\n      <td>BDFH</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>276</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>BDFH</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>265</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>10/2/2019</td>\n      <td>BDFH</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>270</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>BDFH</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>272</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>F</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(210/211)</td>\n      <td>10/21/2019</td>\n      <td>BDFH</td>\n      <td>Low Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>190</td>\n      <td>ThCre</td>\n      <td>M</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (58)</td>\n      <td>C57 (56 or 65)</td>\n      <td>7/18/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>193</td>\n      <td>ThCre</td>\n      <td>M</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (58)</td>\n      <td>C57 (56 or 65)</td>\n      <td>7/18/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>194</td>\n      <td>ThCre</td>\n      <td>M</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (58)</td>\n      <td>C57 (56 or 65)</td>\n      <td>7/18/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>196</td>\n      <td>ThCre</td>\n      <td>M</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (58)</td>\n      <td>C57 (56 or 65)</td>\n      <td>7/18/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>207</td>\n      <td>ThCre</td>\n      <td>M</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (3)</td>\n      <td>C57 (43 or 44)</td>\n      <td>7/23/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>202</td>\n      <td>ThCre</td>\n      <td>M</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (3)</td>\n      <td>C57 (43 or 44)</td>\n      <td>7/23/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>203</td>\n      <td>ThCre</td>\n      <td>M</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (3)</td>\n      <td>C57 (43 or 44)</td>\n      <td>7/23/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>71</td>\n      <td>ThCre</td>\n      <td>F</td>\n      <td>Th-Cre x DREADD</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Th-Cre(1)</td>\n      <td>DREAD(13)</td>\n      <td>3/20/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>99</td>\n      <td>ThCre</td>\n      <td>F</td>\n      <td>Th-Cre x Gcamp</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5/1/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>131</td>\n      <td>ThCre</td>\n      <td>F</td>\n      <td>Th-Cre x C57BL/6NJ</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7/15/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>210</td>\n      <td>ThCre</td>\n      <td>F</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (58)</td>\n      <td>C57 (56 or 65)</td>\n      <td>7/18/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>211</td>\n      <td>ThCre</td>\n      <td>F</td>\n      <td>ThCre</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>ThCre (58)</td>\n      <td>C57 (56 or 65)</td>\n      <td>7/18/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>Disceased:</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>116</td>\n      <td>NaN</td>\n      <td>M</td>\n      <td>Tg-SwDI</td>\n      <td>POS</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Jax</td>\n      <td>Jax</td>\n      <td>NaN</td>\n      <td>ABFG</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>222</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ACDF</td>\n      <td>High Dose CNO</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# load primary data\n",
    "primaryData = pd.read_csv(r'./data/table1.csv')\n",
    "primaryData = primaryData[primaryData['Experimental Status']!='Disceased']\n",
    "primaryData.drop(['Age (wks)','Birth status', 'Estimated Delivery Date', 'Delivery Date', 'Set up', 'Ween', 'Male Status', 'Breeding Status',\n",
    "              'Virus Implantation'],axis=1,inplace=True)\n",
    "\n",
    "primaryData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     Mouse ID        Date  Weight  Baited Arms Retrieved  \\\n442       285  2020-01-01      25                      0   \n443        71  2020-01-01      25                      1   \n444       260  2020-01-01      28                      1   \n445       261  2020-01-01      28                      0   \n446       284  2020-01-01      22                      0   \n..        ...         ...     ...                    ...   \n944       207  2020-02-29      30                      4   \n945       202  2020-02-29      30                      4   \n946       178  2020-02-29      28                      4   \n947       211  2020-02-29      24                      4   \n948       181  2020-02-29      24                      4   \n\n     Reference Memory Errors  Working Memory Errors Other Notes  \\\n442                       16                     23         NaN   \n443                       29                     53         NaN   \n444                       23                     27         NaN   \n445                       26                     46         NaN   \n446                       14                     24         NaN   \n..                       ...                    ...         ...   \n944                        3                      3         NaN   \n945                        9                     10         NaN   \n946                        5                      4         NaN   \n947                        6                      2        GABF   \n948                        4                      3         NaN   \n\n              Start Time             End Time Video Length  ...      Distance  \\\n442  2020-01-01 12:31:07  2020-01-01 12:41:07      0:10:00  ...  21840.552995   \n443  2020-01-01 15:36:00  2020-01-01 15:46:00      0:10:00  ...  31366.376863   \n444  2020-01-01 13:18:12  2020-01-01 13:28:02      0:09:50  ...  16997.318591   \n445  2020-01-01 13:40:21  2020-01-01 13:49:55      0:09:34  ...  27863.110858   \n446  2020-01-01 12:08:45  2020-01-01 12:18:45      0:10:00  ...  20624.253365   \n..                   ...                  ...          ...  ...           ...   \n944  2020-02-29 15:54:14  2020-02-29 15:55:40      0:01:26  ...   4644.497932   \n945  2020-02-29 15:21:45  2020-02-29 15:28:48      0:07:03  ...  11987.690376   \n946  2020-02-29 17:49:36  2020-02-29 17:53:42      0:04:06  ...   6935.927508   \n947  2020-02-29 14:33:15  2020-02-29 14:34:35      0:01:20  ...   4191.584958   \n948  2020-02-29 19:18:11  2020-02-29 19:22:46      0:04:35  ...   6303.803784   \n\n       Center      ArmA      ArmB      ArmC      ArmD      ArmE      ArmF  \\\n442  0.111185  0.080736  0.050119  0.040173  0.234928  0.191365  0.187087   \n443  0.180530  0.094627  0.126299  0.101295  0.118186  0.095016  0.057065   \n444  0.117993  0.168230  0.081657  0.080018  0.156476  0.221349  0.049051   \n445  0.216202  0.078223  0.096806  0.122706  0.142509  0.096632  0.101278   \n446  0.129188  0.178530  0.110630  0.085514  0.263933  0.028394  0.084125   \n..        ...       ...       ...       ...       ...       ...       ...   \n944  0.076744  0.087984  0.191085  0.148837  0.059302  0.188372  0.161628   \n945  0.063617  0.146051  0.168727  0.068420  0.233367  0.138572  0.025667   \n946  0.041384  0.177748  0.039213  0.014111  0.068657  0.299322  0.182632   \n947  0.086164  0.223695  0.203397  0.044739  0.099420  0.050539  0.058824   \n948  0.028364  0.157576  0.054788  0.024242  0.071879  0.355515  0.011636   \n\n         ArmG      ArmH  \n442  0.046674  0.058621  \n443  0.150414  0.079069  \n444  0.050746  0.077588  \n445  0.076829  0.071661  \n446  0.051120  0.069512  \n..        ...       ...  \n944  0.086822  0.000000  \n945  0.075663  0.080545  \n946  0.066757  0.111398  \n947  0.192626  0.041839  \n948  0.000000  0.296606  \n\n[507 rows x 29 columns]",
      "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>Mouse ID</th>\n      <th>Date</th>\n      <th>Weight</th>\n      <th>Baited Arms Retrieved</th>\n      <th>Reference Memory Errors</th>\n      <th>Working Memory Errors</th>\n      <th>Other Notes</th>\n      <th>Start Time</th>\n      <th>End Time</th>\n      <th>Video Length</th>\n      <th>...</th>\n      <th>Distance</th>\n      <th>Center</th>\n      <th>ArmA</th>\n      <th>ArmB</th>\n      <th>ArmC</th>\n      <th>ArmD</th>\n      <th>ArmE</th>\n      <th>ArmF</th>\n      <th>ArmG</th>\n      <th>ArmH</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>442</th>\n      <td>285</td>\n      <td>2020-01-01</td>\n      <td>25</td>\n      <td>0</td>\n      <td>16</td>\n      <td>23</td>\n      <td>NaN</td>\n      <td>2020-01-01 12:31:07</td>\n      <td>2020-01-01 12:41:07</td>\n      <td>0:10:00</td>\n      <td>...</td>\n      <td>21840.552995</td>\n      <td>0.111185</td>\n      <td>0.080736</td>\n      <td>0.050119</td>\n      <td>0.040173</td>\n      <td>0.234928</td>\n      <td>0.191365</td>\n      <td>0.187087</td>\n      <td>0.046674</td>\n      <td>0.058621</td>\n    </tr>\n    <tr>\n      <th>443</th>\n      <td>71</td>\n      <td>2020-01-01</td>\n      <td>25</td>\n      <td>1</td>\n      <td>29</td>\n      <td>53</td>\n      <td>NaN</td>\n      <td>2020-01-01 15:36:00</td>\n      <td>2020-01-01 15:46:00</td>\n      <td>0:10:00</td>\n      <td>...</td>\n      <td>31366.376863</td>\n      <td>0.180530</td>\n      <td>0.094627</td>\n      <td>0.126299</td>\n      <td>0.101295</td>\n      <td>0.118186</td>\n      <td>0.095016</td>\n      <td>0.057065</td>\n      <td>0.150414</td>\n      <td>0.079069</td>\n    </tr>\n    <tr>\n      <th>444</th>\n      <td>260</td>\n      <td>2020-01-01</td>\n      <td>28</td>\n      <td>1</td>\n      <td>23</td>\n      <td>27</td>\n      <td>NaN</td>\n      <td>2020-01-01 13:18:12</td>\n      <td>2020-01-01 13:28:02</td>\n      <td>0:09:50</td>\n      <td>...</td>\n      <td>16997.318591</td>\n      <td>0.117993</td>\n      <td>0.168230</td>\n      <td>0.081657</td>\n      <td>0.080018</td>\n      <td>0.156476</td>\n      <td>0.221349</td>\n      <td>0.049051</td>\n      <td>0.050746</td>\n      <td>0.077588</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>261</td>\n      <td>2020-01-01</td>\n      <td>28</td>\n      <td>0</td>\n      <td>26</td>\n      <td>46</td>\n      <td>NaN</td>\n      <td>2020-01-01 13:40:21</td>\n      <td>2020-01-01 13:49:55</td>\n      <td>0:09:34</td>\n      <td>...</td>\n      <td>27863.110858</td>\n      <td>0.216202</td>\n      <td>0.078223</td>\n      <td>0.096806</td>\n      <td>0.122706</td>\n      <td>0.142509</td>\n      <td>0.096632</td>\n      <td>0.101278</td>\n      <td>0.076829</td>\n      <td>0.071661</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>284</td>\n      <td>2020-01-01</td>\n      <td>22</td>\n      <td>0</td>\n      <td>14</td>\n      <td>24</td>\n      <td>NaN</td>\n      <td>2020-01-01 12:08:45</td>\n      <td>2020-01-01 12:18:45</td>\n      <td>0:10:00</td>\n      <td>...</td>\n      <td>20624.253365</td>\n      <td>0.129188</td>\n      <td>0.178530</td>\n      <td>0.110630</td>\n      <td>0.085514</td>\n      <td>0.263933</td>\n      <td>0.028394</td>\n      <td>0.084125</td>\n      <td>0.051120</td>\n      <td>0.069512</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>944</th>\n      <td>207</td>\n      <td>2020-02-29</td>\n      <td>30</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>2020-02-29 15:54:14</td>\n      <td>2020-02-29 15:55:40</td>\n      <td>0:01:26</td>\n      <td>...</td>\n      <td>4644.497932</td>\n      <td>0.076744</td>\n      <td>0.087984</td>\n      <td>0.191085</td>\n      <td>0.148837</td>\n      <td>0.059302</td>\n      <td>0.188372</td>\n      <td>0.161628</td>\n      <td>0.086822</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>945</th>\n      <td>202</td>\n      <td>2020-02-29</td>\n      <td>30</td>\n      <td>4</td>\n      <td>9</td>\n      <td>10</td>\n      <td>NaN</td>\n      <td>2020-02-29 15:21:45</td>\n      <td>2020-02-29 15:28:48</td>\n      <td>0:07:03</td>\n      <td>...</td>\n      <td>11987.690376</td>\n      <td>0.063617</td>\n      <td>0.146051</td>\n      <td>0.168727</td>\n      <td>0.068420</td>\n      <td>0.233367</td>\n      <td>0.138572</td>\n      <td>0.025667</td>\n      <td>0.075663</td>\n      <td>0.080545</td>\n    </tr>\n    <tr>\n      <th>946</th>\n      <td>178</td>\n      <td>2020-02-29</td>\n      <td>28</td>\n      <td>4</td>\n      <td>5</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>2020-02-29 17:49:36</td>\n      <td>2020-02-29 17:53:42</td>\n      <td>0:04:06</td>\n      <td>...</td>\n      <td>6935.927508</td>\n      <td>0.041384</td>\n      <td>0.177748</td>\n      <td>0.039213</td>\n      <td>0.014111</td>\n      <td>0.068657</td>\n      <td>0.299322</td>\n      <td>0.182632</td>\n      <td>0.066757</td>\n      <td>0.111398</td>\n    </tr>\n    <tr>\n      <th>947</th>\n      <td>211</td>\n      <td>2020-02-29</td>\n      <td>24</td>\n      <td>4</td>\n      <td>6</td>\n      <td>2</td>\n      <td>GABF</td>\n      <td>2020-02-29 14:33:15</td>\n      <td>2020-02-29 14:34:35</td>\n      <td>0:01:20</td>\n      <td>...</td>\n      <td>4191.584958</td>\n      <td>0.086164</td>\n      <td>0.223695</td>\n      <td>0.203397</td>\n      <td>0.044739</td>\n      <td>0.099420</td>\n      <td>0.050539</td>\n      <td>0.058824</td>\n      <td>0.192626</td>\n      <td>0.041839</td>\n    </tr>\n    <tr>\n      <th>948</th>\n      <td>181</td>\n      <td>2020-02-29</td>\n      <td>24</td>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>2020-02-29 19:18:11</td>\n      <td>2020-02-29 19:22:46</td>\n      <td>0:04:35</td>\n      <td>...</td>\n      <td>6303.803784</td>\n      <td>0.028364</td>\n      <td>0.157576</td>\n      <td>0.054788</td>\n      <td>0.024242</td>\n      <td>0.071879</td>\n      <td>0.355515</td>\n      <td>0.011636</td>\n      <td>0.000000</td>\n      <td>0.296606</td>\n    </tr>\n  </tbody>\n</table>\n<p>507 rows × 29 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# load experimental data\n",
    "experimentalData = pd.read_csv(r'./data/table2.csv')\n",
    "experimentalData = experimentalData[experimentalData['Date']>='2020-01-01']\n",
    "experimentalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all mice under test\n",
    "primaryData = primaryData[primaryData['Experimental Status']=='Testing']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['170', '171', '224', '225', '233', '172', '173', '174', '175', '231', '254', '255', '260', '261', '282', '284', '285', '286', '177', '178', '181', '182', '228', '230', '232', '259', '264', '184', '236', '190', '193', '194', '196', '207', '202', '203', '71', '99', '131', '210', '211']\nTotal number of testing mice: 41\n"
    }
   ],
   "source": [
    "# number of valid mice (testing)\n",
    "allMice = list(pd.unique(primaryData['Mouse ID']))\n",
    "print(allMice)\n",
    "print(\"Total number of testing mice:\", len(allMice))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genotype Groups:\n",
    "\n",
    "1. Tg-SwDI + (--)\n",
    "\n",
    "2. Tg-SwDI + (MAMP)\n",
    "\n",
    "3. Th-Cre + (--)\n",
    "\n",
    "4. Tg-SwDI+ x Th-Cre + (low dose)\n",
    "\n",
    "5. Tg-SwDI+ x Th-Cre + (high dose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Group:\n",
    "\n",
    "    # From Group object we can easily retrieve mice data in one group\n",
    "    \n",
    "    def __init__(self,primaryData,experimentalData,genotype,treatment):\n",
    "\n",
    "        # initialize parameter: \n",
    "        #     primaryData: total primary mice data\n",
    "        #     experimentalData: total experimental data\n",
    "        #     genotype: group genotype\n",
    "        #     treatment: group treatment\n",
    "        # properties:\n",
    "        #     name, genotype, treatment, members, size, groupPrimaryData, groupExperimentalData\n",
    "        # methods: \n",
    "        #     group.info(self): display group infomation\n",
    "        \n",
    "        groupPrimaryData = primaryData[primaryData['Genotype']==genotype]\n",
    "        groupPrimaryData = groupPrimaryData[groupPrimaryData['Treatment Group']==treatment]\n",
    "        groupPrimaryData.reset_index(drop=True,inplace=True)\n",
    "        \n",
    "        self.name = genotype + '(' + treatment + ')'\n",
    "        self.groupPrimaryData = groupPrimaryData\n",
    "        self.genotype = genotype\n",
    "        self.treatment = treatment\n",
    "        self.members = list(groupPrimaryData['Mouse ID'])\n",
    "        self.size = len(list(groupPrimaryData['Mouse ID']))\n",
    "        \n",
    "        def in_group(x):\n",
    "            return str(x) in self.members\n",
    "        groupExperimentalData = experimentalData[experimentalData['Mouse ID'].apply(in_group)]\n",
    "        groupExperimentalData.reset_index(drop=True,inplace=True)\n",
    "        self.groupExperimentalData = groupExperimentalData\n",
    "\n",
    "        \n",
    "    def info(self):\n",
    "        print('---------------------------------')\n",
    "        print('Genotype:',self.genotype,' | Treatment:',self.treatment)\n",
    "        print('Members:',self.members)\n",
    "        print('Size:',self.size)\n",
    "        return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "---------------------------------\nGenotype: Tg-SwDI  | Treatment: --\nMembers: ['170', '171', '224', '225', '233', '172', '173', '174', '175', '231']\nSize: 10\n---------------------------------\nGenotype: Tg-SwDI  | Treatment: MAMP\nMembers: ['254', '255', '260', '261', '282', '284', '285', '286']\nSize: 8\n---------------------------------\nGenotype: ThCre  | Treatment: --\nMembers: ['190', '193', '194', '196', '207', '202', '203', '71', '99', '131', '210', '211']\nSize: 12\n---------------------------------\nGenotype: Tg-SwDI x ThCre  | Treatment: Low Dose CNO\nMembers: ['259', '264', '184', '236']\nSize: 4\n---------------------------------\nGenotype: Tg-SwDI x ThCre  | Treatment: High Dose CNO\nMembers: ['177', '178', '181', '182', '228', '230', '232']\nSize: 7\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  Mouse ID Genotype Gender             Line TgSWDI Genotype 2 Genotype 3  \\\n0      170  Tg-SwDI      M          Tg-SwDI    Pos        NaN        NaN   \n1      171  Tg-SwDI      M          Tg-SwDI    Pos        NaN        NaN   \n2      224  Tg-SwDI      M  Tg-SwDI x ThCre    Pos        Neg        NaN   \n3      225  Tg-SwDI      M  Tg-SwDI x ThCre    Pos        Neg        NaN   \n4      233  Tg-SwDI      M  Tg-SwDI x ThCre    Pos        Neg        NaN   \n5      172  Tg-SwDI      F              NaN    Pos        NaN        NaN   \n6      173  Tg-SwDI      F          Tg-SwDI    Pos        NaN        NaN   \n7      174  Tg-SwDI      F          Tg-SwDI    Pos        NaN        NaN   \n8      175  Tg-SwDI      F          Tg-SwDI    Pos        NaN        NaN   \n9      231  Tg-SwDI      F          Tg-SwDI    Pos        NaN        NaN   \n\n          Father         Mother        DOB Maze Configuration Treatment Group  \\\n0  Tg-SwDI (116)  Tg-SwDI (117)   8/2/2019               ABFG              --   \n1  Tg-SwDI (116)  Tg-SwDI (117)   8/2/2019               ABFG              --   \n2   Tg-SwDI(116)   ThCre(71/99)  8/24/2019               ABFG              --   \n3   Tg-SwDI(116)   ThCre(71/99)  8/24/2019               ABFG              --   \n4   Tg-SwDI(116)   ThCre(71/99)  8/24/2019               ABFG              --   \n5  Tg-SwDI (116)  Tg-SwDI (117)   8/2/2019               BCEG              --   \n6  Tg-SwDI (116)  Tg-SwDI (117)   8/2/2019               BCEG              --   \n7  Tg-SwDI (116)  Tg-SwDI (117)   8/2/2019               BCEG              --   \n8  Tg-SwDI (116)  Tg-SwDI (117)   8/2/2019               BCEG              --   \n9   Tg-SwDI(116)   ThCre(71/99)  8/24/2019               BCEG              --   \n\n  Experimental Status  \n0             Testing  \n1             Testing  \n2             Testing  \n3             Testing  \n4             Testing  \n5             Testing  \n6             Testing  \n7             Testing  \n8             Testing  \n9             Testing  ",
      "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>Mouse ID</th>\n      <th>Genotype</th>\n      <th>Gender</th>\n      <th>Line</th>\n      <th>TgSWDI</th>\n      <th>Genotype 2</th>\n      <th>Genotype 3</th>\n      <th>Father</th>\n      <th>Mother</th>\n      <th>DOB</th>\n      <th>Maze Configuration</th>\n      <th>Treatment Group</th>\n      <th>Experimental Status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>170</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>171</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>224</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>225</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>233</td>\n      <td>Tg-SwDI</td>\n      <td>M</td>\n      <td>Tg-SwDI x ThCre</td>\n      <td>Pos</td>\n      <td>Neg</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>ABFG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>172</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>NaN</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>173</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>174</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>175</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI (116)</td>\n      <td>Tg-SwDI (117)</td>\n      <td>8/2/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>231</td>\n      <td>Tg-SwDI</td>\n      <td>F</td>\n      <td>Tg-SwDI</td>\n      <td>Pos</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Tg-SwDI(116)</td>\n      <td>ThCre(71/99)</td>\n      <td>8/24/2019</td>\n      <td>BCEG</td>\n      <td>--</td>\n      <td>Testing</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# Create instances for Group object\n",
    "group1 = Group(primaryData,experimentalData,'Tg-SwDI','--')\n",
    "group2 = Group(primaryData,experimentalData,'Tg-SwDI','MAMP')\n",
    "group3 = Group(primaryData,experimentalData,'ThCre','--')\n",
    "group4 = Group(primaryData,experimentalData,'Tg-SwDI x ThCre','Low Dose CNO')\n",
    "group5 = Group(primaryData,experimentalData,'Tg-SwDI x ThCre','High Dose CNO')\n",
    "\n",
    "# Do grouping and display group info\n",
    "grouping = [group1,group2,group3,group4,group5]\n",
    "group1.info()\n",
    "group2.info()\n",
    "group3.info()\n",
    "group4.info()\n",
    "group5.info()\n",
    "\n",
    "# example: retrieve group1 primary data\n",
    "group1.groupPrimaryData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mouse:\n",
    "    \n",
    "    # From Mouse object we can easily retrieve individual mouse data\n",
    "    \n",
    "    def __init__(self, grouping, ID: str):\n",
    "\n",
    "        # initialize parameter: \n",
    "        #     grouping: a list of Group objects\n",
    "        #     ID: mouse id\n",
    "        # properties:\n",
    "        #     ID, category, primaryData, experimentalData\n",
    "        # methods:\n",
    "        #     mouse.get_age_wks(self,date): return age(wks) ceiled by certain date\n",
    "        #     mouse.get_weekly_performance(self): return weekly performance (dataframe) of individual mouse \n",
    "\n",
    "        self.ID = ID \n",
    "        for group in grouping:\n",
    "            if ID in group.members:\n",
    "                self.category = group.name \n",
    "                self.primaryData = group.groupPrimaryData[group.groupPrimaryData['Mouse ID']==self.ID]\n",
    "                experimentalData = group.groupExperimentalData[group.groupExperimentalData['Mouse ID']==int(self.ID)]\n",
    "                experimentalData.reset_index(drop=True, inplace=True)\n",
    "                self.experimentalData = experimentalData\n",
    "    \n",
    "    def get_age_wks(self,date):\n",
    "        \n",
    "        self.primaryData['Age(days)'] = np.empty_like(self.primaryData['Mouse ID'])\n",
    "        today = pd.to_datetime(str(datetime.date.today()))\n",
    "        \n",
    "        for i in list(self.primaryData.index):\n",
    "            dob = pd.to_datetime(str(self.primaryData['DOB'][i]))\n",
    "            age_days = str(date - dob)[:-14]\n",
    "            age_wks = math.ceil(int(age_days) / 7.0)\n",
    "            \n",
    "        return age_wks\n",
    "    \n",
    "    \n",
    "    def get_weekly_performance(self):\n",
    "\n",
    "        m_df = self.experimentalData[['Date','Weight','Baited Arms Retrieved','Reference Memory Errors','Working Memory Errors','Video Length']]\n",
    "        m_df['Date'] = pd.to_datetime(m_df['Date'])\n",
    "\n",
    "        bins_dt = pd.date_range('2020-01-01', freq='7D', periods=100)\n",
    "        bins_str = bins_dt.astype(str).values\n",
    "\n",
    "        labels = ['({}, {}]'.format(bins_str[i-1], bins_str[i]) for i in range(1, len(bins_str))]\n",
    "\n",
    "\n",
    "        m_df['cat'] = pd.cut(m_df.Date.astype(np.int64)//10**9,\n",
    "                           bins=bins_dt.astype(np.int64)//10**9,\n",
    "                           labels=labels)\n",
    "        m_df['cat'] = m_df['cat'].cat.remove_unused_categories()\n",
    "        \n",
    "        m_df['Age(wks)'] = np.empty_like(m_df['Date'])\n",
    "        for i in range(len(m_df['Date'])):\n",
    "            m_df.loc[i,'Age(wks)'] = int(self.get_age_wks(m_df.loc[i,'Date']))\n",
    "        m_df['Age(wks)'] = m_df['Age(wks)'].astype(int)\n",
    "        \n",
    "        m_df['Mouse ID'] = np.empty_like(m_df['Date'])\n",
    "        for i in range(len(m_df['Date'])):\n",
    "            m_df.loc[i,'Mouse ID'] = self.ID\n",
    "        \n",
    "        m_df = m_df[['Date','Mouse ID','Weight','Age(wks)','Baited Arms Retrieved','Reference Memory Errors','Working Memory Errors','Video Length','cat']]\n",
    "        m_df.sort_values(by='Date',inplace=True)\n",
    "        m_df.reset_index(drop=True,inplace=True)\n",
    "        \n",
    "        return m_df\n",
    "\n",
    "##################################################################################################\n",
    "\n",
    "def collect_weekly_performance(group=None):\n",
    "    \n",
    "    if group == None:\n",
    "        # get weekly data for all mice\n",
    "        m_ID = allMice[0]\n",
    "        m = Mouse(grouping,m_ID)\n",
    "        df = m.get_weekly_performance()\n",
    "        \n",
    "        for m_ID in allMice[1:]:\n",
    "\n",
    "            m = Mouse(grouping,m_ID)\n",
    "            df_append = m.get_weekly_performance()\n",
    "            df = pd.concat([df,df_append],axis=0)\n",
    "        \n",
    "        df.sort_values(by='Age(wks)',inplace=True)\n",
    "        return df\n",
    "    \n",
    "    else:\n",
    "        # get weekly data for mice in one group\n",
    "        m_ID = group.members[0]\n",
    "        m = Mouse(grouping,m_ID)\n",
    "        df = m.get_weekly_performance()\n",
    "        \n",
    "        for m_ID in group.members[1:]:\n",
    "            \n",
    "            m = Mouse(grouping,m_ID)\n",
    "            df_append = m.get_weekly_performance()\n",
    "            df = pd.concat([df,df_append],axis=0)\n",
    "        \n",
    "        return df\n",
    "\n",
    "##################################################################################################\n",
    "\n",
    "def get_weekly_mean(df):\n",
    "    \n",
    "    df_mean = df.groupby('Age(wks)').mean()\n",
    "    df_mean.reset_index(drop=False,inplace=True)\n",
    "    df_mean.sort_values(by='Age(wks)',inplace=True)\n",
    "    df_mean.reset_index(drop=True,inplace=True)\n",
    "    \n",
    "    return df_mean\n",
    "\n",
    "##################################################################################################\n",
    "\n",
    "def plot_weekly_mean(df_mean,group,ax):\n",
    "    \n",
    "    weight_mean = df_mean['Weight']\n",
    "    finished_mean = df_mean['Baited Arms Retrieved']\n",
    "    rme_mean = df_mean['Reference Memory Errors']\n",
    "    wme_mean =  df_mean['Working Memory Errors']\n",
    "    \n",
    "    age_wks = sorted(df_mean['Age(wks)'])\n",
    "    \n",
    "    l1, = plt.plot(age_wks,rme_mean,label='avg_RME')\n",
    "    l2, = plt.plot(age_wks,wme_mean,label='avg_WME') \n",
    "    ax.legend()\n",
    "    ax.set_title(group.name)\n",
    "    ax.set_xlabel('Age(wks)')\n",
    "    ax.set_ylabel('Errors')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(24,12))\n",
    "i = 1\n",
    "\n",
    "for group in grouping:\n",
    "    \n",
    "    df = collect_weekly_performance(group)\n",
    "    df_mean = get_weekly_mean(df)\n",
    "    \n",
    "    ax = fig.add_subplot(2,3,i)\n",
    "    plot_weekly_mean(df_mean,group,ax)\n",
    "\n",
    "    i += 1\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.4, hspace=0.4)\n",
    "plt.savefig(r'./fig/performance_snapshot_group.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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