data_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('./data/36_4_7.xlsx', header=[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
       "      <th>Unnamed: 1_level_0</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Daily ethanol intake (g/kg/day)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Daily total liquid</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ethanol preference (100*daily ethanol/daily total liquid)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Daily liquid/weight (1000*ml/kg)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Daily food intake/weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Mouse Category</th>\n",
       "      <th>Mouse #</th>\n",
       "      <th>Day 1</th>\n",
       "      <th>Day 2</th>\n",
       "      <th>Day 3</th>\n",
       "      <th>Day 4</th>\n",
       "      <th>Day 1</th>\n",
       "      <th>Day 2</th>\n",
       "      <th>Day 3</th>\n",
       "      <th>Day 4</th>\n",
       "      <th>...</th>\n",
       "      <th>Day 3</th>\n",
       "      <th>Day 4</th>\n",
       "      <th>Day 1</th>\n",
       "      <th>Day 2</th>\n",
       "      <th>Day 3</th>\n",
       "      <th>Day 4</th>\n",
       "      <th>Day 1</th>\n",
       "      <th>Day 2</th>\n",
       "      <th>Day 3</th>\n",
       "      <th>Day 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Group-1;Type-A;gradual</td>\n",
       "      <td>1</td>\n",
       "      <td>2.428519</td>\n",
       "      <td>1.185000</td>\n",
       "      <td>1.273756</td>\n",
       "      <td>0.580301</td>\n",
       "      <td>4.15</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>51.086957</td>\n",
       "      <td>19.819820</td>\n",
       "      <td>209.259259</td>\n",
       "      <td>189.285714</td>\n",
       "      <td>157.804460</td>\n",
       "      <td>185.308848</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.571429</td>\n",
       "      <td>5.454545</td>\n",
       "      <td>5.809683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Group-2;Type-B;non</td>\n",
       "      <td>2</td>\n",
       "      <td>1.634483</td>\n",
       "      <td>0.396416</td>\n",
       "      <td>0.521181</td>\n",
       "      <td>0.051634</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>13.768116</td>\n",
       "      <td>1.290323</td>\n",
       "      <td>435.344828</td>\n",
       "      <td>215.053763</td>\n",
       "      <td>239.583333</td>\n",
       "      <td>253.267974</td>\n",
       "      <td>9.762931</td>\n",
       "      <td>6.290323</td>\n",
       "      <td>7.239583</td>\n",
       "      <td>6.960784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Group-2;Type-B;non</td>\n",
       "      <td>3</td>\n",
       "      <td>2.842678</td>\n",
       "      <td>1.433402</td>\n",
       "      <td>2.197984</td>\n",
       "      <td>2.450099</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.9</td>\n",
       "      <td>...</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>78.787879</td>\n",
       "      <td>238.493724</td>\n",
       "      <td>214.432990</td>\n",
       "      <td>201.612903</td>\n",
       "      <td>196.819085</td>\n",
       "      <td>6.841004</td>\n",
       "      <td>7.175258</td>\n",
       "      <td>7.379032</td>\n",
       "      <td>7.514911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Group-2;Type-B;non</td>\n",
       "      <td>4</td>\n",
       "      <td>1.602899</td>\n",
       "      <td>1.420863</td>\n",
       "      <td>2.268858</td>\n",
       "      <td>0.697453</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.15</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>69.166667</td>\n",
       "      <td>21.487603</td>\n",
       "      <td>210.144928</td>\n",
       "      <td>233.812950</td>\n",
       "      <td>207.612457</td>\n",
       "      <td>205.432937</td>\n",
       "      <td>5.869565</td>\n",
       "      <td>7.823741</td>\n",
       "      <td>7.006920</td>\n",
       "      <td>9.066214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Group-2;Type-B;non</td>\n",
       "      <td>5</td>\n",
       "      <td>0.405872</td>\n",
       "      <td>0.931023</td>\n",
       "      <td>0.187140</td>\n",
       "      <td>1.706710</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.35</td>\n",
       "      <td>3.3</td>\n",
       "      <td>...</td>\n",
       "      <td>4.794521</td>\n",
       "      <td>44.295302</td>\n",
       "      <td>326.605505</td>\n",
       "      <td>249.566724</td>\n",
       "      <td>247.038917</td>\n",
       "      <td>243.862520</td>\n",
       "      <td>5.724771</td>\n",
       "      <td>5.251300</td>\n",
       "      <td>7.563452</td>\n",
       "      <td>7.266776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0_level_0 Unnamed: 1_level_0 Daily ethanol intake (g/kg/day)  \\\n",
       "           Mouse Category            Mouse #                           Day 1   \n",
       "0  Group-1;Type-A;gradual                  1                        2.428519   \n",
       "1      Group-2;Type-B;non                  2                        1.634483   \n",
       "2      Group-2;Type-B;non                  3                        2.842678   \n",
       "3      Group-2;Type-B;non                  4                        1.602899   \n",
       "4      Group-2;Type-B;non                  5                        0.405872   \n",
       "\n",
       "                                Daily total liquid                    ...  \\\n",
       "      Day 2     Day 3     Day 4              Day 1 Day 2 Day 3 Day 4  ...   \n",
       "0  1.185000  1.273756  0.580301               4.15   2.1  2.35   1.1  ...   \n",
       "1  0.396416  0.521181  0.051634               2.40   0.7  0.95   0.1  ...   \n",
       "2  1.433402  2.197984  2.450099               4.30   2.2  3.45   3.9  ...   \n",
       "3  1.420863  2.268858  0.697453               2.80   2.5  4.15   1.3  ...   \n",
       "4  0.931023  0.187140  1.706710               0.70   1.7  0.35   3.3  ...   \n",
       "\n",
       "  Ethanol preference (100*daily ethanol/daily total liquid)             \\\n",
       "                                                      Day 3      Day 4   \n",
       "0                                          51.086957         19.819820   \n",
       "1                                          13.768116          1.290323   \n",
       "2                                          69.000000         78.787879   \n",
       "3                                          69.166667         21.487603   \n",
       "4                                           4.794521         44.295302   \n",
       "\n",
       "  Daily liquid/weight (1000*ml/kg)                                      \\\n",
       "                             Day 1       Day 2       Day 3       Day 4   \n",
       "0                       209.259259  189.285714  157.804460  185.308848   \n",
       "1                       435.344828  215.053763  239.583333  253.267974   \n",
       "2                       238.493724  214.432990  201.612903  196.819085   \n",
       "3                       210.144928  233.812950  207.612457  205.432937   \n",
       "4                       326.605505  249.566724  247.038917  243.862520   \n",
       "\n",
       "  Daily food intake/weight                                \n",
       "                     Day 1     Day 2     Day 3     Day 4  \n",
       "0                 5.500000  5.571429  5.454545  5.809683  \n",
       "1                 9.762931  6.290323  7.239583  6.960784  \n",
       "2                 6.841004  7.175258  7.379032  7.514911  \n",
       "3                 5.869565  7.823741  7.006920  9.066214  \n",
       "4                 5.724771  5.251300  7.563452  7.266776  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pair Plot (Abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_ethanol_columns = [('Daily ethanol intake (g/kg/day)', 'Day 1'), ('Daily ethanol intake (g/kg/day)', 'Day 2'), \n",
    "                         ('Daily ethanol intake (g/kg/day)', 'Day 3'), ('Daily ethanol intake (g/kg/day)', 'Day 4')]\n",
    "daily_total_liquid_columns = [('Daily total liquid', 'Day 1'), ('Daily total liquid', 'Day 2'), \n",
    "                              ('Daily total liquid', 'Day 3'), ('Daily total liquid', 'Day 4')]\n",
    "daily_food_intake_columns = [('Daily food intake (g)', 'Day 1'), ('Daily food intake (g)', 'Day 2'), \n",
    "                             ('Daily food intake (g)', 'Day 3'), ('Daily food intake (g)', 'Day 4')]\n",
    "body_weight_columns = [('Body weight (g)', 'Day 1'), ('Body weight (g)', 'Day 2'), \n",
    "                       ('Body weight (g)', 'Day 3'), ('Body weight (g)', 'Day 4')]\n",
    "ethanol_preference_columns = [('Ethanol preference (100*daily ethanol/daily total liquid)', 'Day 1'), ('Ethanol preference (100*daily ethanol/daily total liquid)', 'Day 2'), \n",
    "                              ('Ethanol preference (100*daily ethanol/daily total liquid)', 'Day 3'), ('Ethanol preference (100*daily ethanol/daily total liquid)', 'Day 4')]\n",
    "daily_liquid_weight_columns = [('Daily liquid/weight (1000*ml/kg)', 'Day 1'), ('Daily liquid/weight (1000*ml/kg)', 'Day 2'), \n",
    "                               ('Daily liquid/weight (1000*ml/kg)', 'Day 3'), ('Daily liquid/weight (1000*ml/kg)', 'Day 4')]\n",
    "daily_food_intake_weight_columns = [('Daily food intake/weight', 'Day 1'), ('Daily food intake/weight', 'Day 2'),\n",
    "                                    ('Daily food intake/weight', 'Day 3'), ('Daily food intake/weight', 'Day 4')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Mouse #1:\n",
      "Time Step 1: [  2.42851852   4.15         4.95        27.          73.45132743\n",
      " 209.25925926   5.5       ]\n",
      "Time Step 2: [  1.185        2.1          5.2         28.          39.62264151\n",
      " 189.28571429   5.57142857]\n",
      "Time Step 3: [  1.27375643   2.35         5.3         29.15        51.08695652\n",
      " 157.80445969   5.45454545]\n",
      "Time Step 4: [  0.5803005    1.1          5.8         29.95        19.81981982\n",
      " 185.30884808   5.8096828 ]\n",
      "\n",
      " Mouse #2:\n",
      "Time Step 1: [  1.63448276   2.4          7.55        23.2         23.76237624\n",
      " 435.34482759   9.76293103]\n",
      "Time Step 2: [  0.39641577   0.7          5.85        27.9         11.66666667\n",
      " 215.05376344   6.29032258]\n",
      "Time Step 3: [  0.52118056   0.95         6.95        28.8         13.76811594\n",
      " 239.58333333   7.23958333]\n",
      "Time Step 4: [5.16339869e-02 1.00000000e-01 7.10000000e+00 3.06000000e+01\n",
      " 1.29032258e+00 2.53267974e+02 6.96078431e+00]\n",
      "\n",
      " Mouse #3:\n",
      "Time Step 1: [  2.84267782   4.3          5.45        23.9         75.43859649\n",
      " 238.49372385   6.84100418]\n",
      "Time Step 2: [  1.43340206   2.2          5.8         24.25        42.30769231\n",
      " 214.43298969   7.17525773]\n",
      "Time Step 3: [  2.19798387   3.45         6.1         24.8         69.\n",
      " 201.61290323   7.37903226]\n",
      "Time Step 4: [  2.4500994    3.9          6.3         25.15        78.78787879\n",
      " 196.81908549   7.51491054]\n",
      "\n",
      " Mouse #4:\n",
      "Time Step 1: [  1.60289855   2.8          5.4         27.6         48.27586207\n",
      " 210.14492754   5.86956522]\n",
      "Time Step 2: [  1.42086331   2.5          7.25        27.8         38.46153846\n",
      " 233.81294964   7.82374101]\n",
      "Time Step 3: [  2.26885813   4.15         6.75        28.9         69.16666667\n",
      " 207.61245675   7.00692042]\n",
      "Time Step 4: [  0.69745331   1.3          8.9         29.45        21.48760331\n",
      " 205.43293718   9.06621392]\n",
      "\n",
      " Mouse #5:\n",
      "Time Step 1: [  0.40587156   0.7          5.2         27.25         7.86516854\n",
      " 326.60550459   5.72477064]\n",
      "Time Step 2: [  0.93102253   1.7          5.05        28.85        23.61111111\n",
      " 249.56672444   5.25129983]\n",
      "Time Step 3: [1.87140440e-01 3.50000000e-01 7.45000000e+00 2.95500000e+01\n",
      " 4.79452055e+00 2.47038917e+02 7.56345178e+00]\n",
      "Time Step 4: [  1.70671031   3.3          7.4         30.55        44.29530201\n",
      " 243.86252046   7.26677578]\n",
      "\n",
      " Mouse #6:\n",
      "Time Step 1: [  1.57707948   2.7          5.15        27.05        34.17721519\n",
      " 292.05175601   5.7116451 ]\n",
      "Time Step 2: [  1.7771529    3.2          4.9         28.45        48.48484848\n",
      " 231.98594025   5.16695958]\n",
      "Time Step 3: [  1.3234188    2.45         6.3         29.25        37.12121212\n",
      " 225.64102564   6.46153846]\n",
      "Time Step 4: [  2.40264463   4.6          6.45        30.25        69.17293233\n",
      " 219.83471074   6.39669421]\n",
      "\n",
      " Mouse #7:\n",
      "Time Step 1: [  1.90872483   2.7          5.15        22.35        47.36842105\n",
      " 255.03355705   6.91275168]\n",
      "Time Step 2: [  1.26134454   1.9          5.95        23.8         31.14754098\n",
      " 256.30252101   7.5       ]\n",
      "Time Step 3: [  1.69171717   2.65         5.85        24.75        50.\n",
      " 214.14141414   7.09090909]\n",
      "Time Step 4: [  1.0431068    1.7          5.35        25.75        34.34343434\n",
      " 192.23300971   6.23300971]\n",
      "\n",
      " Mouse #8:\n",
      "Time Step 1: [  1.7064       2.7          1.95        25.          47.36842105\n",
      " 228.           2.34      ]\n",
      "Time Step 2: [  0.54587332   0.9          4.4         26.05        18.75\n",
      " 184.26103647   5.0671785 ]\n",
      "Time Step 3: [  1.51751412   2.55         5.85        26.55        49.03846154\n",
      " 195.85687382   6.61016949]\n",
      "Time Step 4: [  0.46215722   0.8          4.9         27.35        16.84210526\n",
      " 173.67458867   5.37477148]\n",
      "\n",
      " Mouse #9:\n",
      "Time Step 1: [  2.1907563    3.3          4.95        23.8         53.22580645\n",
      " 260.50420168   6.2394958 ]\n",
      "Time Step 2: [  1.08306452   1.7          6.15        24.8         25.75757576\n",
      " 266.12903226   7.43951613]\n",
      "Time Step 3: [  2.29651163   3.75         6.7         25.8         60.48387097\n",
      " 240.31007752   7.79069767]\n",
      "Time Step 4: [  1.13712121   1.9          7.85        26.4         30.4\n",
      " 236.74242424   8.92045455]\n",
      "\n",
      " Mouse #10:\n",
      "Time Step 1: [  1.86614173   3.           4.1         25.4         57.69230769\n",
      " 204.72440945   4.84251969]\n",
      "Time Step 2: [  0.47252336   0.8          5.35        26.75        16.32653061\n",
      " 183.17757009   6.        ]\n",
      "Time Step 3: [  0.71299639   1.25         6.35        27.7         27.17391304\n",
      " 166.06498195   6.87725632]\n",
      "Time Step 4: [  0.22411348   0.4          7.55        28.2          7.47663551\n",
      " 189.71631206   8.03191489]\n",
      "\n",
      " Mouse #11:\n",
      "Time Step 1: [  0.4976378    0.8          5.25        25.4          8.5106383\n",
      " 370.07874016   6.2007874 ]\n",
      "Time Step 2: [  0.84266667   1.4          5.85        26.25        18.66666667\n",
      " 285.71428571   6.68571429]\n",
      "Time Step 3: [  0.61104972   1.05         5.8         27.15        15.44117647\n",
      " 250.46040516   6.40883978]\n",
      "Time Step 4: [  0.52375691   0.9          6.6         27.15        11.46496815\n",
      " 289.13443831   7.29281768]\n",
      "\n",
      " Mouse #12:\n",
      "Time Step 1: [  0.7250478    1.2          4.55        26.15        18.18181818\n",
      " 252.39005736   5.21988528]\n",
      "Time Step 2: [  1.4738806    2.5          4.15        26.8         49.01960784\n",
      " 190.29850746   4.64552239]\n",
      "Time Step 3: [  0.55183824   0.95         5.25        27.2         17.27272727\n",
      " 202.20588235   5.79044118]\n",
      "Time Step 4: [  1.36648649   2.4          5.05        27.75        46.60194175\n",
      " 185.58558559   5.45945946]\n",
      "\n",
      " Mouse #13:\n",
      "Time Step 1: [  1.10232558   1.8          4.65        25.8         31.03448276\n",
      " 224.80620155   5.40697674]\n",
      "Time Step 2: [  1.25681818   2.1          5.          26.4         35.\n",
      " 227.27272727   5.68181818]\n",
      "Time Step 3: [  0.72610294   1.25         4.95        27.2         21.55172414\n",
      " 213.23529412   5.45955882]\n",
      "Time Step 4: [  1.14492754   2.           3.3         27.6         34.18803419\n",
      " 211.95652174   3.58695652]\n",
      "\n",
      " Mouse #14:\n",
      "Time Step 1: [  1.11598513   1.9          7.          26.9         33.33333333\n",
      " 211.89591078   7.80669145]\n",
      "Time Step 2: [  0.92600733   1.6          6.          27.3         32.\n",
      " 183.15018315   6.59340659]\n",
      "Time Step 3: [  0.87153025   1.55         6.85        28.1         29.80769231\n",
      " 185.05338078   7.31316726]\n",
      "Time Step 4: [  0.83304042   1.5          5.75        28.45        30.3030303\n",
      " 173.98945518   6.06326889]\n",
      "\n",
      " Mouse #15:\n",
      "Time Step 1: [  0.96886792   1.3          8.3         21.2         25.\n",
      " 245.28301887  11.74528302]\n",
      "Time Step 2: [  0.63766816   0.9          9.25        22.3         20.93023256\n",
      " 192.82511211  12.44394619]\n",
      "Time Step 3: [  0.30980392   0.45         7.85        22.95        11.25\n",
      " 174.291939    10.26143791]\n",
      "Time Step 4: [  0.74913793   1.1          9.2         23.2         24.71910112\n",
      " 191.81034483  11.89655172]\n",
      "\n",
      " Mouse #16:\n",
      "Time Step 1: [  1.40966543   2.4          5.05        26.9         28.23529412\n",
      " 315.98513011   5.63197026]\n",
      "Time Step 2: [  1.23701068   2.2          6.2         28.1         28.94736842\n",
      " 270.46263345   6.61921708]\n",
      "Time Step 3: [2.72884283e-02 5.00000000e-02 7.05000000e+00 2.89500000e+01\n",
      " 6.17283951e-01 2.79792746e+02 7.30569948e+00]\n",
      "Time Step 4: [1.60677966e-01 3.00000000e-01 6.35000000e+00 2.95000000e+01\n",
      " 4.19580420e+00 2.42372881e+02 6.45762712e+00]\n",
      "\n",
      " Mouse #17:\n",
      "Time Step 1: [1.55665025e-01 3.00000000e-01 6.15000000e+00 3.04500000e+01\n",
      " 4.54545455e+00 2.16748768e+02 6.05911330e+00]\n",
      "Time Step 2: [9.92150706e-02 2.00000000e-01 6.90000000e+00 3.18500000e+01\n",
      " 3.12500000e+00 2.00941915e+02 6.49921507e+00]\n",
      "Time Step 3: [  0.           0.          25.25        32.6          0.\n",
      " 185.58282209  23.23619632]\n",
      "Time Step 4: [1.46976744e-01 3.00000000e-01 1.07000000e+01 3.22500000e+01\n",
      " 4.72440945e+00 1.96899225e+02 9.95348837e+00]\n",
      "\n",
      " Mouse #18:\n",
      "Time Step 1: [  1.06587302   1.7          5.9         25.2         26.5625\n",
      " 253.96825397   7.02380952]\n",
      "Time Step 2: [1.76208178e-01 3.00000000e-01 5.30000000e+00 2.69000000e+01\n",
      " 5.45454545e+00 2.04460967e+02 5.91078067e+00]\n",
      "Time Step 3: [  0.25854545   0.45         6.8         27.5          7.37704918\n",
      " 221.81818182   7.41818182]\n",
      "Time Step 4: [  0.33676732   0.6          6.1         28.15        10.61946903\n",
      " 200.71047957   6.5008881 ]\n",
      "\n",
      " Mouse #19:\n",
      "Time Step 1: [  1.12255773   0.4          5.8         28.15         4.76190476\n",
      " 298.40142096   6.18117229]\n",
      "Time Step 2: [  0.79131886   0.3          5.1         29.95         4.10958904\n",
      " 243.73956594   5.10851419]\n",
      "Time Step 3: [3.87254902e-01 1.50000000e-01 8.10000000e+00 3.06000000e+01\n",
      " 1.87500000e+00 2.61437908e+02 7.94117647e+00]\n",
      "Time Step 4: [3.85365854e-01 1.50000000e-01 4.70000000e+00 3.07500000e+01\n",
      " 2.58620690e+00 1.88617886e+02 4.58536585e+00]\n",
      "\n",
      " Mouse #20:\n",
      "Time Step 1: [  1.25396825   0.4          7.05        25.2          4.44444444\n",
      " 357.14285714   8.39285714]\n",
      "Time Step 2: [5.31092437e-01 2.00000000e-01 5.90000000e+00 2.97500000e+01\n",
      " 2.81690141e+00 2.38655462e+02 5.94957983e+00]\n",
      "Time Step 3: [  2.22719735   0.85         7.5         30.15         9.23913043\n",
      " 305.14096186   7.46268657]\n",
      "Time Step 4: [  1.39262821   0.55         6.65        31.2          6.25\n",
      " 282.05128205   6.39423077]\n",
      "\n",
      " Mouse #21:\n",
      "Time Step 1: [  0.87292818   0.3          5.05        27.15         4.34782609\n",
      " 254.14364641   5.5801105 ]\n",
      "Time Step 2: [  0.57246377   0.2          5.9         27.6          3.63636364\n",
      " 199.27536232   6.41304348]\n",
      "Time Step 3: [  0.68221071   0.25         5.8         28.95         4.38596491\n",
      " 196.89119171   6.01036269]\n",
      "Time Step 4: [1.32550336e-01 5.00000000e-02 6.40000000e+00 2.98000000e+01\n",
      " 8.62068966e-01 1.94630872e+02 6.44295302e+00]\n",
      "\n",
      " Mouse #22:\n",
      "Time Step 1: [  1.00211416   0.3          5.85        23.65         5.88235294\n",
      " 215.6448203    7.42071882]\n",
      "Time Step 2: [  0.6171875    0.2          6.6         25.6          4.76190476\n",
      " 164.0625       7.734375  ]\n",
      "Time Step 3: [  0.           0.           6.8         26.55         0.\n",
      " 145.0094162    7.68361582]\n",
      "Time Step 4: [1.47388060e-01 5.00000000e-02 6.10000000e+00 2.68000000e+01\n",
      " 1.25000000e+00 1.49253731e+02 6.82835821e+00]\n",
      "\n",
      " Mouse #23:\n",
      "Time Step 1: [  1.19020716   0.4          5.7         26.55         5.71428571\n",
      " 263.65348399   6.44067797]\n",
      "Time Step 2: [  5.02473498   1.8          6.45        28.3         21.42857143\n",
      " 296.81978799   6.83745583]\n",
      "Time Step 3: [1.33220911e-01 5.00000000e-02 6.15000000e+00 2.96500000e+01\n",
      " 7.14285714e-01 2.36087690e+02 6.22259696e+00]\n",
      "Time Step 4: [  0.           0.           7.7         29.5          0.\n",
      " 245.76271186   7.83050847]\n",
      "\n",
      " Mouse #24:\n",
      "Time Step 1: [  7.49278351   2.3          5.15        24.25        47.91666667\n",
      " 197.93814433   6.37113402]\n",
      "Time Step 2: [ 10.3109405    3.4          4.3         26.05        72.34042553\n",
      " 180.42226488   4.95201536]\n",
      "Time Step 3: [  7.54494382   2.55         6.85        26.7         49.03846154\n",
      " 194.75655431   7.69662921]\n",
      "Time Step 4: [  7.46345811   2.65         5.8         28.05        53.\n",
      " 178.25311943   6.20320856]\n",
      "\n",
      " Mouse #25:\n",
      "Time Step 1: [6.69491525e-01 2.00000000e-01 4.85000000e+00 2.36000000e+01\n",
      " 3.22580645e+00 2.62711864e+02 6.16525424e+00]\n",
      "Time Step 2: [3.23108384e-01 1.00000000e-01 5.00000000e+00 2.44500000e+01\n",
      " 1.69491525e+00 2.41308793e+02 6.13496933e+00]\n",
      "Time Step 3: [4.75903614e-01 1.50000000e-01 6.30000000e+00 2.49000000e+01\n",
      " 2.27272727e+00 2.65060241e+02 7.59036145e+00]\n",
      "Time Step 4: [1.53100775e-01 5.00000000e-02 5.35000000e+00 2.58000000e+01\n",
      " 8.77192982e-01 2.20930233e+02 6.22093023e+00]\n",
      "\n",
      " Mouse #26:\n",
      "Time Step 1: [8.71323529e-01 3.00000000e-01 5.50000000e+00 2.72000000e+01\n",
      " 3.19148936e+00 3.45588235e+02 6.06617647e+00]\n",
      "Time Step 2: [  1.64298094   0.6          5.75        28.85         6.66666667\n",
      " 311.95840555   5.97920277]\n",
      "Time Step 3: [  1.43874172   0.55         6.05        30.2          5.6122449\n",
      " 324.50331126   6.00993377]\n",
      "Time Step 4: [3.84740260e-01 1.50000000e-01 5.55000000e+00 3.08000000e+01\n",
      " 1.92307692e+00 2.53246753e+02 5.40584416e+00]\n",
      "\n",
      " Mouse #27:\n",
      "Time Step 1: [  0.91682785   0.3          6.65        25.85         4.47761194\n",
      " 259.18762089   7.71760155]\n",
      "Time Step 2: [5.63279857e-01 2.00000000e-01 5.85000000e+00 2.80500000e+01\n",
      " 3.12500000e+00 2.28163993e+02 6.25668449e+00]\n",
      "Time Step 3: [  0.68576389   0.25         6.5         28.8          3.84615385\n",
      " 225.69444444   6.77083333]\n",
      "Time Step 4: [  0.67062818   0.25         6.25        29.45         4.03225806\n",
      " 210.52631579   6.36672326]\n",
      "\n",
      " Mouse #28:\n",
      "Time Step 1: [  1.8126195    0.6          6.6         26.15         8.69565217\n",
      " 263.8623327    7.57170172]\n",
      "Time Step 2: [5.66308244e-01 2.00000000e-01 7.30000000e+00 2.79000000e+01\n",
      " 3.03030303e+00 2.36559140e+02 7.84946237e+00]\n",
      "Time Step 3: [4.05821918e-01 1.50000000e-01 7.90000000e+00 2.92000000e+01\n",
      " 2.41935484e+00 2.12328767e+02 8.11643836e+00]\n",
      "Time Step 4: [  0.67869416   0.25         6.1         29.1          4.03225806\n",
      " 213.05841924   6.28865979]\n",
      "\n",
      " Mouse #29:\n",
      "Time Step 1: [  3.53072626   1.2          6.3         26.85        15.78947368\n",
      " 283.05400372   7.03910615]\n",
      "Time Step 2: [  2.73830156   1.          10.55        28.85        11.49425287\n",
      " 301.55979203  10.97053726]\n",
      "Time Step 3: [1.27214171e-01 5.00000000e-02 1.09000000e+01 3.10500000e+01\n",
      " 6.75675676e-01 2.38325282e+02 1.05314010e+01]\n",
      "Time Step 4: [1.26602564e-01 5.00000000e-02 7.50000000e+00 3.12000000e+01\n",
      " 6.84931507e-01 2.33974359e+02 7.21153846e+00]\n",
      "\n",
      " Mouse #30:\n",
      "Time Step 1: [  1.22957198   0.4          5.7         25.7          4.65116279\n",
      " 334.63035019   6.6536965 ]\n",
      "Time Step 2: [  1.41831239   0.5          5.95        27.85         7.35294118\n",
      " 244.16517056   6.40933573]\n",
      "Time Step 3: [1.36442142e-01 5.00000000e-02 7.50000000e+00 2.89500000e+01\n",
      " 6.57894737e-01 2.62521589e+02 7.77202073e+00]\n",
      "Time Step 4: [1.33671743e-01 5.00000000e-02 6.95000000e+00 2.95500000e+01\n",
      " 7.35294118e-01 2.30118443e+02 7.05583756e+00]\n",
      "\n",
      " Mouse #31:\n",
      "Time Step 1: [  3.78767123   1.4          6.95        29.2         15.90909091\n",
      " 301.36986301   7.14041096]\n",
      "Time Step 2: [  2.54428341   1.           6.8         31.05        11.9047619\n",
      " 270.53140097   6.57004831]\n",
      "Time Step 3: [  5.54602184   2.25         7.35        32.05        28.48101266\n",
      " 246.48985959   6.8798752 ]\n",
      "Time Step 4: [  5.3078125    2.15         6.75        32.          29.05405405\n",
      " 231.25         6.328125  ]\n",
      "\n",
      " Mouse #32:\n",
      "Time Step 1: [  1.68443497   0.5          6.55        23.45         5.26315789\n",
      " 405.11727079   8.37953092]\n",
      "Time Step 2: [6.34538153e-01 2.00000000e-01 6.05000000e+00 2.49000000e+01\n",
      " 1.90476190e+00 4.21686747e+02 7.28915663e+00]\n",
      "Time Step 3: [4.51428571e-01 1.50000000e-01 6.85000000e+00 2.62500000e+01\n",
      " 1.51515152e+00 3.77142857e+02 7.82857143e+00]\n",
      "Time Step 4: [1.45488029e-01 5.00000000e-02 6.85000000e+00 2.71500000e+01\n",
      " 5.10204082e-01 3.60957643e+02 7.56906077e+00]\n",
      "\n",
      " Mouse #33:\n",
      "Time Step 1: [  9.14351852   2.5          6.2         21.6         37.31343284\n",
      " 310.18518519   8.61111111]\n",
      "Time Step 2: [6.56964657e-01 2.00000000e-01 1.29000000e+01 2.40500000e+01\n",
      " 2.46913580e+00 3.36798337e+02 1.60914761e+01]\n",
      "Time Step 3: [  1.68410853   0.55        10.95        25.8          4.26356589\n",
      " 500.          12.73255814]\n",
      "Time Step 4: [  4.51012891   1.55         9.8         27.15        22.46376812\n",
      " 254.14364641  10.82872928]\n",
      "\n",
      " Mouse #34:\n",
      "Time Step 1: [5.93984962e-01 2.00000000e-01 9.65000000e+00 2.66000000e+01\n",
      " 3.44827586e+00 2.18045113e+02 1.08834586e+01]\n",
      "Time Step 2: [  1.10296684   0.4         10.15        28.65         9.30232558\n",
      " 150.08726003  10.62827225]\n",
      "Time Step 3: [4.10034602e-01 1.50000000e-01 8.85000000e+00 2.89000000e+01\n",
      " 3.26086957e+00 1.59169550e+02 9.18685121e+00]\n",
      "Time Step 4: [  0.66386555   0.25         7.7         29.75         5.\n",
      " 168.06722689   7.76470588]\n",
      "\n",
      " Mouse #35:\n",
      "Time Step 1: [  0.93675889   0.3          5.15        25.3          5.76923077\n",
      " 205.53359684   6.10671937]\n",
      "Time Step 2: [6.00760456e-01 2.00000000e-01 5.25000000e+00 2.63000000e+01\n",
      " 3.38983051e+00 2.24334601e+02 5.98859316e+00]\n",
      "Time Step 3: [  1.0202952    0.35         6.95        27.1          5.93220339\n",
      " 217.71217712   7.69372694]\n",
      "Time Step 4: [4.40520446e-01 1.50000000e-01 5.90000000e+00 2.69000000e+01\n",
      " 2.45901639e+00 2.26765799e+02 6.57992565e+00]\n",
      "\n",
      " Mouse #36:\n",
      "Time Step 1: [  1.68983957   0.6          6.25        28.05         6.97674419\n",
      " 306.59536542   6.68449198]\n",
      "Time Step 2: [  0.77960526   0.3          8.9         30.4          4.10958904\n",
      " 240.13157895   8.78289474]\n",
      "Time Step 3: [  3.42375602   1.35        10.5         31.15        17.08860759\n",
      " 253.61155698  10.11235955]\n",
      "Time Step 4: [  5.08477237   2.05         6.3         31.85        28.87323944\n",
      " 222.91993721   5.93406593]\n"
     ]
    }
   ],
   "source": [
    "column_sets = [daily_ethanol_columns, daily_total_liquid_columns, daily_food_intake_columns,\n",
    "               body_weight_columns, ethanol_preference_columns, daily_liquid_weight_columns,\n",
    "               daily_food_intake_weight_columns]\n",
    "\n",
    "num_mice = len(df)\n",
    "num_time_steps = 4\n",
    "num_features = 7 \n",
    "\n",
    "data_3d = np.zeros((num_mice, num_time_steps, num_features))\n",
    "\n",
    "for i in range(num_mice):\n",
    "    data_3d[i, :, 0] = df.iloc[i, df.columns.get_loc(daily_ethanol_columns[0]):df.columns.get_loc(daily_ethanol_columns[-1])+1].values\n",
    "    data_3d[i, :, 1] = df.iloc[i, df.columns.get_loc(daily_total_liquid_columns[0]):df.columns.get_loc(daily_total_liquid_columns[-1])+1].values\n",
    "    data_3d[i, :, 2] = df.iloc[i, df.columns.get_loc(daily_food_intake_columns[0]):df.columns.get_loc(daily_food_intake_columns[-1])+1].values\n",
    "    data_3d[i, :, 3] = df.iloc[i, df.columns.get_loc(body_weight_columns[0]):df.columns.get_loc(body_weight_columns[-1])+1].values\n",
    "    data_3d[i, :, 4] = df.iloc[i, df.columns.get_loc(ethanol_preference_columns[0]):df.columns.get_loc(ethanol_preference_columns[-1])+1].values\n",
    "    data_3d[i, :, 5] = df.iloc[i, df.columns.get_loc(daily_liquid_weight_columns[0]):df.columns.get_loc(daily_liquid_weight_columns[-1])+1].values\n",
    "    data_3d[i, :, 6] = df.iloc[i, df.columns.get_loc(daily_food_intake_weight_columns[0]):df.columns.get_loc(daily_food_intake_weight_columns[-1])+1].values\n",
    "    \n",
    "for i, sample in enumerate(data_3d):\n",
    "    print(f\"\\n Mouse #{i + 1}:\")\n",
    "    for t, time_step in enumerate(sample):\n",
    "        print(f\"Time Step {t + 1}: {time_step}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Heatmap (Abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_2d = data_3d.reshape(num_mice * num_time_steps, num_features)\n",
    "columns = [f\"Feature_{f+1}\" for f in range(num_features)]\n",
    "df_2d = pd.DataFrame(data_2d, columns=columns)\n",
    "\n",
    "correlation_matrix = df_2d.corr()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.set(font_scale=1.2)\n",
    "dataplot = sns.heatmap(correlation_matrix, cmap=\"coolwarm\", annot=True, fmt=\".2f\")\n",
    "plt.title(\"Correlation Heatmap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Interpretation: Training Accuracy vs. Testing Accuracy (Results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# results from model evaluation \n",
    "epochs = [1, 2, 3, 4]\n",
    "train_accuracy = [0.7857, 0.8214, 0.8214, 0.8214]\n",
    "test_accuracy = [0.5, 0.5, 0.5, 0.5]\n",
    "\n",
    "sns.set_style(style='whitegrid')\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(epochs, train_accuracy, marker='o', label='Training Accuracy')\n",
    "\n",
    "ax.plot(epochs, test_accuracy, marker='o', label='Test Accuracy')\n",
    "\n",
    "for i, acc in enumerate(train_accuracy):\n",
    "    ax.annotate(f\"{acc:.4f}\", (epochs[i], acc), textcoords=\"offset points\", xytext=(0, 10), ha='center')\n",
    "\n",
    "for i, acc in enumerate(test_accuracy):\n",
    "    ax.annotate(f\"{acc:.4f}\", (epochs[i], acc), textcoords=\"offset points\", xytext=(0, 10), ha='center')\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training vs. Test Accuracy', pad=20, size=17)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11.1 ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2306a5acb80a0e010bb2ad81db7acfa60593cb52837743bb9b6b35e303592e7c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}