figure1_thirst_ccu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting anneurai@datajoint.internationalbrainlab.org:3306\n"
     ]
    }
   ],
   "source": [
    "# GENERAL THINGS FOR COMPUTING AND PLOTTING\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys, time\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import scipy as sp\n",
    "\n",
    "# visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\", context=\"talk\")\n",
    "\n",
    "# ibl specific things\n",
    "import datajoint as dj\n",
    "from ibl_pipeline import reference, subject, action, acquisition, data, behavior\n",
    "from ibl_pipeline.analyses import behavior as behavioral_analyses\n",
    "\n",
    "# set path for saving figures\n",
    "figpath = 'figures' # put in the same folder, but add to .gitignore for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_uuid</th>\n",
       "      <th>subject_project</th>\n",
       "      <th>session_date</th>\n",
       "      <th>subject_nickname</th>\n",
       "      <th>sex</th>\n",
       "      <th>subject_birth_date</th>\n",
       "      <th>ear_mark</th>\n",
       "      <th>subject_line</th>\n",
       "      <th>subject_source</th>\n",
       "      <th>protocol_number</th>\n",
       "      <th>subject_description</th>\n",
       "      <th>subject_ts</th>\n",
       "      <th>subject_strain</th>\n",
       "      <th>lab_name</th>\n",
       "      <th>subjectlab_ts</th>\n",
       "      <th>subjectproject_ts</th>\n",
       "      <th>avg_weight</th>\n",
       "      <th>total_water</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>ae348d98-4f39-49e5-8c54-82588552d938</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>ZM_337</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:37</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:47</td>\n",
       "      <td>2020-09-18 14:09:26</td>\n",
       "      <td>21.370001</td>\n",
       "      <td>0.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>342f4745-7606-4506-b81d-c7e27ed78c44</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-15</td>\n",
       "      <td>ZM_342</td>\n",
       "      <td>F</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:28</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:37</td>\n",
       "      <td>2020-09-18 14:09:17</td>\n",
       "      <td>16.639999</td>\n",
       "      <td>1.515873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>704479d9-9375-4aaa-9e20-c304e0e8cb09</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>ZM_200</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:33</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:42</td>\n",
       "      <td>2020-09-18 14:09:22</td>\n",
       "      <td>27.299999</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>b00ab20e-27fd-4cf5-93a6-f7b852370c86</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>ZM_203</td>\n",
       "      <td>F</td>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:37</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:47</td>\n",
       "      <td>2020-09-18 14:09:26</td>\n",
       "      <td>20.620001</td>\n",
       "      <td>0.901500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>ae348d98-4f39-49e5-8c54-82588552d938</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>ZM_337</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:37</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:47</td>\n",
       "      <td>2020-09-18 14:09:26</td>\n",
       "      <td>22.580000</td>\n",
       "      <td>0.680000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             subject_uuid    subject_project session_date  \\\n",
       "363  ae348d98-4f39-49e5-8c54-82588552d938  collab_citricacid   2018-10-04   \n",
       "200  342f4745-7606-4506-b81d-c7e27ed78c44  collab_citricacid   2018-10-15   \n",
       "282  704479d9-9375-4aaa-9e20-c304e0e8cb09  collab_citricacid   2018-09-26   \n",
       "379  b00ab20e-27fd-4cf5-93a6-f7b852370c86  collab_citricacid   2018-10-19   \n",
       "353  ae348d98-4f39-49e5-8c54-82588552d938  collab_citricacid   2018-10-10   \n",
       "\n",
       "    subject_nickname sex subject_birth_date ear_mark  subject_line  \\\n",
       "363           ZM_337   M         2018-08-08     None  Thy1-GCaMP6s   \n",
       "200           ZM_342   F         2018-08-08     None  Thy1-GCaMP6s   \n",
       "282           ZM_200   M         2018-07-22     None  Thy1-GCaMP6s   \n",
       "379           ZM_203   F         2018-07-22     None  Thy1-GCaMP6s   \n",
       "353           ZM_337   M         2018-08-08     None  Thy1-GCaMP6s   \n",
       "\n",
       "               subject_source  protocol_number subject_description  \\\n",
       "363  CCU - Margarida colonies                1                None   \n",
       "200  CCU - Margarida colonies                1                None   \n",
       "282  CCU - Margarida colonies                1                None   \n",
       "379  CCU - Margarida colonies                1                None   \n",
       "353  CCU - Margarida colonies                1                None   \n",
       "\n",
       "             subject_ts subject_strain   lab_name       subjectlab_ts  \\\n",
       "363 2019-09-25 01:33:37       C57BL/6J  mainenlab 2020-09-18 14:09:47   \n",
       "200 2019-09-25 01:33:28       C57BL/6J  mainenlab 2020-09-18 14:09:37   \n",
       "282 2019-09-25 01:33:33       C57BL/6J  mainenlab 2020-09-18 14:09:42   \n",
       "379 2019-09-25 01:33:37       C57BL/6J  mainenlab 2020-09-18 14:09:47   \n",
       "353 2019-09-25 01:33:37       C57BL/6J  mainenlab 2020-09-18 14:09:47   \n",
       "\n",
       "      subjectproject_ts  avg_weight  total_water  \n",
       "363 2020-09-18 14:09:26   21.370001     0.680000  \n",
       "200 2020-09-18 14:09:17   16.639999     1.515873  \n",
       "282 2020-09-18 14:09:22   27.299999     2.750000  \n",
       "379 2020-09-18 14:09:26   20.620001     0.901500  \n",
       "353 2020-09-18 14:09:26   22.580000     0.680000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########### SUBJECTS ########### \n",
    "\n",
    "subj = subject.Subject * subject.SubjectLab * subject.SubjectProject & \\\n",
    "      'subject_project = \"collab_citricacid\"' & 'lab_name=\"mainenlab\"'\n",
    "\n",
    "########### WEIGHINGS ########### \n",
    "\n",
    "weight_with_date = subj * (action.WaterAdministration * action.Weighing).proj('weight', \n",
    "                                                                       'water_administered', \n",
    "                                                                       session_date='DATE(weighing_time)')\n",
    "weight_date = (dj.U('subject_uuid', 'session_date') & weight_with_date) & 'session_date < \"2018-10-24\"'\n",
    "baseline_date = (dj.U('subject_uuid', 'session_date') & weight_with_date) & 'session_date < \"2018-09-24\"'\n",
    "\n",
    "avg_weight_date = weight_date.aggr(weight_with_date, avg_weight='AVG(weight)', total_water='AVG(water_administered)') \n",
    "avg_baseline_weight_date = baseline_date.aggr(weight_with_date, avg_weight = 'AVG(weight)')\n",
    "\n",
    "b = subj * avg_weight_date \n",
    "bb = subj * avg_baseline_weight_date\n",
    "weight_df = pd.DataFrame(b.fetch(as_dict=True))\n",
    "baseline_df = pd.DataFrame(bb.fetch(as_dict=True))\n",
    "weight_df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## save to file for archiving\n",
    "weight_df[['subject_uuid', 'subject_nickname', 'subject_birth_date', 'sex', 'subject_strain','subject_line', \n",
    "           'session_date', 'avg_weight', 'total_water']].to_csv('data/figure1A_ccu_weights.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session_age  sex\n",
       "47 days      F                              [ZM_342]\n",
       "             M              [ZM_335, ZM_337, ZM_338]\n",
       "63 days      F                              [ZM_210]\n",
       "             M      [ZM_206, ZM_208, ZM_209, ZM_205]\n",
       "64 days      F                      [ZM_202, ZM_203]\n",
       "             M                              [ZM_200]\n",
       "91 days      F                          [6873, 6872]\n",
       "             M                    [6867, 6870, 6869]\n",
       "Name: subject_nickname, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_df = weight_df.copy()\n",
    "# what are the sex/age in this cohort?\n",
    "normalized_df['session_age'] = (datetime(2018, 9, 24).date() - normalized_df.subject_birth_date)\n",
    "normalized_df['session_age'].describe()\n",
    "normalized_df.groupby(['session_age', 'sex'])['subject_nickname'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_uuid</th>\n",
       "      <th>subject_project</th>\n",
       "      <th>session_date</th>\n",
       "      <th>subject_nickname</th>\n",
       "      <th>sex</th>\n",
       "      <th>subject_birth_date</th>\n",
       "      <th>ear_mark</th>\n",
       "      <th>subject_line</th>\n",
       "      <th>subject_source</th>\n",
       "      <th>protocol_number</th>\n",
       "      <th>subject_description</th>\n",
       "      <th>subject_ts</th>\n",
       "      <th>subject_strain</th>\n",
       "      <th>lab_name</th>\n",
       "      <th>subjectlab_ts</th>\n",
       "      <th>subjectproject_ts</th>\n",
       "      <th>avg_weight</th>\n",
       "      <th>total_water</th>\n",
       "      <th>session_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>26.309999</td>\n",
       "      <td>1.241667</td>\n",
       "      <td>91 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-23</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>25.590000</td>\n",
       "      <td>1.241667</td>\n",
       "      <td>91 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>28.049999</td>\n",
       "      <td>1.241667</td>\n",
       "      <td>91 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>26.059999</td>\n",
       "      <td>1.241667</td>\n",
       "      <td>91 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-09-17</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>28.950001</td>\n",
       "      <td>1.241667</td>\n",
       "      <td>91 days 00:00:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-29</td>\n",
       "      <td>Jax_female</td>\n",
       "      <td>F</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>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-05</td>\n",
       "      <td>Jax_male</td>\n",
       "      <td>M</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>31.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-05</td>\n",
       "      <td>Jax_female</td>\n",
       "      <td>F</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>23.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-12</td>\n",
       "      <td>Jax_male</td>\n",
       "      <td>M</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>32.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-12</td>\n",
       "      <td>Jax_female</td>\n",
       "      <td>F</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>23.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             subject_uuid    subject_project session_date  \\\n",
       "0    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-10-03   \n",
       "1    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-10-23   \n",
       "2    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-09-25   \n",
       "3    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-10-12   \n",
       "4    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-09-17   \n",
       "..                                    ...                ...          ...   \n",
       "455                                   NaN                NaN   2018-10-29   \n",
       "456                                   NaN                NaN   2018-11-05   \n",
       "457                                   NaN                NaN   2018-11-05   \n",
       "458                                   NaN                NaN   2018-11-12   \n",
       "459                                   NaN                NaN   2018-11-12   \n",
       "\n",
       "    subject_nickname sex subject_birth_date ear_mark  subject_line  \\\n",
       "0               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "1               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "2               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "3               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "4               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "..               ...  ..                ...      ...           ...   \n",
       "455       Jax_female   F                NaN      NaN           NaN   \n",
       "456         Jax_male   M                NaN      NaN           NaN   \n",
       "457       Jax_female   F                NaN      NaN           NaN   \n",
       "458         Jax_male   M                NaN      NaN           NaN   \n",
       "459       Jax_female   F                NaN      NaN           NaN   \n",
       "\n",
       "               subject_source  protocol_number subject_description  \\\n",
       "0    CCU - Margarida colonies              1.0                None   \n",
       "1    CCU - Margarida colonies              1.0                None   \n",
       "2    CCU - Margarida colonies              1.0                None   \n",
       "3    CCU - Margarida colonies              1.0                None   \n",
       "4    CCU - Margarida colonies              1.0                None   \n",
       "..                        ...              ...                 ...   \n",
       "455                       NaN              NaN                 NaN   \n",
       "456                       NaN              NaN                 NaN   \n",
       "457                       NaN              NaN                 NaN   \n",
       "458                       NaN              NaN                 NaN   \n",
       "459                       NaN              NaN                 NaN   \n",
       "\n",
       "              subject_ts subject_strain   lab_name        subjectlab_ts  \\\n",
       "0    2019-09-25 01:33:25       C57BL/6J  mainenlab  2020-09-18 14:09:33   \n",
       "1    2019-09-25 01:33:25       C57BL/6J  mainenlab  2020-09-18 14:09:33   \n",
       "2    2019-09-25 01:33:25       C57BL/6J  mainenlab  2020-09-18 14:09:33   \n",
       "3    2019-09-25 01:33:25       C57BL/6J  mainenlab  2020-09-18 14:09:33   \n",
       "4    2019-09-25 01:33:25       C57BL/6J  mainenlab  2020-09-18 14:09:33   \n",
       "..                   ...            ...        ...                  ...   \n",
       "455                  NaN            NaN        NaN                  NaN   \n",
       "456                  NaN            NaN        NaN                  NaN   \n",
       "457                  NaN            NaN        NaN                  NaN   \n",
       "458                  NaN            NaN        NaN                  NaN   \n",
       "459                  NaN            NaN        NaN                  NaN   \n",
       "\n",
       "       subjectproject_ts  avg_weight  total_water       session_age  \n",
       "0    2020-09-18 14:09:13   26.309999     1.241667  91 days 00:00:00  \n",
       "1    2020-09-18 14:09:13   25.590000     1.241667  91 days 00:00:00  \n",
       "2    2020-09-18 14:09:13   28.049999     1.241667  91 days 00:00:00  \n",
       "3    2020-09-18 14:09:13   26.059999     1.241667  91 days 00:00:00  \n",
       "4    2020-09-18 14:09:13   28.950001     1.241667  91 days 00:00:00  \n",
       "..                   ...         ...          ...               ...  \n",
       "455                  NaN   23.000000          NaN               NaN  \n",
       "456                  NaN   31.600000          NaN               NaN  \n",
       "457                  NaN   23.500000          NaN               NaN  \n",
       "458                  NaN   32.100000          NaN               NaN  \n",
       "459                  NaN   23.900000          NaN               NaN  \n",
       "\n",
       "[460 rows x 19 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########### ADD JAX DATA ########### \n",
    "# mean is 67 days = 9.5 weeks; so add the Jax weights at 9 weeks as day 0 \n",
    "\n",
    "jax_weights = pd.read_csv('data/jax_mouse_weights.csv')\n",
    "jax_weights.head(n=3)\n",
    "\n",
    "for weeknr, group in jax_weights.groupby(['Weeks']):\n",
    "    \n",
    "    if 8 <= group.Weeks.mean().item() <= 16:\n",
    "        normalized_df = normalized_df.append({'subject_nickname':'Jax_male',\n",
    "                              'sex':'M',\n",
    "                              'session_date':datetime(2018, 9, 24).date() + \n",
    "                                              timedelta(days=(group.Weeks.mean().item() - 9) * 7),\n",
    "                              'avg_weight':group.Males.mean().item()}, ignore_index=True)\n",
    "        \n",
    "        normalized_df = normalized_df.append({'subject_nickname':'Jax_female',\n",
    "                              'sex':'F',\n",
    "                              'session_date':datetime(2018, 9, 24).date() + \n",
    "                                              timedelta(days=(group.Weeks.mean().item() - 9) * 7),\n",
    "                              'avg_weight':group.Females.mean().item()}, ignore_index=True)\n",
    "normalized_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/iblenv/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "########### WEIGHINGS NORMALIZATION  ########### \n",
    "# day 0 is the start of the experiment, normalize per animal\n",
    "normalized_df['session_day'] = (normalized_df['session_date'] - (datetime(2018, 9, 24)).date()).dt.days\n",
    "\n",
    "# baseline: the five days before day 0\n",
    "normalized2 = pd.DataFrame([])\n",
    "for index, sjdat in normalized_df.groupby(['subject_nickname']):\n",
    "    sjdat['avg_weight'] = sjdat['avg_weight'] / sjdat.loc[sjdat.session_day < 1, 'avg_weight'].mean()\n",
    "    normalized2 = normalized2.append(sjdat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>session_day</th>\n",
       "      <th>avg_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Citric Acid</td>\n",
       "      <td>-7</td>\n",
       "      <td>0.997926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Citric Acid</td>\n",
       "      <td>-6</td>\n",
       "      <td>0.994699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Citric Acid</td>\n",
       "      <td>-5</td>\n",
       "      <td>0.994467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Citric Acid</td>\n",
       "      <td>-4</td>\n",
       "      <td>0.999491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Citric Acid</td>\n",
       "      <td>-3</td>\n",
       "      <td>1.010085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Measured water</td>\n",
       "      <td>23</td>\n",
       "      <td>0.986286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Measured water</td>\n",
       "      <td>24</td>\n",
       "      <td>1.001242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Measured water</td>\n",
       "      <td>25</td>\n",
       "      <td>1.012115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Measured water</td>\n",
       "      <td>28</td>\n",
       "      <td>1.017061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Measured water</td>\n",
       "      <td>29</td>\n",
       "      <td>1.016807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         condition  session_day  avg_weight\n",
       "0      Citric Acid           -7    0.997926\n",
       "1      Citric Acid           -6    0.994699\n",
       "2      Citric Acid           -5    0.994467\n",
       "3      Citric Acid           -4    0.999491\n",
       "4      Citric Acid           -3    1.010085\n",
       "..             ...          ...         ...\n",
       "82  Measured water           23    0.986286\n",
       "83  Measured water           24    1.001242\n",
       "84  Measured water           25    1.012115\n",
       "85  Measured water           28    1.017061\n",
       "86  Measured water           29    1.016807\n",
       "\n",
       "[87 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########### CONDITIONS ########### \n",
    "\n",
    "# CTL = free water regime (control)\n",
    "# CA = ad libitum citric acid water\n",
    "# FA = fixed amount of water per day (600 uL per weekday and 1 mL per weekend)\n",
    "# CA = calculated amount (40 uL/g/day)\n",
    "\n",
    "# now, we want to map these onto the subject names\n",
    "CTL = 'Free water'\n",
    "CA = 'Citric Acid'\n",
    "MW = 'Measured water'\n",
    "Jax = 'Jax'\n",
    "# WD = 'Weight-based amount'\n",
    "# FA = 'Fixed amount'a\n",
    "\n",
    "# condition_map = {'ZM_205':CTL, 'ZM_335':CTL, '6873':CTL, 'ZM_200':CTL, \n",
    "# '6867': CA, '6869':CA, 'ZM_209':CA, '6872':CA, 'ZM_342':CA,\n",
    "# 'ZM_202':FA, 'ZM_337':FA, 'ZM_206':FA, 'ZM_208':FA, \n",
    "# 'ZM_203':WD, 'ZM_210':WD, 'ZM_338':WD, '6870':WD}\n",
    "\n",
    "# group all mice on measured water together\n",
    "condition_map = {'ZM_205':CTL, 'ZM_335':CTL, '6873':CTL, 'ZM_200':CTL, \n",
    "'6867': CA, '6869':CA, 'ZM_209':CA, '6872':CA, 'ZM_342':CA,\n",
    "'ZM_202':MW, 'ZM_337':MW, 'ZM_206':MW, 'ZM_208':MW, \n",
    "'ZM_203':MW, 'ZM_210':MW, 'ZM_338':MW, '6870':MW, 'Jax_male':Jax, 'Jax_female':Jax}\n",
    "\n",
    "normalized2['condition'] = normalized2.subject_nickname.map(condition_map)\n",
    "normalized2.groupby(['condition', 'session_day'])['avg_weight'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "condition       sex\n",
       "Citric Acid     F                              [6872, ZM_342]\n",
       "                M                        [6867, 6869, ZM_209]\n",
       "Free water      F                                      [6873]\n",
       "                M                    [ZM_200, ZM_205, ZM_335]\n",
       "Jax             F                                [Jax_female]\n",
       "                M                                  [Jax_male]\n",
       "Measured water  F                    [ZM_202, ZM_203, ZM_210]\n",
       "                M      [6870, ZM_206, ZM_208, ZM_337, ZM_338]\n",
       "Name: subject_nickname, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized2.groupby(['condition', 'sex'])['subject_nickname'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "F    [6872, 6873, Jax_female, ZM_202, ZM_203, ZM_21...\n",
      "M    [6867, 6869, 6870, Jax_male, ZM_200, ZM_205, Z...\n",
      "Name: subject_nickname, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(normalized2.groupby(['sex'])['subject_nickname'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            count      mean       std       min       25%  \\\n",
      "condition      session_day                                                  \n",
      "Citric Acid    -7             5.0  0.997926  0.017424  0.972847  0.987865   \n",
      "               -6             5.0  0.994699  0.034910  0.970774  0.972382   \n",
      "               -5             5.0  0.994467  0.009395  0.979566  0.991001   \n",
      "               -4             5.0  0.999491  0.007330  0.989046  0.994965   \n",
      "               -3             5.0  1.010085  0.034054  0.951127  1.010986   \n",
      "                0             5.0  1.003331  0.015756  0.987394  0.988295   \n",
      "                1             5.0  0.985624  0.014806  0.971425  0.973170   \n",
      "                2             5.0  0.990054  0.027019  0.962762  0.974192   \n",
      "                3             5.0  0.969437  0.026774  0.948884  0.949846   \n",
      "                4             5.0  0.958220  0.029384  0.933761  0.939517   \n",
      "                7             5.0  0.938622  0.048890  0.882271  0.913751   \n",
      "                8             5.0  0.942692  0.051403  0.890251  0.904728   \n",
      "                9             5.0  0.944476  0.049741  0.900020  0.912802   \n",
      "                10            5.0  0.943757  0.052003  0.904461  0.905516   \n",
      "                11            5.0  0.963775  0.051942  0.912182  0.924628   \n",
      "                14            5.0  0.914815  0.055342  0.862843  0.884718   \n",
      "                15            5.0  0.926216  0.047983  0.891292  0.901164   \n",
      "                16            5.0  0.938046  0.067026  0.884407  0.897190   \n",
      "                17            5.0  0.946465  0.063816  0.898843  0.911067   \n",
      "                18            5.0  0.934125  0.062632  0.882364  0.903491   \n",
      "                21            5.0  0.937385  0.070469  0.880795  0.882965   \n",
      "                23            5.0  0.959132  0.076628  0.879843  0.919244   \n",
      "                24            5.0  0.950369  0.080487  0.870822  0.907082   \n",
      "                25            5.0  0.949940  0.073276  0.881230  0.898058   \n",
      "                28            5.0  0.956397  0.080240  0.900312  0.913751   \n",
      "                29            5.0  0.963475  0.080210  0.887822  0.933857   \n",
      "Free water     -7             4.0  0.984765  0.019905  0.954979  0.983742   \n",
      "               -6             4.0  0.990410  0.025495  0.954449  0.981386   \n",
      "               -5             4.0  0.994698  0.029772  0.951271  0.988247   \n",
      "               -4             4.0  0.993892  0.007467  0.988083  0.988602   \n",
      "               -3             4.0  1.010397  0.022715  0.992567  0.993749   \n",
      "                0             4.0  1.025837  0.045548  0.996584  1.002474   \n",
      "                1             4.0  1.022130  0.043141  0.975532  1.005461   \n",
      "                2             4.0  1.037964  0.019993  1.009001  1.032696   \n",
      "                3             4.0  1.039695  0.023223  1.016227  1.025926   \n",
      "                4             4.0  1.041033  0.033367  1.020791  1.021350   \n",
      "                7             4.0  1.046321  0.061799  0.973890  1.025325   \n",
      "                8             4.0  1.051191  0.043897  1.019034  1.028915   \n",
      "                9             4.0  1.052493  0.046350  1.017570  1.029693   \n",
      "                10            4.0  1.048748  0.049258  1.018487  1.019644   \n",
      "                11            4.0  1.067109  0.059847  1.033352  1.037053   \n",
      "                14            4.0  1.069240  0.072381  1.004383  1.023535   \n",
      "                15            4.0  1.078921  0.068165  1.011626  1.040749   \n",
      "                16            4.0  1.071154  0.093988  1.011663  1.013350   \n",
      "                17            4.0  1.090902  0.107057  1.018868  1.035143   \n",
      "                18            4.0  1.094625  0.074580  1.048935  1.055830   \n",
      "                21            4.0  1.113896  0.087828  1.049292  1.071559   \n",
      "                23            4.0  1.114872  0.082476  1.055149  1.072821   \n",
      "                24            4.0  1.123008  0.108589  1.041484  1.057684   \n",
      "                25            4.0  1.116272  0.093405  1.050756  1.057143   \n",
      "                28            4.0  1.132658  0.101787  1.051732  1.080257   \n",
      "                29            4.0  1.139994  0.102041  1.056125  1.094506   \n",
      "Jax            -7             2.0  0.980465  0.002816  0.978474  0.979469   \n",
      "                0             2.0  1.019535  0.002816  1.017544  1.018539   \n",
      "                7             2.0  1.045216  0.010779  1.037594  1.041405   \n",
      "                14            2.0  1.075909  0.011653  1.067669  1.071789   \n",
      "                21            2.0  1.114430  0.023597  1.097744  1.106087   \n",
      "                28            2.0  1.153500  0.029229  1.132832  1.143166   \n",
      "                35            2.0  1.179181  0.037192  1.152882  1.166032   \n",
      "                42            2.0  1.207368  0.041610  1.177945  1.192656   \n",
      "                49            2.0  1.227178  0.041270  1.197995  1.212586   \n",
      "Measured water -7             8.0  0.989308  0.006984  0.980568  0.981809   \n",
      "               -6             8.0  0.985645  0.013505  0.970789  0.973885   \n",
      "               -5             8.0  1.000854  0.011109  0.986800  0.990098   \n",
      "               -4             8.0  0.999640  0.006329  0.989267  0.995270   \n",
      "               -3             8.0  1.014108  0.014404  0.992239  1.006347   \n",
      "                0             8.0  1.010445  0.015575  0.994038  0.998176   \n",
      "                1             8.0  0.951700  0.013050  0.930317  0.944890   \n",
      "                2             8.0  0.935281  0.016838  0.910135  0.926104   \n",
      "                3             8.0  0.924966  0.023428  0.883639  0.915683   \n",
      "                4             8.0  0.915019  0.028158  0.870391  0.896844   \n",
      "                7             8.0  0.922325  0.029015  0.889006  0.893173   \n",
      "                8             8.0  0.924566  0.029174  0.892235  0.899315   \n",
      "                9             8.0  0.940038  0.029686  0.904336  0.917927   \n",
      "                10            8.0  0.931673  0.027474  0.900706  0.908973   \n",
      "                11            8.0  0.960036  0.026966  0.926971  0.943016   \n",
      "                14            8.0  0.951408  0.032967  0.903638  0.931167   \n",
      "                15            8.0  0.955261  0.038741  0.903243  0.932147   \n",
      "                16            8.0  0.954390  0.041219  0.894938  0.931676   \n",
      "                17            8.0  0.972157  0.041306  0.919457  0.944183   \n",
      "                18            8.0  0.977917  0.041060  0.917480  0.953458   \n",
      "                21            8.0  0.981014  0.045209  0.931321  0.951787   \n",
      "                23            8.0  0.986286  0.048874  0.926971  0.951612   \n",
      "                24            8.0  1.001242  0.049879  0.937253  0.969638   \n",
      "                25            8.0  1.012115  0.053624  0.953537  0.984158   \n",
      "                28            8.0  1.017061  0.055068  0.964935  0.976195   \n",
      "                29            8.0  1.016807  0.054572  0.962958  0.980689   \n",
      "\n",
      "                                 50%       75%       max  \n",
      "condition      session_day                                \n",
      "Citric Acid    -7           1.004395  1.008304  1.016221  \n",
      "               -6           0.984764  0.990170  1.055403  \n",
      "               -5           0.999281  0.999829  1.002660  \n",
      "               -4           1.002222  1.004395  1.006827  \n",
      "               -3           1.024390  1.031960  1.031963  \n",
      "                0           1.007349  1.008637  1.024981  \n",
      "                1           0.981029  0.998914  1.003581  \n",
      "                2           0.976133  1.011482  1.025700  \n",
      "                3           0.960520  0.974244  1.013693  \n",
      "                4           0.949239  0.961210  1.007373  \n",
      "                7           0.922919  0.966796  1.007373  \n",
      "                8           0.924628  0.987742  1.006109  \n",
      "                9           0.914374  0.984019  1.011165  \n",
      "                10          0.913751  0.973778  1.021277  \n",
      "                11          0.945415  1.005896  1.030756  \n",
      "                14          0.896599  0.924438  1.005477  \n",
      "                15          0.904728  0.924628  1.009269  \n",
      "                16          0.923952  0.931807  1.052875  \n",
      "                17          0.918852  0.947530  1.056035  \n",
      "                18          0.904129  0.941036  1.039604  \n",
      "                21          0.917792  0.953763  1.051611  \n",
      "                23          0.927021  0.995190  1.074363  \n",
      "                24          0.914032  0.986812  1.073099  \n",
      "                25          0.929756  0.977036  1.063619  \n",
      "                28          0.923261  0.948177  1.096482  \n",
      "                29          0.940430  0.955625  1.099642  \n",
      "Free water     -7           0.993749  0.994772  0.996584  \n",
      "               -6           0.998355  1.007379  1.010482  \n",
      "               -5           1.004977  1.011428  1.017570  \n",
      "               -4           0.991624  0.996914  1.004237  \n",
      "               -3           1.003854  1.020502  1.041314  \n",
      "                0           1.006507  1.029870  1.093750  \n",
      "                1           1.016503  1.033172  1.079979  \n",
      "                2           1.044943  1.050211  1.052966  \n",
      "                3           1.036054  1.049822  1.070445  \n",
      "                4           1.026385  1.046069  1.090572  \n",
      "                7           1.043197  1.064193  1.125000  \n",
      "                8           1.034867  1.057143  1.115996  \n",
      "                9           1.035820  1.058620  1.120763  \n",
      "                10          1.027341  1.056444  1.121822  \n",
      "                11          1.039153  1.069210  1.156780  \n",
      "                14          1.051807  1.097511  1.168962  \n",
      "                15          1.066224  1.104396  1.171610  \n",
      "                16          1.031603  1.089406  1.209746  \n",
      "                17          1.047370  1.103130  1.250000  \n",
      "                18          1.061763  1.100558  1.206038  \n",
      "                21          1.081324  1.123661  1.243644  \n",
      "                23          1.083790  1.125841  1.236759  \n",
      "                24          1.084914  1.150238  1.280720  \n",
      "                25          1.081107  1.140236  1.252119  \n",
      "                28          1.098825  1.151227  1.281250  \n",
      "                29          1.107592  1.153080  1.288665  \n",
      "Jax            -7           0.980465  0.981461  0.982456  \n",
      "                0           1.019535  1.020531  1.021526  \n",
      "                7           1.045216  1.049027  1.052838  \n",
      "                14          1.075909  1.080029  1.084149  \n",
      "                21          1.114430  1.122773  1.131115  \n",
      "                28          1.153500  1.163834  1.174168  \n",
      "                35          1.179181  1.192330  1.205479  \n",
      "                42          1.207368  1.222079  1.236791  \n",
      "                49          1.227178  1.241769  1.256360  \n",
      "Measured water -7           0.992212  0.994115  0.998184  \n",
      "               -6           0.983880  0.993553  1.007596  \n",
      "               -5           1.002330  1.010512  1.014531  \n",
      "               -4           0.999606  1.004765  1.007661  \n",
      "               -3           1.011769  1.021323  1.040359  \n",
      "                0           1.005388  1.023032  1.035903  \n",
      "                1           0.954295  0.958895  0.968848  \n",
      "                2           0.929763  0.950990  0.958645  \n",
      "                3           0.921018  0.938645  0.957648  \n",
      "                4           0.914288  0.932038  0.951938  \n",
      "                7           0.927582  0.936347  0.968848  \n",
      "                8           0.919748  0.947582  0.970676  \n",
      "                9           0.935088  0.959378  0.987128  \n",
      "                10          0.927091  0.947188  0.976617  \n",
      "                11          0.956696  0.973806  1.008607  \n",
      "                14          0.948206  0.968055  1.012263  \n",
      "                15          0.948490  0.976333  1.025973  \n",
      "                16          0.954847  0.970313  1.031914  \n",
      "                17          0.975052  0.988514  1.048823  \n",
      "                18          0.975878  0.999402  1.052936  \n",
      "                21          0.976251  0.996184  1.073958  \n",
      "                23          0.985587  1.003599  1.084012  \n",
      "                24          0.998850  1.017402  1.101379  \n",
      "                25          1.006375  1.025318  1.122858  \n",
      "                28          1.010757  1.032898  1.134740  \n",
      "                29          1.003407  1.032389  1.136111  \n"
     ]
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also\n",
    "    print(normalized2.groupby(['condition', 'session_day'])['avg_weight'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### PLOTS ########### \n",
    "palette = sns.xkcd_palette(['black', 'deep blue', 'azure', 'raspberry'])\n",
    "hue_order = ['Jax', 'Free water', 'Measured water', 'Citric Acid']\n",
    "\n",
    "f, ax = plt.subplots(ncols=3, nrows=1, gridspec_kw=dict(width_ratios=[3, 0.75, 1]), figsize=(9, 6))\n",
    "    \n",
    "# make black line thicker\n",
    "sns.lineplot(x=\"session_day\", y=\"avg_weight\", estimator=np.mean, err_style='bars', \n",
    "             style='condition', marker=None,\n",
    "             ci=None, color='k', data=normalized2[normalized2.condition == 'Jax'], \n",
    "             ax=ax[0], legend=False, linewidth=3)\n",
    "\n",
    "sns.lineplot(x=\"session_day\", y=\"avg_weight\", estimator=np.mean, err_style='bars', \n",
    "             style='condition', markers={'Jax':'.', 'Free water':'>', 'Measured water':'o', 'Citric Acid':'v'}, \n",
    "             dashes=False, markersize=7,\n",
    "             ci=68, hue=\"condition\", data=normalized2, ax=ax[0], legend=False, \n",
    "            palette=palette, hue_order=hue_order, linewidth=1.5,\n",
    "            err_kws={'elinewidth':1.5})\n",
    "ax[0].set_xlabel(\"Days from protocol start\")    \n",
    "ax[0].set_ylabel(\"Weight (normalized)\")         \n",
    "ax[0].set(ylim=[0.85, 1.22], yticks=[0.9, 1, 1.1, 1.2], xticks=[0, 10, 20, 30], xlim=[-10, 31])\n",
    "ax[0].text(-5, 1.14,'Expected growth\\nC57BL/6J', {'color':palette[0], 'fontsize':14})\n",
    "ax[0].text(-5, 1.09,'Free water', {'color':palette[1], 'fontsize':14})\n",
    "ax[0].text(-6, 0.88,'Measured water', {'color':palette[2], 'fontsize':14})\n",
    "#ax[0].text(-2, 0.81,'40 $\\mu$L/g/day', {'color':palette[2], 'fontsize':16})\n",
    "ax[0].text(14, 0.87,'Free CA water', {'color':palette[3], 'fontsize':14})\n",
    "ax[0].annotate('1% to 2%', xy=(11.5, 0.97), xytext=(12, 1), color=palette[3], fontsize=12,\n",
    "              arrowprops=dict(facecolor='grey', shrink=0, headlength=7))\n",
    "#ax[0].text(-2, 1.17, 'Cohort 1\\nn = 17\\n2-3 months', {'color':'k', 'fontsize':16})\n",
    "ax[0].set_title('Cohort 1, n = 17\\n(2-3 months old)', fontsize=16, fontstyle='italic')\n",
    "\n",
    "# add measurement days for (c) into this plot\n",
    "ax[0].axvspan(15.5, 16.5, 0.1, 0.94, fc='gainsboro')\n",
    "ax[0].axvspan(23.5, 24.5, 0.1, 0.94, fc='gainsboro')\n",
    "ax[0].axvspan(28.5, 29.5, 0.1, 0.94, fc='gainsboro')\n",
    "\n",
    "########### WATER ADMINISTRATIONS ########### \n",
    "water_with_date = (action.WaterAdministration & subj).proj('watertype_name', 'water_administered', 'adlib', # it can restrict on the subj here but it will anyway do later\n",
    "                                                  session_date='DATE(administration_time)')\n",
    "water_date = (dj.U('subject_uuid', 'session_date') & water_with_date) & 'session_date < \"2018-10-24\"'& 'session_date > \"2018-10-05\"'\n",
    "total_water_date = water_date.aggr(water_with_date, total_water='SUM(water_administered)', \n",
    "                                   watertype=\"GROUP_CONCAT(DISTINCT watertype_name SEPARATOR '; ')\", \n",
    "                                   adlib='MAX(adlib)')\n",
    "b = subj * total_water_date # including water will exclude mice with ad lib water\n",
    "df = pd.DataFrame(b.fetch(as_dict=True))\n",
    "\n",
    "########### CONDITIONS ########### \n",
    "df['condition'] = df.subject_nickname.map(condition_map)\n",
    "\n",
    "########### b ########### \n",
    "sns.lineplot(x=\"condition\", y=\"total_water\", style='condition', markers={'Free water':'>', 'Measured water':'o', 'Citric Acid': 'v'}, \n",
    "             ci=None, units='subject_uuid', hue_order=['Free water', 'Citric Acid'], palette=sns.xkcd_palette(['deep blue', 'raspberry']),\n",
    "             hue=\"condition\", \n",
    "             data=df.groupby(['condition', 'subject_uuid'])['total_water'].mean().reset_index(),\n",
    "             estimator=None, legend=False, dashes=None, markersize=7,\n",
    "             ax=ax[1], zorder=100)\n",
    "\n",
    "sns.boxplot(x=\"condition\", y=\"total_water\", order=[ 'Free water', 'Citric Acid'], \n",
    "            data=df.groupby(['subject_uuid', 'condition'])['total_water'].mean().reset_index(), \n",
    "            fliersize=0, boxprops={'facecolor':'None'}, width=.5, ax=ax[1],\n",
    "           zorder=-100)\n",
    "ax[1].set(ylabel='Water drank  (mL/day)\\nin home cage', xlabel='')\n",
    "ax[1].set_xticklabels(['Free water', 'Free CA water'])\n",
    "ax[1].set_yticks([0, 2, 4, 6])\n",
    "\n",
    "########### WATER ########### \n",
    "water= pd.read_csv('data/figure1b_ccu_thirst.csv') # first date is exclude (habituation)\n",
    "# remove measurements from 4-10-2018, CA mice still on 1%\n",
    "water = water[~water['date'].str.match('4-10-2018')]\n",
    "water['condition'] = water.subject.map(condition_map)\n",
    "\n",
    "########### c ########### \n",
    "sns.lineplot(x=\"condition\", y=\"water_amount\", style='condition', markers={'Free water':'>', 'Measured water':'o', 'Citric Acid':'v'}, \n",
    "             ci=None, units='subject', hue_order=hue_order, palette=palette, hue=\"condition\", \n",
    "             data=water.groupby(['condition', 'subject'])['water_amount'].mean().reset_index(),\n",
    "             estimator=None, legend=False, dashes=None, markersize=7,\n",
    "             ax=ax[2], zorder=100)\n",
    "sns.boxplot(x=\"condition\", y=\"water_amount\", order=hue_order[1:], \n",
    "            data=water.groupby(['condition', 'subject'])['water_amount'].mean().reset_index(),\n",
    "            fliersize=0, boxprops={'facecolor':'None'}, width=.5, ax=ax[2], zorder=-100)\n",
    "ax[2].set(ylabel='Plain water drank (mL)\\nin 5 minutes', xlabel='')\n",
    "ax[2].set_xticklabels(['Free water', 'Measured water', 'Free CA water'])\n",
    "ax[2].set_yticks([0, 0.5, 1, 1.5])\n",
    "\n",
    "# layout with rotated ticks\n",
    "for ticklabel, tickcolor in zip(ax[1].get_xticklabels(), [palette[1], palette[3]]):\n",
    "    ticklabel.set_color(tickcolor)\n",
    "    ticklabel.set_rotation(50)\n",
    "    ticklabel.set(ha='right')\n",
    "    \n",
    "for ticklabel, tickcolor in zip(ax[2].get_xticklabels(), [palette[1], palette[2], palette[3]]):\n",
    "    ticklabel.set_color(tickcolor)\n",
    "    ticklabel.set_rotation(50)\n",
    "    ticklabel.set(ha='right')\n",
    "    \n",
    "f.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "sns.despine(trim=True)\n",
    "f.savefig(os.path.join(figpath, \"citricacid_study1.pdf\"))\n",
    "f.savefig(os.path.join(figpath, \"citricacid_study1.png\"), dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">water_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Citric Acid</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.795333</td>\n",
       "      <td>0.165875</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>0.986667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free water</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>0.042284</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.095833</td>\n",
       "      <td>0.113333</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Measured water</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.011250</td>\n",
       "      <td>0.226624</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.955833</td>\n",
       "      <td>1.021667</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>1.406667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               water_amount                                                    \\\n",
       "                      count      mean       std       min       25%       50%   \n",
       "condition                                                                       \n",
       "Citric Acid             5.0  0.795333  0.165875  0.583333  0.666667  0.850000   \n",
       "Free water              4.0  0.122500  0.042284  0.083333  0.095833  0.113333   \n",
       "Measured water          8.0  1.011250  0.226624  0.590000  0.955833  1.021667   \n",
       "\n",
       "                                    \n",
       "                     75%       max  \n",
       "condition                           \n",
       "Citric Acid     0.890000  0.986667  \n",
       "Free water      0.140000  0.180000  \n",
       "Measured water  1.083333  1.406667  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water.groupby(['condition', 'subject'])['water_amount'].mean().reset_index().groupby(['condition']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>subject_uuid</th>\n",
       "      <th>subject_project</th>\n",
       "      <th>session_date</th>\n",
       "      <th>subject_nickname</th>\n",
       "      <th>sex</th>\n",
       "      <th>subject_birth_date</th>\n",
       "      <th>ear_mark</th>\n",
       "      <th>subject_line</th>\n",
       "      <th>subject_source</th>\n",
       "      <th>protocol_number</th>\n",
       "      <th>subject_description</th>\n",
       "      <th>subject_ts</th>\n",
       "      <th>subject_strain</th>\n",
       "      <th>lab_name</th>\n",
       "      <th>subjectlab_ts</th>\n",
       "      <th>subjectproject_ts</th>\n",
       "      <th>total_water</th>\n",
       "      <th>watertype</th>\n",
       "      <th>adlib</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Water 1% Citric Acid</td>\n",
       "      <td>1</td>\n",
       "      <td>Citric Acid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Water 1% Citric Acid</td>\n",
       "      <td>1</td>\n",
       "      <td>Citric Acid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Water 1% Citric Acid</td>\n",
       "      <td>1</td>\n",
       "      <td>Citric Acid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>Water 1% Citric Acid</td>\n",
       "      <td>1</td>\n",
       "      <td>Citric Acid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0124f697-16ce-4f59-b87c-e53fcb3a27ac</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>6867</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:25</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:33</td>\n",
       "      <td>2020-09-18 14:09:13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Water 1% Citric Acid</td>\n",
       "      <td>1</td>\n",
       "      <td>Citric Acid</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>f8f2ec95-bc15-4c72-bf93-74185cbd049f</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-16</td>\n",
       "      <td>ZM_338</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:41</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:52</td>\n",
       "      <td>2020-09-18 14:09:31</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>Water</td>\n",
       "      <td>0</td>\n",
       "      <td>Measured water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>f8f2ec95-bc15-4c72-bf93-74185cbd049f</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>ZM_338</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:41</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:52</td>\n",
       "      <td>2020-09-18 14:09:31</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>Water</td>\n",
       "      <td>0</td>\n",
       "      <td>Measured water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>f8f2ec95-bc15-4c72-bf93-74185cbd049f</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>ZM_338</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:41</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:52</td>\n",
       "      <td>2020-09-18 14:09:31</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>Water</td>\n",
       "      <td>0</td>\n",
       "      <td>Measured water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>f8f2ec95-bc15-4c72-bf93-74185cbd049f</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>ZM_338</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:41</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:52</td>\n",
       "      <td>2020-09-18 14:09:31</td>\n",
       "      <td>1.550000</td>\n",
       "      <td>Water</td>\n",
       "      <td>0</td>\n",
       "      <td>Measured water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>f8f2ec95-bc15-4c72-bf93-74185cbd049f</td>\n",
       "      <td>collab_citricacid</td>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>ZM_338</td>\n",
       "      <td>M</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>None</td>\n",
       "      <td>Thy1-GCaMP6s</td>\n",
       "      <td>CCU - Margarida colonies</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-09-25 01:33:41</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>mainenlab</td>\n",
       "      <td>2020-09-18 14:09:52</td>\n",
       "      <td>2020-09-18 14:09:31</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>Water</td>\n",
       "      <td>0</td>\n",
       "      <td>Measured water</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             subject_uuid    subject_project session_date  \\\n",
       "0    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-09-26   \n",
       "1    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-09-27   \n",
       "2    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-09-28   \n",
       "3    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-10-01   \n",
       "4    0124f697-16ce-4f59-b87c-e53fcb3a27ac  collab_citricacid   2018-10-02   \n",
       "..                                    ...                ...          ...   \n",
       "408  f8f2ec95-bc15-4c72-bf93-74185cbd049f  collab_citricacid   2018-10-16   \n",
       "409  f8f2ec95-bc15-4c72-bf93-74185cbd049f  collab_citricacid   2018-10-17   \n",
       "410  f8f2ec95-bc15-4c72-bf93-74185cbd049f  collab_citricacid   2018-10-19   \n",
       "411  f8f2ec95-bc15-4c72-bf93-74185cbd049f  collab_citricacid   2018-10-20   \n",
       "412  f8f2ec95-bc15-4c72-bf93-74185cbd049f  collab_citricacid   2018-10-22   \n",
       "\n",
       "    subject_nickname sex subject_birth_date ear_mark  subject_line  \\\n",
       "0               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "1               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "2               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "3               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "4               6867   M         2018-06-25     None  Thy1-GCaMP6s   \n",
       "..               ...  ..                ...      ...           ...   \n",
       "408           ZM_338   M         2018-08-08     None  Thy1-GCaMP6s   \n",
       "409           ZM_338   M         2018-08-08     None  Thy1-GCaMP6s   \n",
       "410           ZM_338   M         2018-08-08     None  Thy1-GCaMP6s   \n",
       "411           ZM_338   M         2018-08-08     None  Thy1-GCaMP6s   \n",
       "412           ZM_338   M         2018-08-08     None  Thy1-GCaMP6s   \n",
       "\n",
       "               subject_source  protocol_number subject_description  \\\n",
       "0    CCU - Margarida colonies                1                None   \n",
       "1    CCU - Margarida colonies                1                None   \n",
       "2    CCU - Margarida colonies                1                None   \n",
       "3    CCU - Margarida colonies                1                None   \n",
       "4    CCU - Margarida colonies                1                None   \n",
       "..                        ...              ...                 ...   \n",
       "408  CCU - Margarida colonies                1                None   \n",
       "409  CCU - Margarida colonies                1                None   \n",
       "410  CCU - Margarida colonies                1                None   \n",
       "411  CCU - Margarida colonies                1                None   \n",
       "412  CCU - Margarida colonies                1                None   \n",
       "\n",
       "             subject_ts subject_strain   lab_name       subjectlab_ts  \\\n",
       "0   2019-09-25 01:33:25       C57BL/6J  mainenlab 2020-09-18 14:09:33   \n",
       "1   2019-09-25 01:33:25       C57BL/6J  mainenlab 2020-09-18 14:09:33   \n",
       "2   2019-09-25 01:33:25       C57BL/6J  mainenlab 2020-09-18 14:09:33   \n",
       "3   2019-09-25 01:33:25       C57BL/6J  mainenlab 2020-09-18 14:09:33   \n",
       "4   2019-09-25 01:33:25       C57BL/6J  mainenlab 2020-09-18 14:09:33   \n",
       "..                  ...            ...        ...                 ...   \n",
       "408 2019-09-25 01:33:41       C57BL/6J  mainenlab 2020-09-18 14:09:52   \n",
       "409 2019-09-25 01:33:41       C57BL/6J  mainenlab 2020-09-18 14:09:52   \n",
       "410 2019-09-25 01:33:41       C57BL/6J  mainenlab 2020-09-18 14:09:52   \n",
       "411 2019-09-25 01:33:41       C57BL/6J  mainenlab 2020-09-18 14:09:52   \n",
       "412 2019-09-25 01:33:41       C57BL/6J  mainenlab 2020-09-18 14:09:52   \n",
       "\n",
       "      subjectproject_ts  total_water             watertype  adlib  \\\n",
       "0   2020-09-18 14:09:13     3.000000  Water 1% Citric Acid      1   \n",
       "1   2020-09-18 14:09:13     1.000000  Water 1% Citric Acid      1   \n",
       "2   2020-09-18 14:09:13     1.000000  Water 1% Citric Acid      1   \n",
       "3   2020-09-18 14:09:13     0.666667  Water 1% Citric Acid      1   \n",
       "4   2020-09-18 14:09:13     1.000000  Water 1% Citric Acid      1   \n",
       "..                  ...          ...                   ...    ...   \n",
       "408 2020-09-18 14:09:31     0.730000                 Water      0   \n",
       "409 2020-09-18 14:09:31     0.750000                 Water      0   \n",
       "410 2020-09-18 14:09:31     0.770000                 Water      0   \n",
       "411 2020-09-18 14:09:31     1.550000                 Water      0   \n",
       "412 2020-09-18 14:09:31     0.770000                 Water      0   \n",
       "\n",
       "          condition  \n",
       "0       Citric Acid  \n",
       "1       Citric Acid  \n",
       "2       Citric Acid  \n",
       "3       Citric Acid  \n",
       "4       Citric Acid  \n",
       "..              ...  \n",
       "408  Measured water  \n",
       "409  Measured water  \n",
       "410  Measured water  \n",
       "411  Measured water  \n",
       "412  Measured water  \n",
       "\n",
       "[413 rows x 20 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOW DO THE SAME FOR WATER intake\n",
    "water_with_date = action.WaterAdministration.proj('watertype_name', 'water_administered', 'adlib',\n",
    "                                                  session_date='DATE(administration_time)')\n",
    "water_date = (dj.U('subject_uuid', 'session_date') & water_with_date)\n",
    "# total_water_date = water_date.aggr(water_with_date, total_water='SUM(water_administered)', \n",
    "#                                    watertype=\"CONCAT_WS('; ', watertype_name)\")\n",
    "total_water_date = water_date.aggr(water_with_date, total_water='SUM(water_administered)', \n",
    "                                   watertype=\"GROUP_CONCAT(DISTINCT watertype_name SEPARATOR '; ')\", \n",
    "                                   adlib='MAX(adlib)')\n",
    "water_df = subj * total_water_date\n",
    "water_df = pd.DataFrame(water_df.fetch(as_dict=True))\n",
    "water_df['condition'] = water_df.subject_nickname.map(condition_map)\n",
    "water_df\n",
    "\n",
    "#water_df.groupby(['condition'])['total_water'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protocol_number</th>\n",
       "      <th>total_water</th>\n",
       "      <th>adlib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>160.0</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.842625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.346418</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       protocol_number  total_water  adlib\n",
       "count            160.0   160.000000  160.0\n",
       "mean               1.0     0.842625    0.0\n",
       "std                0.0     0.346418    0.0\n",
       "min                1.0     0.600000    0.0\n",
       "25%                1.0     0.600000    0.0\n",
       "50%                1.0     0.730000    0.0\n",
       "75%                1.0     1.000000    0.0\n",
       "max                1.0     2.300000    0.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water_measured = water_df.loc[water_df.condition == 'Measured water', :]\n",
    "#sns.distplot(water_measured.total_water, kde=False, rug=True)\n",
    "water_measured.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reviewer question: please state the maximum weight loss at any time point, and the stabilized weight loss as percentage of the growth-curve-corrected expected weight within animal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# what's the expected curve without intervention?\n",
    "normalized2.groupby(['condition', 'sex', 'session_day'])['avg_weight'].mean()\n",
    "with sns.axes_style(\"darkgrid\"):\n",
    "    sns.lineplot(data=normalized2, x='session_day', y='avg_weight', estimator=None, units='subject_nickname',\n",
    "            hue='condition', style='sex', markers=True)\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days</th>\n",
       "      <th>Weeks</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>10.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>10.757143</td>\n",
       "      <td>11.442857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>11.414286</td>\n",
       "      <td>12.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>12.071429</td>\n",
       "      <td>13.128571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>12.728571</td>\n",
       "      <td>13.971429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>149</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>34.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>150</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>34.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>151</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>34.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>152</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>34.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>153</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>34.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     days      Weeks    Females      Males\n",
       "0      21   3.000000  10.100000  10.600000\n",
       "1      22   3.142857  10.757143  11.442857\n",
       "2      23   3.285714  11.414286  12.285714\n",
       "3      24   3.428571  12.071429  13.128571\n",
       "4      25   3.571429  12.728571  13.971429\n",
       "..    ...        ...        ...        ...\n",
       "128   149  21.000000  25.800000  34.600000\n",
       "129   150  21.000000  25.800000  34.600000\n",
       "130   151  21.000000  25.800000  34.600000\n",
       "131   152  21.000000  25.800000  34.600000\n",
       "132   153  21.000000  25.800000  34.600000\n",
       "\n",
       "[133 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# INTERPOLATE JAX WEIGHTS PER DAY\n",
    "jax_weights = pd.read_csv('data/jax_mouse_weights.csv')\n",
    "jax_weights['days'] = jax_weights['Weeks'] * 7\n",
    "jax_weights = jax_weights.set_index('days')\n",
    "jax_weights = jax_weights.loc[jax_weights.Weeks < 23, :]\n",
    "# # https://stackoverflow.com/questions/34693079/python-pandas-dataframe-interpolate-missing-data\n",
    "jax_weights = jax_weights.reindex(np.arange(21, 154), fill_value=\"NaN\")\n",
    "jax_weights = jax_weights.astype(float)\n",
    "jax_weights = jax_weights.interpolate(method='linear', axis=0).bfill().reset_index()\n",
    "jax_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# COMPUTE THE EXPECTED GROWTH CURVE BY INTERPOLATING JAX DATA \n",
    "normalized_growth = pd.DataFrame([])\n",
    "for index, sjdat in normalized2[['subject_nickname', 'sex', 'avg_weight', \n",
    "                                 'session_day', 'condition', 'session_age']].sort_values(by=['session_day']).groupby(['subject_nickname']):\n",
    "    \n",
    "    if sjdat['subject_nickname'].unique().item() in ['Jax_female', 'Jax_male']:\n",
    "        continue\n",
    "        \n",
    "    # GRAB THE APPROPIATE SEX FROM JAX DATA\n",
    "    age_days_atstart = sjdat.session_age.unique().item().days\n",
    "    jax_dat = jax_weights.copy()\n",
    "    jax_dat['session_day'] = jax_dat.days - age_days_atstart\n",
    "    sjdat2 = sjdat.merge(jax_dat, on='session_day')\n",
    "    \n",
    "    # express Jax timecourses as growth relative to day 0\n",
    "    sjdat2['Females'] = sjdat2['Females'] / sjdat2.loc[sjdat2.session_day < 0, 'Females'].mean()\n",
    "    sjdat2['Males'] = sjdat2['Males'] / sjdat2.loc[sjdat2.session_day < 0, 'Males'].mean()\n",
    "\n",
    "    if sjdat['sex'].unique() == 'M':\n",
    "            sjdat2['growth_corrected_weight'] = sjdat2['avg_weight'] / sjdat2['Males']\n",
    "    elif sjdat['sex'].unique() == 'F':\n",
    "            sjdat2['growth_corrected_weight'] = sjdat2['avg_weight'] / sjdat2['Females']\n",
    "\n",
    "    normalized_growth = normalized_growth.append(sjdat2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot these growth-corrected weights per animal\n",
    "with sns.axes_style(\"darkgrid\"):\n",
    "    sns.lineplot(data=normalized_growth, x='session_day', y='growth_corrected_weight', estimator=None, units='subject_nickname',\n",
    "            hue='condition', style='sex', markers=True)\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x324 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = sns.xkcd_palette(['deep blue', 'azure', 'raspberry'])\n",
    "hue_order = ['Free water', 'Measured water', 'Citric Acid']\n",
    "\n",
    "f, ax = plt.subplots(ncols=2, nrows=1, figsize=(7, 4.5), sharey=True, sharex=True)\n",
    "sns.set(style=\"ticks\", context=\"talk\")\n",
    "\n",
    "sns.lineplot(x=\"session_day\", y=\"growth_corrected_weight\", estimator=np.mean, err_style='bars', \n",
    "             style='condition', markers={'Jax':'.', 'Free water':'>', 'Measured water':'o', 'Citric Acid':'v'}, \n",
    "             dashes=False, markersize=7,\n",
    "             ci=68, hue=\"condition\", data=normalized_growth, ax=ax[0], legend=False, \n",
    "            palette=palette, hue_order=hue_order, linewidth=1.5,\n",
    "            err_kws={'elinewidth':1.5})\n",
    "ax[0].set_xlabel(\"Days from protocol start\")    \n",
    "ax[0].set_ylabel(\"Weight change\\n(growth-corrected)\")         \n",
    "ax[0].set(ylim=[0.78, 1.05], yticks=[0.8, 0.9, 1], xticks=[0, 10, 20, 30], xlim=[-10, 31])\n",
    "# ax[0].text(-5, 1.14,'Expected growth\\nC57BL/6J', {'color':palette[0], 'fontsize':14})\n",
    "ax[0].text(20, 1.04,'Free water', {'color':palette[0], 'fontsize':14})\n",
    "ax[0].text(-5, 0.79,'Measured\\nwater', {'color':palette[1], 'fontsize':14})\n",
    "#ax[0].text(-2, 0.81,'40 $\\mu$L/g/day', {'color':palette[2], 'fontsize':16})\n",
    "ax[0].text(10, 0.75,'Free CA water', {'color':palette[2], 'fontsize':14})\n",
    "ax[0].annotate('1% to 2%', xy=(11.5, 0.91), xytext=(14, 0.91), color=palette[2], fontsize=10,\n",
    "              arrowprops=dict(facecolor='grey', shrink=0, headlength=7))\n",
    "ax[0].set_title('Cohort 1, n = 17\\n(2-3 months old)', fontsize=16, fontstyle='italic')\n",
    "\n",
    "# make black line thicker\n",
    "sns.lineplot(x=\"session_day\", y=\"growth_corrected_weight\", estimator=None, units='subject_nickname', err_style='bars', \n",
    "             style='condition', markers={'Jax':'.', 'Free water':'>', 'Measured water':'o', 'Citric Acid':'v'}, \n",
    "             dashes=False, markersize=5, alpha=0.4, \n",
    "             ci=68, hue=\"condition\", data=normalized_growth, ax=ax[1], legend=False, \n",
    "            palette=palette, hue_order=hue_order, linewidth=1.5,\n",
    "            err_kws={'elinewidth':1.5})\n",
    "ax[1].set_xlabel(\"Days from protocol start\")    \n",
    "ax[1].set_ylabel(\"\")         \n",
    "ax[1].set(ylim=[0.71, 1.15], yticks=[0.7, 0.8, 0.9, 1, 1.1], xticks=[0, 10, 20, 30], xlim=[-10, 31])\n",
    "\n",
    "sns.despine(trim=True)\n",
    "f.tight_layout()\n",
    "f.savefig(os.path.join(figpath, \"ccu_weights_growthcorrected.pdf\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "condition       subject_nickname\n",
       "Citric Acid     6867                0.79\n",
       "                6869                0.82\n",
       "                6872                0.83\n",
       "                ZM_209              0.77\n",
       "                ZM_342              0.92\n",
       "Free water      6873                0.96\n",
       "                ZM_200              0.91\n",
       "                ZM_205              0.91\n",
       "                ZM_335              0.95\n",
       "Measured water  6870                0.85\n",
       "                ZM_202              0.84\n",
       "                ZM_203              0.89\n",
       "                ZM_206              0.80\n",
       "                ZM_208              0.82\n",
       "                ZM_210              0.88\n",
       "                ZM_337              0.85\n",
       "                ZM_338              0.81\n",
       "Name: growth_corrected_weight, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. what's the maximum weight loss at any time point?\n",
    "normalized_growth.groupby(['condition', 'subject_nickname'])['growth_corrected_weight'].min().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "condition       subject_nickname\n",
       "Citric Acid     6867                0.79\n",
       "                6869                0.83\n",
       "                6872                0.89\n",
       "                ZM_209              0.78\n",
       "                ZM_342              0.95\n",
       "Free water      6873                0.97\n",
       "                ZM_200              0.93\n",
       "                ZM_205              0.94\n",
       "                ZM_335              1.02\n",
       "Measured water  6870                0.86\n",
       "                ZM_202              0.87\n",
       "                ZM_203              0.90\n",
       "                ZM_206              0.81\n",
       "                ZM_208              0.84\n",
       "                ZM_210              0.90\n",
       "                ZM_337              0.90\n",
       "                ZM_338              0.83\n",
       "Name: growth_corrected_weight, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. what's the stable weight?\n",
    "normalized_growth.loc[normalized_growth.session_day > 20, :].groupby(['condition', 'subject_nickname'])['growth_corrected_weight'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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