AIC_FP_Analysis_Review.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a511971a-1202-4217-a90a-9e25f51a0b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "plt.rcParams['font.sans-serif'] = 'Helvetica'\n",
    "import seaborn as sns\n",
    "sns.set_context(\"paper\")\n",
    "import os \n",
    "import glob\n",
    "import statsmodels.api as sm\n",
    "from pathlib import Path \n",
    "from scipy import stats\n",
    "import pingouin as pg\n",
    "import scipy\n",
    "\n",
    "#some helper fxn's for formatting\n",
    "from math import floor, log10\n",
    "\n",
    "def sig_figs(x: float, precision: int):\n",
    "    \"\"\"\n",
    "    Rounds a number to number of significant figures\n",
    "    Parameters:\n",
    "    - x - the number to be rounded\n",
    "    - precision (integer) - the number of significant figures\n",
    "    Returns:\n",
    "    - float\n",
    "    \"\"\"\n",
    "\n",
    "    x = float(x)\n",
    "    precision = int(precision)\n",
    "\n",
    "    return round(x, -int(floor(log10(abs(x)))) + (precision - 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082ea879",
   "metadata": {},
   "source": [
    "# DID Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d77e538-b73c-4543-9ce5-123101cccddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_to_open = os.path.join(os.getcwd(), 'DrinkingData.xlsx')\n",
    "print(file_to_open)\n",
    "\n",
    "DID = pd.read_excel(file_to_open)\n",
    "\n",
    "#calc intakes\n",
    "DID.loc[DID['Fluid'] == \"Alcohol\", \"Intake\"] = (((DID['InitialWeight']-DID['FinalWeight'])/0.97336)*0.789*0.2)/(DID['Weight']*0.001)\n",
    "DID.loc[DID['Fluid'] == \"Water\", \"Intake\"] = (((DID['InitialWeight']-DID['FinalWeight'])/1)*1)/(DID['Weight']*0.001)\n",
    "\n",
    "#calc water intake from previous 24 hours\n",
    "DID['Water_Intake'] = (((DID['WaterBottleWeightStart']-DID['WaterBottleWeightStop'])/1)*1)/(DID['Weight']*0.001)\n",
    "\n",
    "#Add DID Week\n",
    "DID.loc[DID['Session'] <= 0, \"DIDWeek\"] = 0\n",
    "DID.loc[(DID['Session'] >= 1) & (DID['Session'] <= 5), \"DIDWeek\"] = 1\n",
    "DID.loc[(DID['Session'] >= 6) & (DID['Session'] <= 10), \"DIDWeek\"] = 2\n",
    "DID.loc[(DID['Session'] >= 11) & (DID['Session'] <= 15), \"DIDWeek\"] = 3\n",
    "DID.loc[(DID['Session'] >= 16) & (DID['Session'] <= 20), \"DIDWeek\"] = 4\n",
    "DID.loc[(DID['Session'] >= 21) & (DID['Session'] <= 25), \"DIDWeek\"] = 5\n",
    "DID.loc[(DID['Session'] >= 26) & (DID['Session'] <= 30), \"DIDWeek\"] = 6\n",
    "\n",
    "#Add Cohort Data\n",
    "DID.loc[DID['ID'] == \"P1\", \"Cohort\"] = 1\n",
    "DID.loc[DID['ID'] == \"P2\", \"Cohort\"] = 1\n",
    "DID.loc[DID['ID'] == \"P3\", \"Cohort\"] = 1\n",
    "DID.loc[DID['ID'] == \"P4\", \"Cohort\"] = 1\n",
    "\n",
    "DID.loc[DID['ID'] == \"P5\", \"Cohort\"] = 2\n",
    "DID.loc[DID['ID'] == \"P6\", \"Cohort\"] = 2\n",
    "DID.loc[DID['ID'] == \"P7\", \"Cohort\"] = 2\n",
    "DID.loc[DID['ID'] == \"P8\", \"Cohort\"] = 2\n",
    "DID.loc[DID['ID'] == \"P9\", \"Cohort\"] = 2\n",
    "\n",
    "DID.loc[DID['ID'] == \"P10\", \"Cohort\"] = 3\n",
    "DID.loc[DID['ID'] == \"P11\", \"Cohort\"] = 3\n",
    "DID.loc[DID['ID'] == \"P12\", \"Cohort\"] = 3\n",
    "DID.loc[DID['ID'] == \"P13\", \"Cohort\"] = 3\n",
    "DID.loc[DID['ID'] == \"P14\", \"Cohort\"] = 3\n",
    "\n",
    "for i in DID['ID'].unique():\n",
    "    DID.loc[DID['ID'] == i, 'Water_Intake'] = DID.loc[DID['ID'] == i, 'Water_Intake'].shift()\n",
    "    DID.loc[DID['ID'] == i, 'Date_Parsed'] = pd.to_datetime(DID.loc[DID['ID'] == i, 'Date'])\n",
    "    DID.loc[DID['ID'] == i, 'Date_Diff'] = DID.loc[DID['ID'] == i, 'Date_Parsed'].diff()\n",
    "\n",
    "DID.loc[(DID['Session'] == 15) & (DID['Cohort'] == 1), \"Water_Intake\"] = np.nan ##Water Bottle tops chewed off so evaporation error\n",
    "\n",
    "DID['Previous_Water'] = DID['Water_Intake']/(DID['Date_Diff'].dt.days)\n",
    "\n",
    "DID.loc[DID['Previous_Water'] > 275, 'Previous_Water'] = np.nan #filter out leaks\n",
    "\n",
    "#Clean up raw data columns\n",
    "DID = DID.drop(['InitialWeight'], axis = 1)\n",
    "DID = DID.drop(['FinalWeight'], axis = 1)\n",
    "\n",
    "#remove datapoints in notes\n",
    "DID.loc[(DID['ID'] == \"P14\") & (DID['Session'] == 19), 'Intake'] = np.nan\n",
    "DID.loc[(DID['ID'] == \"P14\") & (DID['Session'] == 20), 'Intake'] = np.nan\n",
    "\n",
    "DID.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0dc4f45e-784e-4db5-8a2d-66fe9da2d192",
   "metadata": {},
   "source": [
    "# Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "178c045c-33c2-4a9c-a3dc-e100498ed882",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(12,8))\n",
    "sns.despine()\n",
    "\n",
    "sns.lineplot(data = DID, x = 'Session', y = \"Weight\", legend=False, errorbar='se')\n",
    "sns.lineplot(data = DID, x = 'Session', y = \"Weight\", hue = \"ID\", legend=False, alpha = 0.5)\n",
    "\n",
    "plt.ylabel('Mouse Weight (g)')\n",
    "plt.xlabel('Drinking Session')\n",
    "\n",
    "plt.ylim(18,32)\n",
    "\n",
    "plt.savefig('Weights.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b3ea529-06d3-4dd0-80a6-1de536bfcc2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(12,8))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[DID['Session'] > 0]\n",
    "\n",
    "sns.lineplot(data = data, x = 'Session', y = \"Weight\", hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], palette=['#023047', '#FB8500'])\n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:], frameon=False, loc = 'upper left')\n",
    "\n",
    "plt.ylabel('Mouse Weight (g)')\n",
    "plt.xlabel('Drinking Session')\n",
    "\n",
    "plt.ylim(18,32)\n",
    "\n",
    "plt.savefig('Weight_Sex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=\"Weight\", within='Session', between='Sex', subject='ID', data=data).round(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9af60d6c-4a33-4b05-adb9-f97d414e854c",
   "metadata": {},
   "source": [
    "# Intakes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1e8b7740-8c43-4b88-871f-33c65fdfd6d7",
   "metadata": {},
   "source": [
    "### Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "035a2729-8cfd-4679-8c52-286520d595ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(24,8))\n",
    "sns.despine()\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\")]\n",
    "\n",
    "sns.pointplot(data = data, x = 'Session', y = var, hue = \"ID\", errorbar = 'se')\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)')\n",
    "plt.xlabel('Drinking Session')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 2)\n",
    "\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=\"Intake\", within='Session', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44997ab9-8552-4745-b7cd-98b96ea92a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(12,8))\n",
    "sns.despine()\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\")]\n",
    "\n",
    "sns.stripplot(data = data, x = 'Session', y = var, hue = \"ID\", jitter = True, edgecolor='gray', alpha =0.5)\n",
    "sns.pointplot(data = data, x = 'Session', y = var, hue = \"Fluid\", errorbar = 'se')\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)')\n",
    "plt.xlabel('Drinking Session')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 2)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "plt.savefig('Water_Intakes_ALL.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "#pg.rm_anova(dv=\"Intake\", within='Session', subject='ID', data=data, effsize=\"ng2\").round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bada9908-1d49-4411-a3fb-19dcedfc37b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(12,8))\n",
    "sns.despine()\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\")]\n",
    "\n",
    "sns.stripplot(data = data, x = 'Session', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5)\n",
    "sns.pointplot(data = data, x = 'Session', y = var, hue = \"Sex\", errorbar = 'se')\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)')\n",
    "plt.xlabel('Drinking Session')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 2)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "plt.savefig('Water_Intakes_ALL_SEX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=\"Intake\", within='Session', between='Sex', subject='ID', data=data, effsize=\"ng2\").round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aedcffb3-2659-4aee-ab70-b9ffe4b22344",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(12,8))\n",
    "sns.despine()\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\")]\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"ID\", jitter = True, edgecolor='gray', alpha =0.5, linewidth=0)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Fluid\", errorbar= 'se', estimator= np.mean)\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)')\n",
    "plt.xlabel('Drinking Week')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper right')\n",
    "\n",
    "plt.ylim([0,100])\n",
    "\n",
    "plt.savefig('WaterxWeek_ALL.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=\"Intake\", within='DIDWeek', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b158567b-4ef3-41d2-8e31-2e751e45b5ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=\"Intake\", within='DIDWeek', subject='ID', padjust='sidak', data=data)\n",
    "posthoc.loc[posthoc['p-corr'] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d73261f-b487-487c-a03d-5859ec3b1bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\") & (DID['Session'] > 0)]\n",
    "\n",
    "sns.stripplot(data = data, x = 'Session', y = var, hue = \"ID\", jitter = True, edgecolor='gray', alpha =0.5, palette='dark:#1B263B', size = 1)\n",
    "sns.pointplot(data = data, x = 'Session', y = var, hue = \"Fluid\", errorbar = 'se', palette='dark:#1B263B', scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)', fontsize = 5)\n",
    "plt.xlabel('Drinking Session', fontsize = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 2)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('WaterIntake.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=\"Intake\", within='Session', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99411e02-c40f-46f9-ade0-c86f0ffeb4ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\") & (DID['Session'] > 0)]\n",
    "\n",
    "sns.stripplot(data = data, x = 'Session', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1)\n",
    "sns.pointplot(data = data, x = 'Session', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Drinking Session', size = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "plt.tight_layout()\n",
    "plt.savefig('WaterIntake_Sex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, within='Session', between='Sex', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8099e196-c946-498c-9e44-b4a1ca3bbdec",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\") & (DID['Session'] > 0)]\n",
    "\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"ID\", jitter = True, edgecolor='gray', alpha =0.5, palette='dark:#1B263B', size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Fluid\", errorbar= 'se', estimator= np.mean, palette='dark:#1B263B', scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.ylim([0,100])\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('WaterIntakexWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=\"Intake\", within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94da6a95-dca8-40df-a773-1eb11d46e9b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Water\") & (DID['Session'] > 0)]\n",
    "\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.ylim([0,100])\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('WaterIntakexWeekxSex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=\"Intake\", within='DIDWeek', between='Sex', subject='ID', data=data).round(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "91b51251-8bb5-45f4-9bb5-268525e0c718",
   "metadata": {},
   "source": [
    "### Alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c44232fc-0ffa-48d8-a20a-419bdb2cd883",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(24,8))\n",
    "sns.despine()\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Alcohol\")]\n",
    "\n",
    "sns.pointplot(data = data, x = 'Session', y = var, hue = \"ID\", errorbar = 'se')\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)')\n",
    "plt.xlabel('Drinking Session')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 2)\n",
    "\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=\"Intake\", within='Session', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9b028a4-e1f3-4c84-8b34-f04e5476bcba",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[(DID['Fluid'] == \"Alcohol\")]\n",
    "\n",
    "sns.stripplot(data = data, x = 'Session', y = var, hue = \"ID\", jitter = True, edgecolor='gray', alpha =0.5, palette='dark:#00a170', size = 1)\n",
    "sns.pointplot(data = data, x = 'Session', y = var, errorbar = 'se', color = \"#00a170\", scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[0:0], labels[:], frameon=False, loc = 2)\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)\\n', size = 5)\n",
    "plt.xlabel('Drinking Session', size = 5)\n",
    "\n",
    "plt.ylim(0,8)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('AlcoholIntakes.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=\"Intake\", within='Session', subject='ID', data=data, effsize=\"ng2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80cfdc09-ec26-4a38-8a3a-fcaca0e15114",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "sns.stripplot(data = data, x = 'Session', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1)\n",
    "sns.pointplot(data = data, x = 'Session', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)\\n', size = 5)\n",
    "plt.xlabel('Drinking Session', size = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.ylim(0,8)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('AlcoholxSex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=\"Intake\", within='Session', between='Sex', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06731a58-f5a1-4fe1-8c4c-9fb86fd4f3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"ID\", jitter = True, edgecolor='gray', alpha =0.5, palette='dark:#00a170', size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, errorbar= 'se', estimator= np.mean, color = '#00a170', scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)\\n', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.ylim([0,8])\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('AlcoholxWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=\"Intake\", within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2d5fe30-ae82-4c6d-852f-8603bfdf9ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=\"Intake\", within='DIDWeek', subject='ID', padjust='sidak', data=data)\n",
    "posthoc.loc[posthoc['p-corr'] < 0.05].round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deb3bf44-77aa-45ba-9a44-b8e2de635b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth/1.6\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Intake'\n",
    "\n",
    "data= DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)\\n', size = 5)\n",
    "plt.xlabel('Drinking Session', size = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.ylim([0,8])\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('AlcoholXweekXSex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=\"Intake\", within='DIDWeek', between='Sex', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac8b961c-a99c-4b10-b6c7-4c4f9b3d2553",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=\"Intake\", within='DIDWeek', between='Sex', subject='ID', padjust='sidak', data=data)\n",
    "posthoc.loc[posthoc['p-corr'] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c73344b-b058-4cb4-971c-a06058116b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/5\n",
    "figheight = figwidth\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = 'Previous_Water'\n",
    "\n",
    "data = DID.loc[DID['Session'] > 0]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Inter-Session\\nWater Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.ylim([0,400])\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('IntasessionWater.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, within='DIDWeek', between='Sex', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "655d718a-ca45-4ab1-90ca-c554e13dfeb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, within='DIDWeek', between='Sex', subject='ID', padjust='sidak', data=data)\n",
    "posthoc.loc[posthoc['p-corr'] < 0.05]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6e987141-5934-42d9-a4be-355e80690c52",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Microstructure Dataloader & Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6c4aa1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get current working directory and then move into the CSVs folder\n",
    "#wd = os.path.join(os.getcwd(), 'CSVs')\n",
    "\n",
    "#for i in DID['ID'].unique():\n",
    "#    print(i)\n",
    "#    #define new basefolder to search based on current animal ID\n",
    "#    base_folder = os.path.join(wd, str(i))\n",
    "#    #pull all the CSVs for the current animal\n",
    "#    for csv_file in glob.glob(str(base_folder) + \"/*.CSV\"):\n",
    "#        print(csv_file)\n",
    "#        csv = pd.read_csv(csv_file)\n",
    "#        xlsx_file = os.path.splitext(csv_file)[0] + '.xlsx'\n",
    "#        csv.to_excel(xlsx_file, index=None, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a7a514f",
   "metadata": {},
   "outputs": [],
   "source": [
    "masterdrinker = pd.DataFrame()\n",
    "\n",
    "#get current working directory and then move into the CSVs folder\n",
    "wd = os.path.join(os.getcwd(), 'CSVs')\n",
    "\n",
    "for i in DID['ID'].unique():\n",
    "    print(\"Loading Data...\", i)\n",
    "    #define new basefolder to search based on current animal ID\n",
    "    base_folder = os.path.join(wd, str(i))\n",
    "    #pull all the XLSXs for the current animal\n",
    "    filenames =  glob.glob(str(base_folder) + \"/*.xlsx\")\n",
    "    #loop thru all the files for this animal and add them to a list that resets with each animal\n",
    "    DIDs = []\n",
    "    current_animal = []\n",
    "    for filename in filenames:\n",
    "        DIDs.append(pd.read_excel(filename, ))\n",
    "    current_animal = pd.concat(DIDs, ignore_index=True)\n",
    "    current_animal.columns = current_animal.columns.str.replace(' ', '') #remove spaces from column names\n",
    "    current_animal = current_animal.drop(columns=['Shocks','LeftShockProbability', 'RightShockProbability']) #drop stupid columns\n",
    "    current_animal['ID'] = i #assign a new column based on animal index\n",
    "    current_animal['Sex'] = DID.loc[DID['ID'] == i, 'Sex'].iloc[0] #look up sex\n",
    "    current_animal['Cohort'] = DID.loc[DID['ID'] == i, 'Cohort'].iloc[0] #look up cohort\n",
    "    current_animal['Date'] = pd.to_datetime(current_animal['MM:DD:YYYYhh:mm:ss'], format='%m/%d/%Y %H:%M:%S') #convert to pandas datetime object\n",
    "\n",
    "    current_animal = current_animal.drop(['MM:DD:YYYYhh:mm:ss','Device'], axis = 1) #drop old datetime and change in time columns\n",
    "\n",
    "    for j in DID.loc[DID['ID'] == i, \"Session\"].unique():\n",
    "        # does the lickometer data for the session that you're looking up even exist?\n",
    "        if len(current_animal.loc[current_animal['Date'].dt.strftime('%m-%d-%Y') == DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"Date\"].dt.strftime('%m-%d-%Y').iloc[0]].copy()) != 0: \n",
    "            current_session = current_animal.loc[current_animal['Date'].dt.strftime('%m-%d-%Y') == DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"Date\"].dt.strftime('%m-%d-%Y').iloc[0]].copy()\n",
    "            current_session['Session'] = j\n",
    "            current_session['DIDWeek'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"DIDWeek\"].iloc[0]\n",
    "            current_session['SessionLength'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"SessionLength\"].iloc[0]\n",
    "            current_session['Fluid'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"Fluid\"].iloc[0]\n",
    "            current_session['BottleSide'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"BottleSide\"].iloc[0]\n",
    "\n",
    "            #if using an old version of lickometer software - this will convert old column tags to new versions\n",
    "            if 'ElapsedTime' in current_animal.columns:\n",
    "                current_session['ElapsedSecs'] = pd.to_timedelta(current_session['TimeElapsed'], unit='seconds') #convert change in time to correct type\n",
    "\n",
    "            current_session['ElapsedSecs'] = pd.to_timedelta(current_session['ElapsedSecs'], unit='seconds') #convert change in time to correct type\n",
    "            \n",
    "            if((current_session['BottleSide'].iloc[0]) == \"Left\"):\n",
    "                current_session['FluidCount'] = current_session['LeftCount']\n",
    "                current_session['FluidDuration'] = current_session['LeftDuration']\n",
    "\n",
    "            if((current_session['BottleSide'].iloc[0]) == \"Right\"):\n",
    "                current_session['FluidCount'] = current_session['RightCount']\n",
    "                current_session['FluidDuration'] = current_session['RightDuration']\n",
    "\n",
    "            current_session['FluidCountDiff'] = current_session['FluidCount'].diff().fillna(0)\n",
    "            current_session['FluidDurationDiff'] = current_session['FluidDuration'].diff().fillna(0)            \n",
    "\n",
    "            current_session['VideoRecording'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"VideoRecording\"].iloc[0]\n",
    "            current_session['FPRecording'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), \"FPRecording\"].iloc[0]\n",
    "            current_session['Seconds'] = current_session['ElapsedSecs'].dt.seconds\n",
    "            current_session['Minutes'] = current_session['ElapsedSecs'].dt.seconds/60 #convert TimeElapsed into minutes\n",
    "            current_session['Hours'] = current_session['ElapsedSecs'].dt.seconds/3600 #convert TimeElapsed into hours\n",
    "\n",
    "            current_session_cleaned = []\n",
    "           \n",
    "            current_session_cleaned = current_session.loc[current_session['ElapsedSecs'] <= pd.Timedelta(hours= current_session['SessionLength'].unique()[0])].copy()\n",
    "\n",
    "            current_session_cleaned = current_session_cleaned.loc[current_session_cleaned['FluidCountDiff'] > 0]\n",
    "\n",
    "            masterdrinker = pd.concat([masterdrinker, current_session_cleaned])            \n",
    "        # there's no lickometer data for this DID session, so continue the loop and keep going\n",
    "        else:\n",
    "            continue\n",
    "\n",
    "print( '\\n Cleaning Data... \\n')\n",
    "\n",
    "for i in masterdrinker['ID'].unique():\n",
    "    for j in masterdrinker.loc[(masterdrinker['ID'] == i), 'Session'].unique():\n",
    "        model = sm.OLS(masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j), 'FluidDurationDiff'],\n",
    "                       masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j), 'FluidCountDiff'])\n",
    "        masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j), 'resid'] = model.fit().resid\n",
    "\n",
    "masterdrinker = masterdrinker.loc[masterdrinker['resid'] < 3]\n",
    "masterdrinker = masterdrinker.loc[masterdrinker['resid'] > -3]\n",
    "\n",
    "masterdrinker = masterdrinker.loc[masterdrinker['FluidDurationDiff'] < 15]\n",
    "\n",
    "masterdrinker = masterdrinker.dropna(axis=1)\n",
    "masterdrinker.to_excel('masterdrinker.xlsx')\n",
    "masterdrinker.head()\n",
    "\n",
    "sns.relplot(data = masterdrinker, x = \"FluidCountDiff\", y = \"FluidDurationDiff\", hue = 'ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4984e9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "for i in masterdrinker['ID'].unique():\n",
    "    print(i)\n",
    "    sessions = masterdrinker.loc[(masterdrinker['ID'] == i), 'Session'].unique()\n",
    "    for j in sessions:\n",
    "        #Total Licks\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'TotalEvents'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0), 'FluidCountDiff'].sum()\n",
    "        #Total Lick Duration\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'TotalEventDuration'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0), 'FluidDurationDiff'].sum()\n",
    "        #Number of Drinks\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Drinks'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0), 'FluidCountDiff'].count()\n",
    "        #Mean Licks per Drink\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'MeanEventsperDrink'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0), 'FluidCountDiff'].mean()\n",
    "        #Mean Duration per Drink\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'MeanEventDurattionperDrink'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0), 'FluidDurationDiff'].mean()\n",
    "        #Latency to Drink\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Latency'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0), \"ElapsedSecs\"].iloc[0].seconds\n",
    "        #Interdrink Interval\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'IDI'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0), \"ElapsedSecs\"].diff().dropna().mean().seconds\n",
    "\n",
    "        #####################################################################\n",
    "        #First 30 Minutes\n",
    "        #####################################################################\n",
    "        #Total Licks\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'TotalEvents30'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0) & (masterdrinker['ElapsedSecs'] <= pd.Timedelta(hours=0.5)), 'FluidCountDiff'].sum()\n",
    "        #Total Lick Duration\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'TotalEventDuration30'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0) & (masterdrinker['ElapsedSecs'] <= pd.Timedelta(hours=0.5)), 'FluidDurationDiff'].sum()\n",
    "        #Number of Drinks\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Drinks30'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0) & (masterdrinker['ElapsedSecs'] <= pd.Timedelta(hours=0.5)), 'FluidCountDiff'].count()\n",
    "        #Mean Licks per Drink\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'MeanEventsperDrink30'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0) & (masterdrinker['ElapsedSecs'] <= pd.Timedelta(hours=0.5)), 'FluidCountDiff'].mean()\n",
    "        #Mean Duration per Drink\n",
    "        DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'MeanEventDurattionperDrink30'] = masterdrinker.loc[(masterdrinker['ID'] == i) & (masterdrinker['Session'] == j) & (masterdrinker['FluidCountDiff'] > 0) & (masterdrinker['ElapsedSecs'] <= pd.Timedelta(hours=0.5)), 'FluidDurationDiff'].mean()\n",
    "\n",
    "DID.to_excel('DID.xlsx') \n",
    "DID.to_parquet('DID.parquet')     \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9898b0",
   "metadata": {},
   "source": [
    "# Microstructure Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5ec08f",
   "metadata": {},
   "source": [
    "## Event Corrrelations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ec50f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Water\"]\n",
    "\n",
    "var = 'TotalEvents'\n",
    "\n",
    "sns.lmplot(x= var, y=  \"Intake\", data = data, hue = 'Sex', \n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           hue_order=['Male','Female'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Drinking Events', size = 5)\n",
    "\n",
    "plt.ylim([0,120])\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.legend(frameon = False, loc = 'lower right', fontsize = 5, markerscale = 2)\n",
    "\n",
    "stat_out = pg.corr(data['Intake'], data[var], method='shepherd')\n",
    "pval = sig_figs(stat_out['p-val'].iloc[0],4)\n",
    "rval = sig_figs(stat_out['r'].iloc[0],4)\n",
    "\n",
    "plt.text(x= 1, y = 110,  s = (f\"r = {rval}\\np = {pval}\"), size = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_Events.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "print(pval)\n",
    "print(rval)\n",
    "\n",
    "pg.ancova(data=data, dv='Intake', between='Sex', covar= var, effsize='np2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4cf7a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "var = 'TotalEvents'\n",
    "\n",
    "sns.lmplot(x= var, y=  \"Intake\", data = data, hue = 'Sex', \n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           hue_order=['Male','Female'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Drinking Events', size = 5)\n",
    "\n",
    "plt.ylim([0,10])\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.legend(frameon = False, loc = 'lower right', fontsize = 5, markerscale = 2)\n",
    "\n",
    "stat_out = pg.corr(data['Intake'], data[var], method='shepherd')\n",
    "pval = sig_figs(stat_out['p-val'].iloc[0],4)\n",
    "rval = sig_figs(stat_out['r'].iloc[0],4)\n",
    "\n",
    "plt.text(x= 1, y = 9,  s = (f\"r = {rval}\\np = {pval}\"), size = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_Events.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "print(pval)\n",
    "print(rval)\n",
    "\n",
    "pg.ancova(data=data, dv='Intake', between='Sex', covar= var, effsize='np2').round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd4db558",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Water\"]\n",
    "\n",
    "var = 'TotalEventDuration'\n",
    "\n",
    "sns.lmplot(x= var, y=  \"Intake\", data = data, hue = 'Sex', \n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           hue_order=['Male','Female'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "plt.ylabel('Water Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Total Event Duration (s)', size = 5)\n",
    "\n",
    "plt.ylim([0,120])\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.legend(frameon = False, loc = 'lower right', fontsize = 5, markerscale = 2)\n",
    "\n",
    "stat_out = pg.corr(data['Intake'], data[var], method='shepherd')\n",
    "pval = sig_figs(stat_out['p-val'].iloc[0],4)\n",
    "rval = sig_figs(stat_out['r'].iloc[0],4)\n",
    "\n",
    "plt.text(x= 1, y = 110,  s = (f\"r = {rval}\\np = {pval}\"), size = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_Event_Duration.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "print(pval)\n",
    "print(rval)\n",
    "\n",
    "pg.ancova(data=data, dv='Intake', between='Sex', covar= var, effsize='np2').round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75bcae3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "var = 'TotalEventDuration'\n",
    "\n",
    "sns.lmplot(x= var, y=  \"Intake\", data = data, hue = 'Sex', \n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           hue_order=['Male','Female'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Total Event Duration (s)', size = 5)\n",
    "\n",
    "plt.ylim([0,10])\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.legend(frameon = False, loc = 'lower right', fontsize = 5, markerscale = 2)\n",
    "\n",
    "stat_out = pg.corr(data['Intake'], data[var], method='shepherd')\n",
    "pval = sig_figs(stat_out['p-val'].iloc[0],4)\n",
    "rval = sig_figs(stat_out['r'].iloc[0],4)\n",
    "\n",
    "plt.text(x= 1, y = 9,  s = (f\"r = {rval}\\np = {pval}\"), size = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_Event_Duration.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "print(pval)\n",
    "print(rval)\n",
    "\n",
    "pg.ancova(data=data, dv='Intake', between='Sex', covar= var, effsize='np2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1d8e43a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "var = 'Drinks'\n",
    "\n",
    "sns.lmplot(x= var, y=  \"Intake\", data = data, hue = 'Sex', \n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           hue_order=['Male','Female'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "plt.ylabel('Alcohol Intake (g/kg)', size = 5)\n",
    "plt.xlabel('Drinks', size = 5)\n",
    "\n",
    "plt.ylim([0,10])\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.legend(frameon = False, loc = 'lower right', fontsize = 5, markerscale = 2)\n",
    "\n",
    "stat_out = pg.corr(data['Intake'], data[var], method='shepherd')\n",
    "pval = sig_figs(stat_out['p-val'].iloc[0],4)\n",
    "rval = sig_figs(stat_out['r'].iloc[0],4)\n",
    "\n",
    "plt.text(x= 1, y = 9,  s = (f\"r = {rval}\\np = {pval}\"), size = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_Drinks.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "print(pval)\n",
    "print(rval)\n",
    "\n",
    "pg.ancova(data=data, dv='Intake', between='Sex', covar= var, effsize='np2')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f2b97775-1bf2-4442-b906-af49ee2e6310",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Thirst State Correlations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "320a849f-6a1c-49e2-ba2e-7d099c12f2de",
   "metadata": {},
   "source": [
    "### Does previous 24 hours of water intake correlate with water or alcohol intake during DID Task?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "020d0a53-0c83-47e3-9663-35bb69d28979",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/5\n",
    "figheight = figwidth\n",
    "\n",
    "data = data = DID.loc[DID['Fluid'] == \"Water\"]\n",
    "\n",
    "print(pg.multivariate_normality(data[['Previous_Water', 'Intake']]))\n",
    "\n",
    "h = sns.lmplot(x= 'Previous_Water', y=  \"Intake\", data = data, hue = 'Fluid',\n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           palette = ['#000000'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "h.set_axis_labels('Previous Inter-Session\\nWater Intake (g/kg)', 'DID Session\\nWater Intake (g/kg)', size = 5)\n",
    "\n",
    "plt.ylim([0,120])\n",
    "\n",
    "plt.yticks([0,20,40,60,80,100,120])\n",
    "\n",
    "stat_out = pg.corr(data['Previous_Water'], data['Intake'], method='shepherd')\n",
    "pval = stat_out['p-val'].iloc[0]\n",
    "plt.text(x= 75, y = 100,  s = (f\"r = {stat_out['r'].iloc[0].round(4)}\\np = {pval.round(4)}\"), size = 5)\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('previouswaterdrinking.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.corr(data['Previous_Water'], data['Intake'], method='shepherd').round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "299be222-387c-4b57-9257-2bc72eaf744b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/5\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Water\"]\n",
    "\n",
    "print(pg.multivariate_normality(data[['Previous_Water', 'Intake']]))\n",
    "\n",
    "h = sns.lmplot(x= 'Previous_Water', y=  \"Intake\", data = data, hue = 'Sex',\n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           hue_order=['Male','Female'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "h.set_axis_labels('Previous Inter-Session\\nWater Intake (g/kg)', 'DID Session\\nWater Intake (g/kg)', size = 5)\n",
    "\n",
    "plt.ylim([0,120])\n",
    "plt.yticks([0,20,40,60,80,100,120])\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.legend(frameon = False, loc = 'upper left', fontsize = 5, markerscale = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('previouswaterLMbySex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.ancova(data=data, dv='Intake', between='Sex', covar='Previous_Water', effsize='np2').round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d00f441-6ea1-4f10-b394-b369cc0d1c28",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/5\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "print(pg.multivariate_normality(data[['Previous_Water', 'Intake']]))\n",
    "\n",
    "h = sns.lmplot(x= 'Previous_Water', y=  \"Intake\", data = data, hue = 'Fluid',\n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           palette = ['#00a170'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "h.set_axis_labels('Previous Inter-Session\\nWater Intake (g/kg)', 'DID Session\\nAlcohol Intake (g/kg)', size = 5)\n",
    "\n",
    "plt.ylim([0,10])\n",
    "\n",
    "stat_out = pg.corr(data['Previous_Water'], data['Intake'], method='shepherd')\n",
    "pval = stat_out['p-val'].iloc[0]\n",
    "plt.text(x= 45, y = 8,  s = (f\"r = {stat_out['r'].iloc[0].round(4)}\\np = {pval.round(4)}\"), size = 5)\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('previousalocholbyintersession.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.corr(data['Previous_Water'], data['Intake'], method='shepherd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf07099c-a580-48bf-b626-4861ec6447e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/5\n",
    "figheight = figwidth\n",
    "\n",
    "data = DID.loc[DID['Fluid'] == \"Alcohol\"]\n",
    "\n",
    "print(pg.multivariate_normality(data[['Previous_Water', 'Intake']]))\n",
    "\n",
    "h = sns.lmplot(x= 'Previous_Water', y=  \"Intake\", data = data, hue = 'Sex',\n",
    "           ci = 68,\n",
    "           height = figheight,\n",
    "           hue_order=['Male','Female'],\n",
    "           scatter_kws={'linewidths': 0, \"s\": 2.5},\n",
    "           line_kws={\"linewidth\":0.5},\n",
    "           legend = False\n",
    "           )\n",
    "\n",
    "h.set_axis_labels('Previous Inter-Session\\nWater Intake (g/kg)', 'DID Session\\nAlcohol Intake (g/kg)', size = 5)\n",
    "\n",
    "plt.ylim([0,10])\n",
    "\n",
    "plt.tick_params(labelsize=5, width=0.5, length = 2)\n",
    "\n",
    "plt.legend(frameon = False, loc = 'upper left', fontsize = 5, markerscale = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('previouswaterALCOHOLbySex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.ancova(data=data, dv='Intake', between='Sex', covar='Previous_Water', effsize='np2').round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea332a3-5228-400c-9bcf-28d482119edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = DID.loc[(DID['Fluid'] == \"Alcohol\") & (DID['Sex'] == \"Female\")]\n",
    "\n",
    "print(pg.multivariate_normality(data[['Previous_Water', 'Intake']]))\n",
    "\n",
    "sns.lmplot(x= 'Previous_Water', y=  \"Intake\", data = data, hue = 'Sex', ci = 68, height=8,\n",
    "           scatter_kws={'linewidths': 0}, legend=False)\n",
    "\n",
    "plt.ylim(0,10)\n",
    "\n",
    "plt.xlabel(\"Previous Inter-Session\\nWater Intake (g/kg)\")\n",
    "plt.ylabel(\"DID Session\\nAlcohol Intake (g/kg)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.corr(data['Previous_Water'], data['Intake'], method='shepherd').round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50a7a251-7255-4fcb-bb40-34ac030852a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = DID.loc[(DID['Fluid'] == \"Alcohol\") & (DID['Sex'] == \"Male\")]\n",
    "\n",
    "print(pg.multivariate_normality(data[['Previous_Water', 'Intake']]))\n",
    "\n",
    "sns.lmplot(x= 'Previous_Water', y=  \"Intake\", data = data, hue = 'Sex', ci = 68, height=8, scatter_kws={'linewidth': 0}, legend=False)\n",
    "\n",
    "plt.ylim(0,10)\n",
    "\n",
    "plt.xlabel(\"Previous Inter-Session\\nWater Intake (g/kg)\")\n",
    "plt.ylabel(\"DID Session\\nAlcohol Intake (g/kg)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('previouswaterALCOHOLbySex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.corr(data['Previous_Water'], data['Intake'], method='shepherd').round(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "54730693-0f99-4785-a97e-2b330e71530d",
   "metadata": {},
   "source": [
    "## Microstructure by Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b5d5009-472e-45c2-a87b-d2c2b910da5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Water\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'TotalEvents'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Events', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,500)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_Events_Time.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aef6da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Alcohol\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'TotalEvents'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Events', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,700)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_Events_Time.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9bbd3ed-18db-4c26-8c8b-6bbe82c2165a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Water\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'TotalEventDuration'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Event Duration (s)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,140)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_EventDuration_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "669ebe12-2457-40b0-94f6-1b84a193a7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Alcohol\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'TotalEventDuration'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Event Duration (s)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,60)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_EventDuration_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a9717e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pg.pairwise_tests(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data, padjust='sidak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83bbee78-c2f2-499b-882b-79b3b026c66b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Water\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'Drinks'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Drinks', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,200)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_Drinks_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec8e64ae-d17a-4ac7-bb3f-15e4bc223853",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Alcohol\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'Drinks'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Drinks', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,90)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_Drinks_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5bf4e40-533d-4a37-ac7a-f4eb716cf22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Water\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'Latency'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Latency to Drink (s)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,6000)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_Latency_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6652d60-062f-4f82-ab0d-1c7399df989d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Alcohol\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'Latency'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Latency to Drink (s)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,9000)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_Latency_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce48f21c-b05f-476a-a1d2-67dd0049630e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Water\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'IDI'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Mean\\nInter Drink Interval (s)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,6000)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_IDI_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d45de40c-4f53-4b2f-bacb-4a4a4f7db590",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Alcohol\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'IDI'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Mean\\nInter Drink Interval (s)', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,3000)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_IDI_Week.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a93d436-8412-4baa-8785-689ab58d3d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pg.pairwise_tests(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data, padjust='sidak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7475a8d-eb6f-4594-8370-db4c9f26ccfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Water\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'TotalEvents30'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Water Events\\nin First 30 Minutes', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,70)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Water_Events_30.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f63173a9-237c-40bc-8461-fac769235520",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/6\n",
    "figheight = figwidth*1.6\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = DID.loc[(DID['DIDWeek'] > 0) & (DID['Fluid'] == \"Alcohol\")]\n",
    "data['DIDWeek'] = data['DIDWeek'].astype('int')\n",
    "\n",
    "var = 'TotalEvents30'\n",
    "\n",
    "sns.stripplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", jitter = True, edgecolor='gray', alpha =0.5, hue_order=['Male','Female'], size = 1)\n",
    "sns.pointplot(data = data, x = 'DIDWeek', y = var, hue = \"Sex\", errorbar='se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5)\n",
    "\n",
    "plt.ylabel('Alcohol Events\\nin First 30 Minutes', size = 5)\n",
    "plt.xlabel('Drinking Week', size = 5)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:2], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Alcohol_Events_30.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, between='Sex', within='DIDWeek', subject='ID', data=data).round(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9987a8ac-ef7b-495c-9218-234ff2befd01",
   "metadata": {},
   "source": [
    "# FP DATALOADER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2920d122",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import linear_model\n",
    "from scipy import signal\n",
    "from sklearn.linear_model import RANSACRegressor\n",
    "\n",
    "#define biexponential function\n",
    "def _2exponential(x, a, b, c, d,):\n",
    "    return a*np.exp(b*x) + c*np.exp(d*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78041f37-a103-4802-87e7-7e84c2679a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "FP = pd.DataFrame()\n",
    "\n",
    "for i in DID['ID'].unique():\n",
    "    print(\"Initalizing...\", i)\n",
    "    files = []\n",
    "    for j in DID.loc[DID['ID'] == i, \"Session\"].unique():\n",
    "        wd = os.path.join(os.getcwd(), \"Session\" + \" \" + str(j))\n",
    "        current_session = os.path.join(wd, str(i))\n",
    "        filename = glob.glob(current_session+\"/\"+\"all_raw*.csv\")\n",
    "        if len(filename) != 0:\n",
    "            files.append(filename)\n",
    "            print(\"Adding Session...\", j)\n",
    "            for k in files:\n",
    "                temp_df = pd.read_csv(k[0])\n",
    "                temp_df['ID'] = i\n",
    "                temp_df['Session'] = j\n",
    "                temp_df['Date'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Date'].iloc[0]\n",
    "                temp_df['BottleSide'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'BottleSide'].iloc[0]\n",
    "                temp_df['Fluid'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Fluid'].iloc[0]\n",
    "                temp_df['SessionLength'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'SessionLength'].iloc[0]\n",
    "                temp_df['DIDWeek'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'DIDWeek'].iloc[0]\n",
    "                temp_df['Sex'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Sex'].iloc[0]\n",
    "            FP = pd.concat([FP, temp_df])\n",
    "\n",
    "FP.to_parquet('FP.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f92782d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for animal in FP['ID'].unique():\n",
    "    print(\"Initalizing...\", animal)\n",
    "\n",
    "    for session in FP.loc[FP['ID'] == animal, \"Session\"].unique():\n",
    "        print(session)\n",
    "        \n",
    "        FPSession = FP.loc[(FP['ID'] == animal) & (FP['Session'] == session)].copy()\n",
    "\n",
    "        FPSession = FPSession.loc[FPSession['FrameCounter'] > 300]\n",
    "\n",
    "        ### Fit Left Isosbestic\n",
    "        try:\n",
    "            popt_left, pcov_left = curve_fit(_2exponential,\n",
    "                                np.array(FPSession.loc[FPSession['LedState'] == 1, 'Timestamp']),\n",
    "                                np.array(FPSession.loc[FPSession['LedState'] == 1, 'Region2G']),\n",
    "                                p0=[0,-0.0025,0.000025,0], maxfev=100000000\n",
    "                                )\n",
    "        except ValueError:\n",
    "            print(animal, session, \"Fit Failed Left Side\")\n",
    "\n",
    "        FPSession.loc[FPSession['LedState'] == 1, 'IsosbesticFit_Left'] = _2exponential(np.array(FPSession.loc[FPSession['LedState'] == 1, 'Timestamp']), *popt_left) #write fit y values to array\n",
    "\n",
    "        ### Fit Right Isosbestic\n",
    "        try:\n",
    "            popt_right, pcov_right = curve_fit(_2exponential,\n",
    "                                np.array(FPSession.loc[FPSession['LedState'] == 1, 'Timestamp']),\n",
    "                                np.array(FPSession.loc[FPSession['LedState'] == 1, 'Region3G']),\n",
    "                                p0=[0,-0.0025,0.000025,0], maxfev=100000000\n",
    "                                )\n",
    "        except ValueError:\n",
    "            print(animal, session, \"Fit Failed Left Side\")\n",
    "\n",
    "        FPSession.loc[FPSession['LedState'] == 1, 'IsosbesticFit_Right'] = _2exponential(np.array(FPSession.loc[FPSession['LedState'] == 1, 'Timestamp']), *popt_right) #write fit y values to array\n",
    "\n",
    "        ###Scale Left Fit\n",
    "\n",
    "        X = np.array(FPSession.loc[FPSession['LedState'] == 1, 'IsosbesticFit_Left'])\n",
    "        y = np.array(FPSession.loc[FPSession['LedState'] == 2, 'Region2G'])\n",
    "\n",
    "        if len(X) != len(y):\n",
    "            X = signal.resample(X, len(y))\n",
    "\n",
    "        X = X.reshape(-1,1)\n",
    "        y = y.reshape(-1,1)\n",
    "\n",
    "        robust_linear_model = RANSACRegressor(random_state=42)\n",
    "        robust_linear_model.fit(X.reshape(-1,1), y.reshape(-1,1))\n",
    "        FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region2G\"] = robust_linear_model.predict(X)\n",
    "\n",
    "        ###Scale Right Fit\n",
    "\n",
    "        X = np.array(FPSession.loc[FPSession['LedState'] == 1, 'IsosbesticFit_Right']).reshape(-1, 1)\n",
    "        y = np.array(FPSession.loc[FPSession['LedState'] == 2, 'Region3G']).reshape(-1, 1)\n",
    "\n",
    "        if len(X) != len(y):\n",
    "            X = signal.resample(X, len(y))\n",
    "\n",
    "        X = X.reshape(-1,1)\n",
    "        y = y.reshape(-1,1)\n",
    "\n",
    "        robust_linear_model = RANSACRegressor(random_state=42)\n",
    "        robust_linear_model.fit(X, y)\n",
    "        FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region3G\"] = robust_linear_model.predict(X)\n",
    "\n",
    "        ### Lowpass Left Signal\n",
    "        FPSession.loc[FPSession['LedState'] == 2, 'lowpass470_left'] = signal.sosfilt(signal.butter(2, 6, fs = 30, btype='lowpass', output = 'sos'), FPSession.loc[FPSession['LedState'] == 2, \"Region2G\"])\n",
    "\n",
    "        ### Lowpass Right Signal\n",
    "        FPSession.loc[FPSession['LedState'] == 2, 'lowpass470_right'] = signal.sosfilt(signal.butter(2, 6, fs = 30, btype='lowpass', output = 'sos'), FPSession.loc[FPSession['LedState'] == 2, \"Region3G\"])\n",
    "\n",
    "        ### Left Graphs\n",
    "        #plot raw 415\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 1, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 1, \"Region2G\"], 'b-', label='Raw 415nm Channel')\n",
    "        #plot isobestic fit\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 1, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 1, \"IsosbesticFit_Left\"], 'r-', label='Isobestic Fit')\n",
    "        #plot raw 470\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 2, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 2, \"Region2G\"], 'g-', label='Raw 470nm Channel')\n",
    "        #plot lowpass filtered 470 data\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 2, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_left\"], 'c-', label='6 Hz Lowpass Filtered 470nm Channel')\n",
    "        #plot scaled isobestic fit\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 2, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region2G\"], 'y-', label='Scaled Isobestic Fit')\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.title(\"Left DLS\")\n",
    "        plt.xlabel('Time (s)')\n",
    "        plt.ylabel('AU')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "        ### Right Graphs\n",
    "        #plot raw 415\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 1, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 1, \"Region3G\"], 'b-', label='Raw 415nm Channel')\n",
    "        #plot isobestic fit\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 1, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 1, \"IsosbesticFit_Right\"], 'r-', label='Isobestic Fit')\n",
    "        #plot raw 470\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 2, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 2, \"Region3G\"], 'g-', label='Raw 470nm Channel')\n",
    "        #plot lowpass filtered 470 data\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 2, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_right\"], 'c-', label='6 Hz Lowpass Filtered 470nm Channel')\n",
    "        #plot scaled isobestic fit\n",
    "        plt.plot(FPSession.loc[FPSession['LedState'] == 2, \"Timestamp\"], FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region3G\"], 'y-', label='Scaled Isobestic Fit')\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.title(\"Right DLS\")\n",
    "        plt.xlabel('Time (s)')\n",
    "        plt.ylabel('AU')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "        #Calculate dF/F\n",
    "        FP.loc[(FP['ID'] == animal) & (FP['Session'] == session) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), \"LeftSignal_dF\"] = ((FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_left\"]) / (FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region2G\"]))\n",
    "        FP.loc[(FP['ID'] == animal) & (FP['Session'] == session) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), \"LeftSignal_dFF\"] = (((FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_left\"])-(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region2G\"]))/(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region2G\"]))\n",
    "        FP.loc[(FP['ID'] == animal) & (FP['Session'] == session) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), \"LeftSignal_dFF_zscore\"] = stats.zscore((((FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_left\"])-(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region2G\"]))/(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region2G\"])), nan_policy='omit')\n",
    "\n",
    "        \n",
    "        FP.loc[(FP['ID'] == animal) & (FP['Session'] == session) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), \"RightSignal_dF\"] = ((FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_right\"]) / (FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region3G\"]))\n",
    "        FP.loc[(FP['ID'] == animal) & (FP['Session'] == session) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), \"RightSignal_dFF\"] = (((FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_right\"])-(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region3G\"]))/(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region3G\"]))\n",
    "        FP.loc[(FP['ID'] == animal) & (FP['Session'] == session) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), \"RightSignal_dFF_zscore\"] = stats.zscore((((FPSession.loc[FPSession['LedState'] == 2, \"lowpass470_right\"])-(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region3G\"]))/(FPSession.loc[FPSession['LedState'] == 2, \"Scaled_Isobestic_Fit_Region3G\"])), nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9449319b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove data because the cable was broke, bad recording, or nonsense fit\n",
    "FP.loc[(FP['ID'] == 'P1') & (FP['Session'] == 5) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P1') & (FP['Session'] == 9) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P1') & (FP['Session'] == 13) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P1') & (FP['Session'] == 17) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P1') & (FP['Session'] == 17) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P1') & (FP['Session'] == 21) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P2') & (FP['Session'] == 4) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P2') & (FP['Session'] == 4) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P2') & (FP['Session'] == 8) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P2') & (FP['Session'] == 12) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P2') & (FP['Session'] == 20) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 3) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 7) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 11) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 15) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 23) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 23) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 27) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P3') & (FP['Session'] == 27) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P4') & (FP['Session'] == 2) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P4') & (FP['Session'] == 6) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P4') & (FP['Session'] == 10) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P4') & (FP['Session'] == 14) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P4') & (FP['Session'] == 14) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P4') & (FP['Session'] == 18) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P4') & (FP['Session'] == 30) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P5') & (FP['Session'] == 2) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P8') & (FP['Session'] == 4) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P8') & (FP['Session'] == 25) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P10') & (FP['Session'] == 27) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P10') & (FP['Session'] == 27) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P12') & (FP['Session'] == 9) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P12') & (FP['Session'] == 13) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P12') & (FP['Session'] == 30) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "FP.loc[(FP['ID'] == 'P14') & (FP['Session'] == 3) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'LeftSignal_dFF_zscore'] = np.nan\n",
    "FP.loc[(FP['ID'] == 'P14') & (FP['Session'] == 3) & (FP['LedState'] == 2) & (FP['FrameCounter'] > 300), 'RightSignal_dFF_zscore'] = np.nan\n",
    "\n",
    "#create empty dataframe named timestamped_FP\n",
    "timestamped_FP = pd.DataFrame()\n",
    "\n",
    "for i in FP['ID'].unique():\n",
    "    print(\"Initalizing...\", i)\n",
    "    #create an empty list for storing filenames\n",
    "    files = []\n",
    "    \n",
    "    for j in FP.loc[FP['ID'] == i, \"Session\"].unique():\n",
    "        wd = os.path.join(os.getcwd(), \"Session\" + \" \" + str(j))\n",
    "        current_session = os.path.join(wd, str(i))\n",
    "\n",
    "        filename = glob.glob(current_session+\"/\"+\"FP_Timestamps*.csv\")\n",
    "\n",
    "        if len(filename) != 0:\n",
    "            files.append(filename)\n",
    "            print(\"Adding Session...\", j)\n",
    "\n",
    "            for k in files:\n",
    "                temp_df = pd.read_csv(k[0], header=None)\n",
    "                temp_df.columns = ('SYNC_Time','FrameCounter')\n",
    "                temp_df = FP.loc[(FP['ID'] == i) & (FP['Session'] == j)].merge(temp_df, left_on='FrameCounter', right_on='FrameCounter')\n",
    "               \n",
    "            timestamped_FP = pd.concat([timestamped_FP, temp_df])\n",
    "\n",
    "#lets do a little clean up here before we move on to adding licks\n",
    "#drop unnecessary columns\n",
    "timestamped_FP = timestamped_FP.drop(['Region0R', 'Region1R', 'Stimulation', 'Output0', 'Output1', 'Input0','Input1', 'Region4R', 'Region5G'], axis=1)\n",
    "\n",
    "#create empty dataframe named leftlicks_timestamped_FP to assign left licks to sync time \n",
    "leftlicks_timestamped_FP = pd.DataFrame()\n",
    "\n",
    "for i in timestamped_FP['ID'].unique():\n",
    "    print(\"Initalizing...\", i)\n",
    "    files = []\n",
    "    \n",
    "    for j in timestamped_FP.loc[timestamped_FP['ID'] == i, \"Session\"].unique():\n",
    "        wd = os.path.join(os.getcwd(), \"Session\" + \" \" + str(j))\n",
    "        current_session = os.path.join(wd, str(i))\n",
    "\n",
    "        filename = glob.glob(current_session+\"/\"+\"left_licks*.csv\")\n",
    "\n",
    "        if len(filename) != 0:\n",
    "            files.append(filename)\n",
    "            print(\"Adding Session...\", j)\n",
    "            \n",
    "            for k in files:\n",
    "                temp_df = pd.read_csv(k[0], header=None)             \n",
    "                temp_df.columns = ('SYNC_Time','LeftLick')\n",
    "                temp_df['ID'] = i\n",
    "                temp_df['Session'] = j\n",
    "                temp_df['Date'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Date'].iloc[0]\n",
    "                temp_df['BottleSide'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'BottleSide'].iloc[0]\n",
    "                temp_df['Fluid'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Fluid'].iloc[0]\n",
    "                temp_df['SessionLength'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'SessionLength'].iloc[0]\n",
    "                temp_df['DIDWeek'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'DIDWeek'].iloc[0]\n",
    "                temp_df['Sex'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Sex'].iloc[0]\n",
    "                temp_df = pd.concat([timestamped_FP.loc[(timestamped_FP['ID'] == i) & (timestamped_FP['Session'] == j)], temp_df]).sort_values(by='SYNC_Time')\n",
    "\n",
    "            #add temp_df to leftlicks_timestamped_FP\n",
    "            leftlicks_timestamped_FP = pd.concat([leftlicks_timestamped_FP, temp_df])\n",
    "\n",
    "#create empty dataframe named rightleftlicks_timestamped_FP to assign right licks to sync time after leftlicks have already been accomplished \n",
    "rightleftlicks_timestamped_FP = pd.DataFrame()\n",
    "\n",
    "for i in timestamped_FP['ID'].unique():\n",
    "    print(\"Initalizing...\", i)\n",
    "    files = []\n",
    "    \n",
    "    for j in timestamped_FP.loc[timestamped_FP['ID'] == i, \"Session\"].unique():\n",
    "        wd = os.path.join(os.getcwd(), \"Session\" + \" \" + str(j))\n",
    "        current_session = os.path.join(wd, str(i))\n",
    "\n",
    "        filename = glob.glob(current_session+\"/\"+\"right_licks*.csv\")\n",
    "\n",
    "        if len(filename) != 0:\n",
    "            files.append(filename)\n",
    "            print(\"Adding Session...\", j)\n",
    "            \n",
    "            for k in files:\n",
    "                temp_df = pd.read_csv(k[0], header=None)             \n",
    "                temp_df.columns = ('SYNC_Time','RightLick')\n",
    "                temp_df['ID'] = i\n",
    "                temp_df['Session'] = j\n",
    "                temp_df['Date'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Date'].iloc[0]\n",
    "                temp_df['BottleSide'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'BottleSide'].iloc[0]\n",
    "                temp_df['Fluid'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Fluid'].iloc[0]\n",
    "                temp_df['SessionLength'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'SessionLength'].iloc[0]\n",
    "                temp_df['DIDWeek'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'DIDWeek'].iloc[0]\n",
    "                temp_df['Sex'] = DID.loc[(DID['ID'] == i) & (DID['Session'] == j), 'Sex'].iloc[0]\n",
    "                temp_df = pd.concat([leftlicks_timestamped_FP.loc[(leftlicks_timestamped_FP['ID'] == i) & (leftlicks_timestamped_FP['Session'] == j)], temp_df]).sort_values(by='SYNC_Time')\n",
    "\n",
    "            #add temp_df to leftlicks_timestamped_FP\n",
    "            rightleftlicks_timestamped_FP = pd.concat([rightleftlicks_timestamped_FP, temp_df])\n",
    "\n",
    "# rename rightlick_leftlick_timestamped_FP to FP_FINAL\n",
    "FP_FINAL = rightleftlicks_timestamped_FP  \n",
    "\n",
    "#if bottleside is left and leftlick is Ture, then create a new column called Lick and set it to True\n",
    "FP_FINAL.loc[(FP_FINAL['BottleSide'] == 'Left') & (FP_FINAL['LeftLick'] == True), 'Lick'] = True\n",
    "FP_FINAL.loc[(FP_FINAL['BottleSide'] == 'Left') & (FP_FINAL['LeftLick'] == False), 'Lick'] = False\n",
    "#if bottleside is right and rightlick is Ture, then create a new column called Lick and set it to True\n",
    "FP_FINAL.loc[(FP_FINAL['BottleSide'] == 'Right') & (FP_FINAL['RightLick'] == True), 'Lick'] = True\n",
    "FP_FINAL.loc[(FP_FINAL['BottleSide'] == 'Right') & (FP_FINAL['RightLick'] == False), 'Lick'] = False\n",
    "\n",
    "#align licks to bout start\n",
    "for i in FP_FINAL['ID'].unique():\n",
    "    print(\"Initalizing...\", i)\n",
    "    for j in FP_FINAL.loc[FP_FINAL['ID'] == i, \"Session\"].unique():\n",
    "        print(\"Session\", j)\n",
    "        if len(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'] == True)]) != 0:\n",
    "            #set some vars \n",
    "            windowsize = 10000 #ms\n",
    "            drinkstart = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'] == True), \"SYNC_Time\"].iloc[0]\n",
    "            windowstart = 0\n",
    "            windowend = 0\n",
    "            drinknumber = 1\n",
    "            while len(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'] == True) & (FP_FINAL['SYNC_Time'] > drinkstart), 'SYNC_Time']) > 0:\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] == drinkstart), \"DrinkStart\"] = \"True\"\n",
    "                windowstart = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] >= drinkstart - (windowsize/2)), \"SYNC_Time\"].min()\n",
    "                windowend = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] <= drinkstart + (windowsize/2)), \"SYNC_Time\"].max()\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] >= windowstart) & (FP_FINAL['SYNC_Time'] <= windowend), 'DrinkWindow'] = True\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] >= windowstart) & (FP_FINAL['SYNC_Time'] <= windowend), 'DrinkNumber'] = drinknumber\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] >= drinkstart) & (FP_FINAL['SYNC_Time'] <= windowend), \"TimeFromDrink\"] = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] >= drinkstart) & (FP_FINAL['SYNC_Time'] <= windowend), 'SYNC_Time'] - drinkstart\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] >= windowstart) & (FP_FINAL['SYNC_Time'] <= drinkstart), 'TimeFromDrink'] = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['SYNC_Time'] >= windowstart) & (FP_FINAL['SYNC_Time'] <= drinkstart), 'SYNC_Time'] - drinkstart\n",
    "                drinknumber += 1\n",
    "                print('Found Drink Number ...', drinknumber)\n",
    "                \n",
    "                try:\n",
    "                    drinkstart = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) &  (FP_FINAL['Lick'] == True) & (FP_FINAL['SYNC_Time'] > windowend + (windowsize/2)),'SYNC_Time'].iloc[0]\n",
    "                except IndexError:\n",
    "                    break\n",
    "            \n",
    "            print('Analyzing...')\n",
    "            for k in FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2), \"DrinkNumber\"].unique():\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'LeftSignal_dFF_zscore_filtered'] = gaussian_filter1d(np.array(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'LeftSignal_dFF_zscore']), 2)\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'RightSignal_dFF_zscore_filtered'] = gaussian_filter1d(np.array(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'RightSignal_dFF_zscore']), 2)\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkNumber'] == k), 'DrinkLength'] = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].notnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['DrinkNumber'] == k), 'TimeFromDrink'].max()/1000\n",
    "                \n",
    "\n",
    "for i in FP_FINAL['ID'].unique():\n",
    "    print(i)\n",
    "    for j in FP_FINAL.loc[FP_FINAL['ID'] == i, \"Session\"].unique():\n",
    "        print(j)\n",
    "        for k in FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j), \"DrinkNumber\"].dropna().unique():\n",
    "            print(k)\n",
    "            if len(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['DrinkNumber'] == k)]) == 150:\n",
    "                   FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['DrinkNumber'] == k), 'TimeFromDrinkRound'] = np.linspace(-5, 5, num=150)\n",
    "            if len(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['DrinkNumber'] == k)]) == 149:\n",
    "                time_of_first_frame = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['DrinkNumber'] == k),'FrameCounter'].min()\n",
    "                #go grab one more frame...set it to the current drink number...and assign the drinkwindow to be true....\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['FrameCounter'] >= time_of_first_frame - 2) & (FP_FINAL['FrameCounter'] < time_of_first_frame), 'DrinkNumber'] = k\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['FrameCounter'] >= time_of_first_frame - 2) & (FP_FINAL['FrameCounter'] < time_of_first_frame), 'DrinkWindow'] = True\n",
    "                \n",
    "                #refilter with new frame...\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'LeftSignal_dFF_zscore_filtered'] = gaussian_filter1d(np.array(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'LeftSignal_dFF_zscore']), 2)\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'RightSignal_dFF_zscore_filtered'] = gaussian_filter1d(np.array(FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['Lick'].isnull()) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkNumber'] == k), 'RightSignal_dFF_zscore']), 2)\n",
    "\n",
    "                #then assign linspace...\n",
    "                FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['LedState'] == 2) & (FP_FINAL['DrinkWindow'] == True) & (FP_FINAL['DrinkNumber'] == k), 'TimeFromDrinkRound'] = np.linspace(-5, 5, num=150)\n",
    "\n",
    "FP_FINAL.to_parquet('FP_FINAL_THIS_ONE_IS_THE_SMOOTH_ONE.parquet')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "32937fea",
   "metadata": {},
   "source": [
    "# FP FINAL ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a71688f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "DID = pd.read_parquet('DID.parquet')\n",
    "masterdrinker = pd.read_excel('masterdrinker.parquet')\n",
    "FP_FINAL = pd.read_parquet('FP_Final.parquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966a3195",
   "metadata": {},
   "source": [
    "## Figure 3 - Supplement 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ec20df",
   "metadata": {},
   "source": [
    "### Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c16d3968",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecordingfromchewedcable\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,  data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = \"Sex\",\n",
    "             hue_order= ['Male','Female']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:2] + handles[3:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_BySex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, within='TimeFromDrinkRound', between='Sex', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bf143af",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecordingfromchewedcable\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-5,20)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('Left_Water_BySex_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6558589e",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecordingfromchewedcable\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_BySex_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9032967e",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecordingfromchewedcable\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_BySex_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98da1d5",
   "metadata": {},
   "source": [
    "### Right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f2cc752",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,  data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = \"Sex\",\n",
    "             hue_order= ['Male','Female']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:2] + handles[3:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_BySex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, within='TimeFromDrinkRound', between='Sex', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8d3a261",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-5,20)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_BySex_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e58244bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_BySex_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f22eb15a",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_BySex_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4d41ff",
   "metadata": {},
   "source": [
    "## Figure 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40b7cc3",
   "metadata": {},
   "source": [
    "### Males"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d12eb04",
   "metadata": {},
   "source": [
    "#### Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "579a7ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 1','Week 2','Week 3'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_Male_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['DIDWeek','TimeFromDrinkRound'], subject = 'ID', data = data, detailed=True).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7648766b",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_Male_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f37e041c",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_Male_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14374b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_Male_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076d9e6b",
   "metadata": {},
   "source": [
    "#### Right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "addfcc0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 1','Week 2','Week 3'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_Male_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['TimeFromDrinkRound','DIDWeek'], subject = 'ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c57f8b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_Male_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be692f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_Male_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bf55c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_Male_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54406ff4",
   "metadata": {},
   "source": [
    "### Females"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641cec71",
   "metadata": {},
   "source": [
    "#### Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee065fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 1','Week 2','Week 3'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_FEMale_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['DIDWeek','TimeFromDrinkRound'], subject = 'ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed5a633a",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_FEMale_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "838fee41",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_FEMale_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "098c0709",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Water_FEMale_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6af0402",
   "metadata": {},
   "source": [
    "#### Right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69bb4145",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 1','Week 2','Week 3'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_FEMale_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['TimeFromDrinkRound','DIDWeek'], subject = 'ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34a8501b",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_FEMale_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e61689",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_FEMale_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ca2417d",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Water') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 1','Week 2','Week 3'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Water_FEMale_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e58d05",
   "metadata": {},
   "source": [
    "## Figure 3 - Supplement 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a19a33",
   "metadata": {},
   "source": [
    "### Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e49743f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,  data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = \"Sex\",\n",
    "             hue_order= ['Male','Female']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:2] + handles[3:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_BySex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, within='TimeFromDrinkRound', between='Sex', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b302384",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, within='TimeFromDrinkRound', between='Sex', subject='ID', data=data)\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * Sex\") & (posthoc['p-unc'] < 0.05)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d0778e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-5,20)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_BySex_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bc1e985",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_BySex_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2754ab58",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_BySex_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461e87e8",
   "metadata": {},
   "source": [
    "### Right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2fc6e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,  data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = \"Sex\",\n",
    "             hue_order= ['Male','Female']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:2] + handles[3:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_BySex.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.mixed_anova(dv=var, within='TimeFromDrinkRound', between='Sex', subject='ID', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af264929",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, within='TimeFromDrinkRound', between='Sex', subject='ID', data=data)\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * Sex\") & (posthoc['p-unc'] < 0.05)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70035b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-5,20)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_BySex_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20098e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_BySex_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0a6cb00",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\")].copy()\n",
    "\n",
    "#badrecordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Sex\", ascending = False)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Sex', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= False, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Sex', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[0:0], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_BySex_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "normalality = pg.normality(data, dv=var, group='Sex', method=\"shapiro\")\n",
    "if False in normalality['normal'].unique():\n",
    "    print(pg.mwu(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], alternative='two-sided'))\n",
    "else:\n",
    "    print(pg.ttest(x = data.loc[data['Sex'] == \"Male\", var], y = data.loc[data['Sex'] == \"Female\", var], correction='auto'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1729599",
   "metadata": {},
   "source": [
    "## Figure 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "168e3cfa",
   "metadata": {},
   "source": [
    "### Males"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab69ff7f",
   "metadata": {},
   "source": [
    "#### Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf81ee6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 4','Week 5','Week 6'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_Male_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['TimeFromDrinkRound','DIDWeek'], subject = 'ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61285fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv= var, within=['TimeFromDrinkRound','DIDWeek'], subject = 'ID', data = data, padjust='fdr')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * DIDWeek\") & (posthoc['p-corr'] < 0.05)].sort_values(by=['TimeFromDrinkRound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97396eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_Male_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfe09cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#badrecording\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_Male_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bfe2293",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#badrecording\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), var] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), var] = np.nan\n",
    "\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_Male_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295169ea",
   "metadata": {},
   "source": [
    "#### Right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc18f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 4','Week 5','Week 6'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_Male_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['TimeFromDrinkRound','DIDWeek'], subject = 'ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0d6b26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_Male_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "266f742a",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_Male_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d8485b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_Male_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117e2931",
   "metadata": {},
   "source": [
    "### Females"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d1eee4",
   "metadata": {},
   "source": [
    "#### Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96b5fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 4','Week 5','Week 6'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_Female_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['TimeFromDrinkRound','DIDWeek'], subject = 'ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2389b3cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_FEMale_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3df1c427",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_FEMale_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9a0f935",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_Alcohol_FEMale_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf79d61",
   "metadata": {},
   "source": [
    "#### Right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17d671f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var, data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = 'Week', hue_order = ['Week 4','Week 5','Week 6'],\n",
    "             palette=['#262730','#8DA7BE','#DD6E42']\n",
    "            )\n",
    "\n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_FEMale_ByWeek.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv= var, within=['TimeFromDrinkRound','DIDWeek'], subject = 'ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e17f1bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_FEMale_ByWeek_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bdf636d",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_FEMale_ByWeek_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47736587",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == 'Alcohol') &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")].copy()\n",
    "\n",
    "#bad recordings\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), var] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 1, 'Week'] = 'Week 1'\n",
    "data.loc[data['DIDWeek'] == 2, 'Week'] = 'Week 2'\n",
    "data.loc[data['DIDWeek'] == 3, 'Week'] = 'Week 3'\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Week', y = var, hue = \"Week\", hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], alpha = 0.5, size = 1,  dodge= False, jitter=True)\n",
    "sns.pointplot(data = data, x = 'Week', y = var, hue = \"Week\", errorbar = 'se', hue_order = ['Week 4','Week 5','Week 6'], palette=['#262730','#8DA7BE','#DD6E42'], scale = 0.5, errwidth = 0.5, dodge=False, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "plt.xticks(rotation = 25)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[0:0])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_Alcohol_FEMale_ByWeek_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "variance = pg.homoscedasticity(data=data, dv=var, group='Week')\n",
    "if variance['equal_var'].iloc[0] == False:\n",
    "    print(\"Welch\")\n",
    "    print(pg.welch_anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_gameshowell(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['pval'] < 0.05])\n",
    "else:\n",
    "    print(\"ANOVA\")\n",
    "    print(pg.anova(dv = var, between='Week', data = data))\n",
    "    posthoc = pg.pairwise_tukey(dv = var, between='Week', data = data)\n",
    "    print(posthoc.loc[posthoc['p-tukey'] < 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee1ea51",
   "metadata": {},
   "source": [
    "## Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d587f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# by sex\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth*0.75\n",
    "figheight = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "handles = []\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "#combine data columns into one column\n",
    "data['Sex-Fluid'] = data['Sex'] + ' - ' + data[\"Fluid\"]\n",
    "data = pd.melt(data, id_vars=['ID','Session','DrinkNumber','Sex','Fluid','Sex-Fluid','TimeFromDrinkRound'], value_vars=['RightSignal_dFF_zscore_filtered','LeftSignal_dFF_zscore_filtered'])\n",
    "data['Sex-Fluid-Side'] = data['Sex-Fluid'] + '-' + data['variable']\n",
    "data = data.reset_index()\n",
    "\n",
    "plt.hlines(y = 0 , xmin = -5, xmax = 5, linestyles = 'dashed', color = 'black', alpha = 0.5, linewidth = 0.5)\n",
    "plt.vlines(x = 0 , ymin = -0.95, ymax = 1.45, linestyles = 'dashed', color = 'black', alpha = 0.5, linewidth = 0.5)\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = \"value\", \n",
    "             hue = 'Sex-Fluid',\n",
    "             style = 'variable',\n",
    "             data = data,\n",
    "             hue_order = ['Male - Water','Female - Water','Male - Alcohol', 'Female - Alcohol'],\n",
    "             style_order=['LeftSignal_dFF_zscore_filtered','RightSignal_dFF_zscore_filtered'],\n",
    "             estimator=np.mean, errorbar='se', size = 1)\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"zscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Bout Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightVLeft.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv='value', between=['Sex','Fluid','TimeFromDrinkRound','variable'], data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1755a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 999\n",
    "\n",
    "posthoc = pg.pairwise_tests(dv='value', between=['TimeFromDrinkRound',\"Sex-Fluid\"], data = data, padjust='sidak')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * Sex-Fluid\") & (posthoc['p-corr'] < 0.05)].sort_values(by = 'TimeFromDrinkRound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08b2ae9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv='value', between=['TimeFromDrinkRound',\"variable\"], data = data, padjust='sidak')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * variable\") & (posthoc['p-corr'] < 0.05)].sort_values(by = 'TimeFromDrinkRound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dff96c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# by sex\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth*0.25\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "#combine data columns into one column\n",
    "data['Sex-Fluid'] = data['Sex'] + ' - ' + data[\"Fluid\"]\n",
    "data = pd.melt(data, id_vars=['ID','Session','DrinkNumber','Sex','Fluid','Sex-Fluid','TimeFromDrinkRound'], value_vars=['LeftSignal_dFF_zscore_filtered','RightSignal_dFF_zscore_filtered'])\n",
    "data = data.groupby(['ID','Session','Sex','Fluid','Sex-Fluid','DrinkNumber','variable']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "sns.stripplot(y = \"value\", x = 'variable', hue = 'Sex-Fluid', data = data, legend = False, hue_order = ['Male - Water','Female - Water','Male - Alcohol', 'Female - Alcohol'], alpha = 0.5, size = 1,  dodge= True, jitter=True)\n",
    "sns.pointplot(y = \"value\", x = \"variable\", hue = 'Sex-Fluid', dodge=.6, data = data, estimator=np.mean, errorbar='se', hue_order = ['Male - Water','Female - Water','Male - Alcohol', 'Female - Alcohol'], scale = 0.5, errwidth = 0.5, join = False)\n",
    "\n",
    "plt.ylabel(\"Max zscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\" \")\n",
    "\n",
    "plt.ylim(-5,20)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[:], frameon=False, loc = 'upper right', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('BYside_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv='value', between=['variable','Sex-Fluid'], data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e74fe85",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv='value', between=['variable','Sex-Fluid'], data = data, padjust='sidak')\n",
    "posthoc.loc[posthoc['p-corr'] < 0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d57531c",
   "metadata": {},
   "source": [
    "### Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c97bde6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "var = 'LeftSignal_dFF_zscore_filtered'\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "data = data.dropna(subset=['LeftSignal_dFF_zscore_filtered'])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,  data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = \"Sex\",\n",
    "             hue_order= ['Male','Female'],\n",
    "             style = 'Fluid',\n",
    "             style_order= ['Water','Alcohol'],\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv=var, between=['TimeFromDrinkRound','Sex','Fluid'], data = data,  detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0203a925",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, between=['TimeFromDrinkRound',\"Sex\"], data = data, padjust='sidak')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * Sex\") & (posthoc['p-corr'] < 0.05)].sort_values(by = 'TimeFromDrinkRound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c76c626",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, between=['TimeFromDrinkRound',\"Fluid\"], data = data, padjust='sidak')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * Fluid\") & (posthoc['p-corr'] < 0.05)].sort_values(by = 'TimeFromDrinkRound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3656953e",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = 'LeftSignal_dFF_zscore_filtered'\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= True, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=.4, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv = var, between=['Sex','Fluid'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e739f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "pg.pairwise_tests(dv = var, between=['Sex','Fluid'], data = data, padjust='sidak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e3ee98f",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = 'LeftSignal_dFF_zscore_filtered'\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= True, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=.4, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv = var, between=['Sex','Fluid'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7af9d10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = 'LeftSignal_dFF_zscore_filtered'\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= True, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=.4, join = False)\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Left_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv = var, between=['Sex','Fluid'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f98382cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# by sex\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "var = 'RightSignal_dFF_zscore_filtered'\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,  data = data,\n",
    "             estimator=np.mean, errorbar='se', size = 1,\n",
    "             hue = \"Sex\",\n",
    "             hue_order= ['Male','Female'],\n",
    "             style = 'Fluid',\n",
    "             style_order= ['Water','Alcohol'],\n",
    "            )\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "printedhandles = (handles[:3] + handles[4:])\n",
    "ax.legend(handles = printedhandles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv=var, between=['TimeFromDrinkRound','Sex','Fluid'], data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278e0daa",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, between=['TimeFromDrinkRound',\"Sex\"], data = data, padjust='sidak')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * Sex\") & (posthoc['p-corr'] < 0.05)].sort_values(by = 'TimeFromDrinkRound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48642cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, between=['TimeFromDrinkRound',\"Fluid\"], data = data, padjust='sidak')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"TimeFromDrinkRound * Fluid\") & (posthoc['p-corr'] < 0.05)].sort_values(by = 'TimeFromDrinkRound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a832dff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = 'RightSignal_dFF_zscore_filtered'\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"time_to_peak\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= True, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=.4, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nTime to Peak Amplitude (s)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(0,6)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_TTP.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv = var, between=['Sex','Fluid'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60c2ac93",
   "metadata": {},
   "outputs": [],
   "source": [
    "pg.pairwise_tests(dv = var, between=['Sex','Fluid'], data = data, padjust='sidak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0b12e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = 'RightSignal_dFF_zscore_filtered'\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"AUC\"\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= True, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=.4, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nAUC (Peak Amplitude to Drink End)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-15,15)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_AUC.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv = var, between=['Sex','Fluid'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3846778",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = 'RightSignal_dFF_zscore_filtered'\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False].copy()\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "\n",
    "data.loc[data['DIDWeek'] == 4, 'Week'] = 'Week 4'\n",
    "data.loc[data['DIDWeek'] == 5, 'Week'] = 'Week 5'\n",
    "data.loc[data['DIDWeek'] == 6, 'Week'] = 'Week 6'\n",
    "\n",
    "for i in data['ID'].unique():\n",
    "  for j in data.loc[data['ID'] == i, 'Session'].unique():\n",
    "    for k in data.loc[(data['ID'] == i) & (data['Session'] == j), 'DrinkNumber'].unique():\n",
    "      if data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0] > 0:\n",
    "        drinklength = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"DrinkLength\"].unique()[0]\n",
    "      else:\n",
    "        break\n",
    "      #filter down the lengths to find the peaks so your not peak finding past the end of the drink...\n",
    "      data_new = data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= 0) & (data['TimeFromDrinkRound'] <= drinklength)].copy()\n",
    "\n",
    "      xdata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), 'TimeFromDrinkRound'])\n",
    "      ydata = np.array(data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k), var])\n",
    "\n",
    "      max_value =  ydata.max()\n",
    "      time_to_peak = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrinkRound'].iloc[0]\n",
    "\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"time_to_peak\"] = data_new.loc[(data_new['ID'] == i) & (data_new['Session'] == j) & (data_new['DrinkNumber'] == k) &  (data_new[var] == ydata.max()), 'TimeFromDrink'].iloc[0]/1000\n",
    "\n",
    "      xdata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), \"TimeFromDrinkRound\"])\n",
    "      ydata = np.array(data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k) & (data['TimeFromDrinkRound'] >= time_to_peak) & (data['TimeFromDrinkRound'] <= drinklength), var])\n",
    "\n",
    "      area = scipy.integrate.trapezoid(ydata, xdata)\n",
    "      # Print the decay coefficient\n",
    "      data.loc[(data['ID'] == i) & (data['Session'] == j) & (data['DrinkNumber'] == k), \"AUC\"] = area\n",
    "      \n",
    "\n",
    "data = data.groupby(['ID','Session','DrinkNumber','Sex','Fluid']).max()\n",
    "data = data.reset_index()\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "data = data.sort_values(by=\"Week\", ascending = True)\n",
    "\n",
    "sns.stripplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", hue_order=['Male','Female'], alpha = 0.5, size = 1,  dodge= True, jitter=True,)\n",
    "sns.pointplot(data = data, x = 'Fluid', y = var, hue = \"Sex\", errorbar = 'se', hue_order=['Male','Female'], scale = 0.5, errwidth = 0.5, dodge=.4, join = False)\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\n Peak Amplitude (Max zscore ΔF/F)\", size = 5)\n",
    "plt.xlabel(\" \", size = 5)\n",
    "\n",
    "plt.ylim(-3,10)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:], labels[2:], frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, handletextpad= 0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Right_MAX.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv = var, between=['Sex','Fluid'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a291859",
   "metadata": {},
   "outputs": [],
   "source": [
    "pg.pairwise_tests(dv = var, between=['Sex','Fluid'], data = data, padjust='sidak')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07d9c45c",
   "metadata": {},
   "source": [
    "## Figure 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1157b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# by sex\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth*0.75\n",
    "figheight = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "handles = []\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['ID'] == \"P7\") & (FP_FINAL['Session'] == 17)]\n",
    "adjust = data['Timestamp'].min()\n",
    "\n",
    "for i in data.loc[data['DrinkNumber'].notnull(), \"DrinkNumber\"].unique():\n",
    "    linetime = data.loc[data['SYNC_Time'] < data.loc[(data['TimeFromDrink'] == 0) & (data['DrinkNumber'] == i), \"SYNC_Time\"].iloc[0], 'Timestamp'].max()\n",
    "    plt.vlines(x = linetime - adjust , ymin = -1.85, ymax = 8.15, linestyles = 'dashed', color = 'black', alpha = 0.5, linewidth = 0.5)\n",
    "\n",
    "data['TimestampPRETYY'] = data['Timestamp'] - data['Timestamp'].min()\n",
    "\n",
    "sns.lineplot(x = \"TimestampPRETYY\", y = 'LeftSignal_dFF_zscore', data = data.loc[data['Timestamp'] > 1000])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:] + handles[:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('WholeSessionLeft.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85070876",
   "metadata": {},
   "outputs": [],
   "source": [
    "# by sex\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth*0.75\n",
    "figheight = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "handles = []\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['ID'] == \"P7\") & (FP_FINAL['Session'] == 17)]\n",
    "adjust = data['Timestamp'].min()\n",
    "\n",
    "for i in data.loc[data['DrinkNumber'].notnull(), \"DrinkNumber\"].unique():\n",
    "    linetime = data.loc[data['SYNC_Time'] < data.loc[(data['TimeFromDrink'] == 0) & (data['DrinkNumber'] == i), \"SYNC_Time\"].iloc[0], 'Timestamp'].max()\n",
    "    plt.vlines(x = linetime - adjust , ymin = -1.85, ymax = 8.15, linestyles = 'dashed', color = 'black', alpha = 0.5, linewidth = 0.5)\n",
    "\n",
    "data['TimestampPRETYY'] = data['Timestamp'] - data['Timestamp'].min()\n",
    "\n",
    "sns.lineplot(x = \"TimestampPRETYY\", y = 'RightSignal_dFF_zscore', data = data.loc[data['Timestamp'] > 1000], color = '#ff7f0e')\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:] + handles[:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('WholeSessionRIGHT.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "108dab13",
   "metadata": {},
   "outputs": [],
   "source": [
    "cocktailmenu = pd.DataFrame()\n",
    "\n",
    "for i in FP_FINAL['ID'].unique():\n",
    "    for j in FP_FINAL.loc[(FP_FINAL['ID'] == i), 'Session'].unique():\n",
    "        drinklist = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j) & (FP_FINAL['DrinkNumber'].notnull()), 'DrinkNumber'].unique()\n",
    "        menu = pd.DataFrame(data=drinklist)\n",
    "        menu['ID'] = i\n",
    "        menu['Session'] = j\n",
    "        menu['Sex'] = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j), \"Sex\"].unique()[0]\n",
    "        menu['Fluid'] = FP_FINAL.loc[(FP_FINAL['ID'] == i) & (FP_FINAL['Session'] == j), \"Fluid\"].unique()[0]\n",
    "            \n",
    "        cocktailmenu = pd.concat([cocktailmenu, menu])\n",
    "\n",
    "cocktailmenu = cocktailmenu.rename(columns={0: 'DrinkNumber'})\n",
    "\n",
    "pagewidth = 180/25.4\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth*0.75\n",
    "figheight = figwidth/2\n",
    "figwidth = 2\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "sns.histplot(data=cocktailmenu, x='DrinkNumber')\n",
    "#ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.ylabel(\"Count\", size = 5)\n",
    "plt.xlabel(\"Drinks\", size = 5)\n",
    "\n",
    "plt.ylim([0,250])\n",
    "\n",
    "plt.xticks([0, cocktailmenu['DrinkNumber'].quantile(.25), cocktailmenu['DrinkNumber'].quantile(.50), cocktailmenu['DrinkNumber'].quantile(.75), cocktailmenu['DrinkNumber'].max()])\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('DrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b28d5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth*0.75\n",
    "figheight = figwidth/2\n",
    "figwidth = 2\n",
    "\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "sns.histplot(data=FP_FINAL.loc[FP_FINAL['DrinkLength'].notnull(), \"DrinkLength\"].unique())\n",
    "\n",
    "plt.ylabel(\"Count\", size = 5)\n",
    "plt.xlabel(\"Drink Length (s)\", size = 5)\n",
    "\n",
    "plt.ylim([0,250])\n",
    "\n",
    "plt.xticks([0, FP_FINAL['DrinkLength'].quantile(.25), FP_FINAL['DrinkLength'].quantile(.50), FP_FINAL['DrinkLength'].quantile(.75), FP_FINAL['DrinkLength'].max()], rotation=45, ha=\"right\")\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('DrinkLengthDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d8c4c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# by sex\n",
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth*0.75\n",
    "figheight = figwidth/2\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "handles = []\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] < cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[FP_FINAL['DrinkNumber'].isnull() == False]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "\n",
    "#combine data columns into one column\n",
    "data['Sex-Fluid'] = data['Sex'] + ' - ' + data[\"Fluid\"]\n",
    "data = pd.melt(data, id_vars=['ID','Session','DrinkNumber','Sex','Fluid','Sex-Fluid','TimeFromDrinkRound','DrinkNumDist'], value_vars=['RightSignal_dFF_zscore_filtered','LeftSignal_dFF_zscore_filtered'])\n",
    "data['Sex-Fluid-Side'] = data['Sex-Fluid'] + '-' + data['variable']\n",
    "data = data.reset_index()\n",
    "\n",
    "plt.hlines(y = 0 , xmin = -5, xmax = 5, linestyles = 'dashed', color = 'black', alpha = 0.5, linewidth = 0.5)\n",
    "plt.vlines(x = 0 , ymin = -0.95, ymax = 1.45, linestyles = 'dashed', color = 'black', alpha = 0.5, linewidth = 0.5)\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = \"value\", \n",
    "             hue = 'DrinkNumDist',\n",
    "             style = 'variable',\n",
    "             data = data.loc[data['Sex'] == \"Male\"],\n",
    "             #hue_order = ['Male - Water','Female - Water','Male - Alcohol', 'Female - Alcohol'],\n",
    "             style_order=['LeftSignal_dFF_zscore_filtered','RightSignal_dFF_zscore_filtered'],\n",
    "             estimator=np.mean, errorbar='se', size = 1)\n",
    "\n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"zscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Bout Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('RightVLeft.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv='value', between=['DrinkNumDist','Fluid','TimeFromDrinkRound','variable'], data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d24a155c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b21a6680",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] < cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False)]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('LeftMaleWaterDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864704f9",
   "metadata": {},
   "source": [
    "## Figure 6 - Figure Supplement 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dbf1a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] < cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftMaleWaterDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7756a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.25,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightMaleWaterDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf990a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] < cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,2)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftMaleAlcoholDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44f4ab4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "posthoc = pg.pairwise_tests(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, padjust='sidak')\n",
    "posthoc.loc[(posthoc['Contrast'] == \"DrinkNumnDistDUMMY\") & (posthoc['p-unc'] < 0.05)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "525f3dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] < cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightMaleAlcoholDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0f06ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] < cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftFEMaleWaterDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85034ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightFEMaleWaterDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a053f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] < cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftFEMaleAlcoholDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b43f8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.25), \"DrinkNumDist\"] = f\"Drinks 1 - {round(cocktailmenu['DrinkNumber'].quantile(.25))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.25)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.5)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.25))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.5)))}\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.5)) & (FP_FINAL['DrinkNumber'] <= cocktailmenu['DrinkNumber'].quantile(.75)), \"DrinkNumDist\"] = f\"Drinks {(round(cocktailmenu['DrinkNumber'].quantile(.5))+1)} - {(round(cocktailmenu['DrinkNumber'].quantile(.75)))}\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkNumber'] > cocktailmenu['DrinkNumber'].quantile(.75), \"DrinkNumDist\"] = f\"Drinks {round(cocktailmenu['DrinkNumber'].quantile(.75)+1)}+\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "data = data.loc[data['DrinkNumDist'].notnull()]\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkNumDist',\n",
    "             hue_order=['Drinks 1 - 5','Drinks 6 - 9','Drinks 10 - 17','Drinks 18+'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightFEMaleAlcoholDrink_NUM_Dist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "data['DrinkNumnDistDUMMY'] = pd.factorize(data['DrinkNumDist'])[0]\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkNumnDistDUMMY','TimeFromDrinkRound'], subject='ID', data = data, detailed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c525cc24",
   "metadata": {},
   "source": [
    "## Figure 6 - Figure Supplement 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9984d4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title = 'Drink Duration', title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftMaleWaterDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkDist','TimeFromDrinkRound'], subject='ID', data = data.loc[data['DrinkDist'].notnull()], detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62bfa96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title = 'Drink Duration', title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftFEMaleWaterDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkDist','TimeFromDrinkRound'], subject='ID', data = data.loc[data['DrinkDist'].notnull()], detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e2ab930",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title = 'Drink Duration', title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftMaleAlcoholDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkDist','TimeFromDrinkRound'], subject='ID', data = data.loc[data['DrinkDist'].notnull()], detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cb58112",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"LeftSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Left AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title = 'Drink Duration', title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('LeftFEMaleAlcoholDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv=var, between=['DrinkDist','TimeFromDrinkRound'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dd9fe49",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title = 'Drink Duration', title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "#lt.savefig('RightMaleAlcoholDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv=var, between=['DrinkDist','TimeFromDrinkRound'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9971bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Alcohol\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5, title = 'Drink Duration', title_fontsize = 5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightFEMaleAlcoholDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.anova(dv=var, between=['DrinkDist','TimeFromDrinkRound'], data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b02e1c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Male\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightMaleWaterDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkDist','TimeFromDrinkRound'], subject='ID', data = data.loc[data['DrinkDist'].notnull()], detailed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69b28ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pagewidth = 180/25.4\n",
    "figwidth = pagewidth/3\n",
    "figheight = figwidth\n",
    "figwidth = figwidth + (figwidth/2)\n",
    "f, ax = plt.subplots(figsize=(figwidth,figheight))\n",
    "sns.despine()\n",
    "\n",
    "var = \"RightSignal_dFF_zscore_filtered\"\n",
    "\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.25), \"DrinkDist\"] = f\"0 - {FP_FINAL['DrinkLength'].quantile(.25).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.25)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.5)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.25).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.5).round(3)} (s)\"\n",
    "FP_FINAL.loc[(FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.5)) & (FP_FINAL['DrinkLength'] <= FP_FINAL['DrinkLength'].quantile(.75)), \"DrinkDist\"] = f\"{(FP_FINAL['DrinkLength'].quantile(.5).round(3)+0.001)} - {FP_FINAL['DrinkLength'].quantile(.75).round(3)} (s)\"\n",
    "FP_FINAL.loc[FP_FINAL['DrinkLength'] > FP_FINAL['DrinkLength'].quantile(.75), \"DrinkDist\"] = f\"{((FP_FINAL['DrinkLength'].quantile(.75).round(3))+0.001).round(3)}+ (s)\"\n",
    "\n",
    "data = FP_FINAL.loc[(FP_FINAL['DrinkNumber'].isnull() == False) &\n",
    "                    (FP_FINAL['Fluid'] == \"Water\") &\n",
    "                    (FP_FINAL['Sex'] == \"Female\")]\n",
    "\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 7), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 8), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 10), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P8\") & (data['Session'] == 16), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P6\") & (data['Session'] == 18), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 20), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P9\") & (data['Session'] == 24), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P7\") & (data['Session'] == 30), 'LeftSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P1\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data.loc[(data['ID'] == \"P13\") & (data['Session'] == 25), 'RightSignal_dFF_zscore_filtered'] = np.nan\n",
    "data = data.dropna(subset=[var])\n",
    "\n",
    "sns.lineplot(x = \"TimeFromDrinkRound\", y = var,\n",
    "             hue = 'DrinkDist',\n",
    "             hue_order=['0 - 0.434 (s)','0.435 - 1.421 (s)','1.422 - 3.683 (s)','3.684+ (s)'],\n",
    "             errorbar='se',\n",
    "             data = data)\n",
    "             \n",
    "plt.ylim(-1.2,1.5)\n",
    "plt.xticks([-5,-4,-3,-2,-1,0,1,2,3,4,5])\n",
    "\n",
    "plt.ylabel(\"Right AIC Inputs\\nzscore ΔF/F\", size = 5)\n",
    "plt.xlabel(\"Time from Drink Start (s)\", size = 5)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "for _,s in ax.spines.items():\n",
    "    s.set_linewidth(0.5)\n",
    "    \n",
    "ax.tick_params(width=0.5, length = 2)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = (handles[:5] + handles[6:])\n",
    "ax.legend(handles = handles, frameon=False, loc = 'upper left', prop = {\"size\":5}, markerscale = 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('RightFEMaleWaterDrinkDist.pdf', transparent = True, dpi = 1200)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "pg.rm_anova(dv=var, within=['DrinkDist','TimeFromDrinkRound'], subject='ID', data = data.loc[data['DrinkDist'].notnull()], detailed=True)"
   ]
  }
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