1-eda-brief.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1-eda-brief.ipynb\n",
    "\n",
    "Exploratory Data Analysis using conda environment `pycaret3`, accompanying the manuscript:<br>\n",
    "\"Assessment of self-reported executive function in irritable bowel syndrome using a machine learning framework\"<br> \n",
    "(overleaf: https://www.overleaf.com/project/641372a5904da63d1548c286)\n",
    "\n",
    "Authors:<br>\n",
    "Astri J Lundervold, Eline M Hillestad, Julie Billing, Tina E  Johnsen, Gülen A Lied, Elisabeth Steinsvik, Trygve Hausken, Birgitte Berentsen and Arvid Lundervold.\n",
    "\n",
    "Arvid Lundervold 2023-03-27<br>\n",
    "Last updatd: 2023-03-29\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import platform\n",
    "from itertools import repeat\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy.stats import gaussian_kde\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from numpy import mean\n",
    "from numpy import var\n",
    "from math import sqrt\n",
    "import statsmodels\n",
    "from statsmodels.stats.weightstats import ttest_ind  # Welsch t-test\n",
    "import statsmodels.api as sm\n",
    "import sklearn\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "import pycaret\n",
    "from os.path import expanduser\n",
    "home = expanduser(\"~\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy: 1.23.5\n",
      "Scipy: 1.9.3\n",
      "Pandas: 1.5.3\n",
      "Statsmodels: 0.13.5\n",
      "Seaborn: 0.12.2\n",
      "Scikit-learn: 1.2.2\n",
      "Pycaret: 3.0.0\n"
     ]
    }
   ],
   "source": [
    "print('Numpy:', np.__version__)\n",
    "print('Scipy:', scipy.__version__)\n",
    "print('Pandas:', pd.__version__)\n",
    "print('Statsmodels:', statsmodels.__version__)\n",
    "print('Seaborn:', sns.__version__)\n",
    "print('Scikit-learn:', sklearn.__version__)\n",
    "print('Pycaret:', pycaret.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, you are running on MacOS (Darwin Kernel Version 22.3.0: Mon Jan 30 20:42:11 PST 2023; root:xnu-8792.81.3~2/RELEASE_X86_64)\n"
     ]
    }
   ],
   "source": [
    "if platform.system() == 'Darwin':\n",
    "    print(f'OK, you are running on MacOS ({platform.version()})')\n",
    "    OVERLEAF_dir = f'{home}/Dropbox/Apps/Overleaf/Executive_function_in_IBS'\n",
    "    TREE = '/usr/local/bin/tree -L 1'          # MacOS (brew install tree)\n",
    "    MacOS = True\n",
    "if platform.system() == 'Linux':\n",
    "    print(f'OK, you are running on Linux ({platform.version()})')\n",
    "    OVERLEAF_dir = f'/media/arvid/prj2/Dropbox/Apps/Overleaf/Executive_function_in_IBS'\n",
    "    TREE = '/usr/bin/tree -L 4'\n",
    "    MacOS = False\n",
    "if platform.system() == 'Windows':\n",
    "    print(f'OK, but consider to install WSL for Windows10 since you are running on {platform.system()}')\n",
    "    print('Check https://docs.microsoft.com/en-us/windows/wsl/install-win10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read  .csv file into Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(f'./data/questionnaire_BRIEF_and_more_66x18.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BG_ID', 'Group', 'Gender', 'Age', 'Inhibition', 'Shifting',\n",
       "       'Emotional_control', 'Self_monitoring', 'Initiate', 'Working_memory',\n",
       "       'Planning', 'Task_monitoring', 'Organization', 'HADS_anx', 'HADS_dep',\n",
       "       'HADS_tot', 'IBS-SSS', 'RBANS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_length = max(len(item) for item in list(df.columns))\n",
    "max_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_cols = ['BG_ID', 'Group', 'Gender', 'Age', 'Inhibition', 'Shifting',\n",
    "       'Emotional_control', 'Self_monitoring', 'Initiate', 'Working_memory',\n",
    "       'Planning', 'Task_monitoring', 'Organization']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1a = df.copy()\n",
    "df1 = df1a[df1_cols]\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57, 18)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2a = df.dropna()\n",
    "#col_list = list(df2a)\n",
    "#col_list[-2], col_list[-1] = col_list[-1], col_list[-2]\n",
    "#df2b = df2a[col_list]\n",
    "df2 = df2a\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Gentle Introduction to Effect Size Measures in Python<br>\n",
    "\n",
    "- https://machinelearningmastery.com/effect-size-measures-in-python\n",
    "- https://www.analyticsvidhya.com/blog/2022/08/statistical-effect-size-and-python-implementation\n",
    "- https://stats.stackexchange.com/questions/153450/how-do-i-calculate-the-effect-size-of-an-unequal-sample-size-independent-t-test\n",
    "- https://grabngoinfo.com/power-analysis-for-sample-size-using-python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def interpret_cohens_d(cohens_d):\n",
    "    \"\"\"\n",
    "    Determines text interpretation of effect size given Cohen's d value\n",
    "\n",
    "    :param cohens_d: float of Cohen's d value\n",
    "    :returns: effect_size_interpretation: adjective to describe magnitude of effect size\n",
    "    \"\"\"\n",
    "    if 0<=cohens_d<0.1:\n",
    "        effect_size_interpretation = \"Very Small\"\n",
    "    elif 0.1<=cohens_d<0.35:\n",
    "        effect_size_interpretation = \"Small\"\n",
    "    elif 0.35<=cohens_d<0.65:\n",
    "        effect_size_interpretation = \"Medium\"\n",
    "    elif 0.65<=cohens_d<0.9:\n",
    "        effect_size_interpretation = \"Large\"\n",
    "    elif cohens_d >= 0.9:\n",
    "        effect_size_interpretation = \"Very Large\"\n",
    "    return effect_size_interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cohen_d(values_1, values_2):\n",
    "    \"\"\"\n",
    "    Function to calculate Cohen's d for independent samples\n",
    "\n",
    "    :param values_1: array of values in group 1\n",
    "    :param values_2: array of values in group 2 \n",
    "    :returns: d:  Cohen's d\n",
    "    \"\"\"\n",
    "    x = values_1\n",
    "    y = values_2\n",
    "\n",
    "    nx = len(x)\n",
    "    ny = len(y)\n",
    "    dof = nx + ny - 2\n",
    "    \n",
    "    # Calculate the pooled standard deviation\n",
    "    pooled_std = np.sqrt(((nx - 1) * np.std(x, ddof=1) ** 2 + (ny - 1) * np.std(y, ddof=1) ** 2) / dof)\n",
    "    \n",
    "    # Calculate Cohen's d\n",
    "    d = (np.mean(x) - np.mean(y)) / pooled_std\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def visualize_variable_dist_IBS_HC(df_adhoc_IBS, df_adhoc_HC, var_name, var_print, cohens_d_function):\n",
    "\n",
    "\n",
    "   arr_IBS = df_adhoc_IBS[var_name].values\n",
    "   len_IBS = len(arr_IBS)\n",
    "   mean_arr_IBS = np.mean(arr_IBS)\n",
    "\n",
    "   arr_HC = df_adhoc_HC[var_name].values  \n",
    "   len_HC = len(arr_HC)\n",
    "   mean_arr_HC = np.mean(arr_HC)\n",
    "\n",
    "   d = '{:.2f}'.format(np.round(cohens_d_function(arr_IBS, arr_HC), 2))\n",
    "\n",
    "   grp_IBS = list(repeat('IBS', len_IBS))\n",
    "   grp_HC = list(repeat('HC', len_HC))\n",
    "\n",
    "   df_IBS = pd.DataFrame({var_name: arr_IBS, 'Group':  grp_IBS })\n",
    "   df_HC = pd.DataFrame({var_name: arr_HC, 'Group': grp_HC})\n",
    "\n",
    "   df_IBS_HC = pd.concat([df_IBS , df_HC ], ignore_index=True, sort=False)\n",
    "\n",
    "\n",
    "   sns.displot(df_IBS_HC, x=var_name, kind=\"kde\", hue=\"Group\", rug=True, fill=True, aspect=2.0, multiple=\"layer\") # \"stack\",\n",
    "   #           height=8, aspect=1.7)\n",
    "   plt.ylabel(\"Density\")\n",
    "   plt.xlabel(var_print)\n",
    "   plt.title(f\"Distribution of {var_print} in the IBS (n={len_IBS}) and the HC (n={len_HC}) groups; Cohen's d = {d}\", fontsize=16)\n",
    "\n",
    "   plt.axvline(mean_arr_IBS, color=\"black\", linestyle=\"dashed\")\n",
    "   #plt.vlines(mean_arr_IBS, 0.0, 0.001, color=\"blue\", linestyle=\"dashed\")\n",
    "   plt.axvline(mean_arr_HC, color=\"black\", linestyle=\"dashed\")\n",
    "   #plt.vlines(mean_arr_HC, 0.0, 0.001, color=\"orange\", linestyle=\"dashed\")\n",
    "\n",
    "   #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def add_blanks(s, length):\n",
    "    \"\"\"Add blanks to a string to achieve the desired length\"\"\"\n",
    "    if len(s) >= length:\n",
    "        return s\n",
    "    else:\n",
    "        num_blanks = length - len(s)\n",
    "        return s + ' ' * num_blanks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_underscore(string):\n",
    "    return string.replace('_', '\\_');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_latex_table_IBS_HC(OVERLEAF, OVERLEAF_dir, table_tex, clin_scale_list, df_cat_IBS, df_cat_HC, cohend_func, length):\n",
    "\n",
    "\n",
    "    OVERLEAF = OVERLEAF # True or False\n",
    "\n",
    "    stdout = sys.stdout\n",
    "\n",
    "    if OVERLEAF:\n",
    "        print(f'Writing to {OVERLEAF_dir}/{table_tex} for synching of LaTeX table!')\n",
    "        # Open the file in write mode\n",
    "        sys.stdout = open(f'{OVERLEAF_dir}/{table_tex}', 'w')\n",
    "    else:\n",
    "        sys.stdout = stdout\n",
    "        \n",
    "    print('\\\\begin{tabular}{l|c|c|c|c|}')\n",
    "    print(f\"Clinical Scale      &    Mean     &     SD     & p-value & Cohen's d \\\\\\\\\")\n",
    "    print(f'                    &  IBS / HC   &  IBS / HC  &         &            \\\\\\\\')\n",
    "    print(f'\\hline')\n",
    "    for i, n in enumerate(clin_scale_list):\n",
    "       m = add_blanks(n, length)\n",
    "    \n",
    "       mu_IBS = np.round(df_cat_IBS[n].mean(), 1)\n",
    "       mu_HC = np.round(df_cat_HC[n].mean(), 1) \n",
    "    \n",
    "       s_IBS = np.round(df_cat_IBS[n].std(), 1)\n",
    "       s_HC = np.round(df_cat_HC[n].std(), 1) \n",
    "    \n",
    "       arr_IBS = df_cat_IBS[n].values\n",
    "       arr_HC = df_cat_HC[n].values \n",
    "    \n",
    "       # Perform the t-test using Welch's t-test which assumes unequal variances\n",
    "       results = ttest_ind(arr_IBS, arr_HC, usevar='unequal')\n",
    "       p_val = np.round(results[1], 3)\n",
    "       if results[1] < 0.001:\n",
    "          p_val_print = '$<$ 0.001'\n",
    "       else:\n",
    "          p_val_print = '{:.3f}'.format(p_val)\n",
    "        \n",
    "       # The Cohen's d effect size measure is used to determine the standardized difference between the means of two groups\n",
    "       d = '{:.2f}'.format(np.round(cohen_d(arr_IBS, arr_HC), 2))\n",
    "\n",
    "       print(f'{transform_underscore(m)}   & {mu_IBS} / {mu_HC} & {s_IBS} / {s_HC}  &  {p_val_print}    &   {d}      \\\\\\\\')    \n",
    "\n",
    "    print('\\\\end{tabular}')\n",
    "    print(f'% n_IBS: {len(df_cat_IBS)}, n_HC: {len(df_cat_HC)}')\n",
    "\n",
    "    sys.stdout = stdout\n",
    "    if OVERLEAF:\n",
    "       print('Latex table wiritten to disk')\n",
    "    OVERLEAF = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check some of the features in the data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BG_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HC</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBS</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BG_ID\n",
       "Group       \n",
       "HC        22\n",
       "IBS       44"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Group','BG_ID']].groupby('Group').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BG_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BG_ID\n",
       "Gender       \n",
       "F          49\n",
       "M          17"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Gender','BG_ID']].groupby('Gender').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>BG_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HC</th>\n",
       "      <th>F</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBS</th>\n",
       "      <th>F</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              BG_ID\n",
       "Group Gender       \n",
       "HC    F          15\n",
       "      M           7\n",
       "IBS   F          34\n",
       "      M          10"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Group', 'Gender', 'BG_ID']].groupby(['Group', 'Gender']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "variable_name = 'Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Estimate the Age density using a Gaussian kernel\n",
    "data = np.asarray(list(df['Age'].values))\n",
    "kde = gaussian_kde(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set up the binning for the density estimate\n",
    "x_min = data.min()\n",
    "x_max = data.max()\n",
    "num_bins = 100\n",
    "x_range = np.linspace(x_min, x_max, num_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Evaluate the density estimate at each bin\n",
    "density_estimate = kde.evaluate(x_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_range, density_estimate)\n",
    "# multiple lines with varying ymin and ymax\n",
    "plt.vlines(x=list(x_range), ymin=0 , ymax=list(density_estimate), colors='blue')\n",
    "plt.title('Age distribution (kde)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1062.24x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.displot(df, x='Age', hue='Gender', col='Group', kind='kde', fill=True, rug=True, alpha=0.4, multiple=\"layer\") # layer\")\n",
    "plt.savefig(f\"{OVERLEAF_dir}/Figs/Age_dist_IBS_and_HC_in_M_vs_F.pdf\")\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1072.12x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.displot(df, x='Age', hue='Group', col='Gender', kind='kde', fill=True, rug=True, alpha=0.4, multiple=\"layer\")\n",
    "#           height=8, aspect=1.7) # layer\")\n",
    "plt.savefig(f\"{OVERLEAF_dir}/Figs/Age_dist_M_and_F_in_IBS_vs_HC.pdf\")\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th colspan=\"2\" halign=\"left\">HC</th>\n",
       "      <th colspan=\"2\" halign=\"left\">IBS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BG_ID</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inhibition</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shifting</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emotional_control</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self_monitoring</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Initiate</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Working_memory</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Planning</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Task_monitoring</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Organization</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_anx</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_dep</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_tot</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBS-SSS</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBANS</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Group              HC    IBS    \n",
       "Gender              F  M   F   M\n",
       "BG_ID              15  7  34  10\n",
       "Age                15  7  34  10\n",
       "Inhibition         15  7  34  10\n",
       "Shifting           15  7  34  10\n",
       "Emotional_control  15  7  34  10\n",
       "Self_monitoring    15  7  34  10\n",
       "Initiate           15  7  34  10\n",
       "Working_memory     15  7  34  10\n",
       "Planning           15  7  34  10\n",
       "Task_monitoring    15  7  34  10\n",
       "Organization       15  7  34  10\n",
       "HADS_anx           11  6  30  10\n",
       "HADS_dep           11  6  30  10\n",
       "HADS_tot           11  6  30  10\n",
       "IBS-SSS            11  6  30  10\n",
       "RBANS              11  6  30  10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_IBS = df[df['Group']=='IBS']\n",
    "df_HC = df[df['Group']=='HC']\n",
    "df_combined = pd.concat([df_IBS, df_HC], ignore_index=True)\n",
    "\n",
    "df_combinded = pd.concat([df_IBS, df_HC], ignore_index=True)\n",
    "\n",
    "df_combined.groupby(['Group', 'Gender']).count().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age characteristics by Group and Gender:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HC</th>\n",
       "      <th>F</th>\n",
       "      <td>15.0</td>\n",
       "      <td>36.60</td>\n",
       "      <td>11.54</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>7.0</td>\n",
       "      <td>35.14</td>\n",
       "      <td>13.78</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBS</th>\n",
       "      <th>F</th>\n",
       "      <td>34.0</td>\n",
       "      <td>34.59</td>\n",
       "      <td>11.03</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.25</td>\n",
       "      <td>34.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>10.0</td>\n",
       "      <td>36.50</td>\n",
       "      <td>6.85</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.50</td>\n",
       "      <td>36.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count   mean    std   min    25%   50%   75%   max\n",
       "Group Gender                                                    \n",
       "HC    F        15.0  36.60  11.54  22.0  26.00  36.0  46.0  54.0\n",
       "      M         7.0  35.14  13.78  21.0  24.00  33.0  43.0  58.0\n",
       "IBS   F        34.0  34.59  11.03  19.0  25.25  34.5  41.0  61.0\n",
       "      M        10.0  36.50   6.85  28.0  31.50  36.5  41.0  49.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Age characteristics by Group and Gender:')\n",
    "np.round(df_combined.groupby(['Group', 'Gender']).Age.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th colspan=\"2\" halign=\"left\">HC</th>\n",
       "      <th colspan=\"2\" halign=\"left\">IBS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.0</th>\n",
       "      <td>1</td>\n",
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       "      <th>30.0</th>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31.0</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
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       "      <th>44.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>47.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>49.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Group  HC    IBS   \n",
       "Gender  F  M   F  M\n",
       "Age                \n",
       "19.0    0  0   2  0\n",
       "21.0    0  1   1  0\n",
       "22.0    1  1   2  0\n",
       "23.0    2  0   2  0\n",
       "24.0    1  0   1  0\n",
       "25.0    0  0   1  0\n",
       "26.0    0  1   1  0\n",
       "28.0    1  0   1  2\n",
       "30.0    1  0   4  0\n",
       "31.0    1  0   0  1\n",
       "32.0    0  0   1  0\n",
       "33.0    0  1   0  1\n",
       "34.0    0  0   1  0\n",
       "35.0    0  0   1  1\n",
       "36.0    1  0   1  0\n",
       "37.0    0  0   3  0\n",
       "38.0    0  0   0  2\n",
       "39.0    1  1   0  0\n",
       "40.0    0  0   2  0\n",
       "41.0    0  0   3  0\n",
       "42.0    0  0   1  1\n",
       "43.0    0  0   1  1\n",
       "44.0    0  0   1  0\n",
       "46.0    3  0   0  0\n",
       "47.0    0  1   0  0\n",
       "48.0    1  0   1  0\n",
       "49.0    0  0   0  1\n",
       "53.0    1  0   0  0\n",
       "54.0    1  0   0  0\n",
       "57.0    0  0   1  0\n",
       "58.0    0  1   1  0\n",
       "61.0    0  0   1  0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crosstab = pd.crosstab(index=[df['Group'], df['Gender']], columns=[df['Age']]).T # [df_age_kde['x_range'], df_age_kde['density_estimate']]).T \n",
    "print(len(df_crosstab))\n",
    "#df_crosstab['x'] = df_crosstab.index\n",
    "\n",
    "#my_list = list(df_age_kde['x_range'])\n",
    "#sub_list = list(df_crosstab['x'])\n",
    "\n",
    "#indexes = [i for i, x in enumerate(my_list)] \n",
    "\n",
    "\n",
    "#df_crosstab['y'] = df_age_kde['density_estimate']\n",
    "df_crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_IBS_M = df_IBS[df_IBS['Gender']=='M']\n",
    "df_IBS_M['Group_Gender']= list(repeat('IBS_M', len(df_IBS_M)))\n",
    "\n",
    "df_IBS_F = df_IBS[df_IBS['Gender']=='F']\n",
    "df_IBS_F['Group_Gender']= list(repeat('IBS_F', len(df_IBS_F)))\n",
    "\n",
    "df_HC_M = df_HC[df_HC['Gender']=='M']\n",
    "df_HC_M['Group_Gender']= list(repeat('HC_M', len(df_HC_M)))\n",
    "\n",
    "\n",
    "df_HC_F = df_HC[df_HC['Gender']=='F']\n",
    "df_HC_F['Group_Gender']= list(repeat('HC_F', len(df_HC_F)))\n",
    "\n",
    "df_Gender_combinded = pd.concat([df_IBS_M, df_IBS_F, df_HC_M, df_HC_M], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "sns.histplot(\n",
    "    data=df_combined,\n",
    "    x=variable_name,  # Replace 'Value' with the column you want to plot\n",
    "    hue='Gender',\n",
    "    multiple='stack',\n",
    "    palette='Set1',  # Choose a color palette\n",
    "    edgecolor='black',\n",
    "    bins=20,\n",
    "    kde=True,\n",
    "    element='step',\n",
    "    stat='density',  # Use 'density' to normalize histograms\n",
    ")\n",
    "\n",
    "plt.title('Distribution of Values for IBS and HC populations by Gender')\n",
    "plt.xlabel(variable_name)  # Replace 'Value' with the column name you want to plot\n",
    "plt.ylabel('Density')\n",
    "\n",
    "# Add legend\n",
    "legend_labels = ['IBS Male', 'IBS Female', 'HC Male', 'HC Female']\n",
    "plt.legend(title='Group and Gender', labels=legend_labels, loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create a histplot with hue based on gender and separate datasets for IBS and HC\n",
    "sns.histplot(data=df_combined, x=variable_name, hue='Gender', multiple='dodge', kde=True, element='step', stat='density', common_norm=False, palette='viridis', alpha=0.7, bins=20, linewidth=1)\n",
    "\n",
    "# Customize the plot\n",
    "plt.title(f'Distribution of {variable_name} for IBS and HC populations by Gender')\n",
    "plt.xlabel(variable_name)\n",
    "plt.ylabel('Density')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BRIEF variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'Inhibition',\n",
       " 'Shifting',\n",
       " 'Emotional_control',\n",
       " 'Self_monitoring',\n",
       " 'Initiate',\n",
       " 'Working_memory',\n",
       " 'Planning',\n",
       " 'Task_monitoring',\n",
       " 'Organization']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# brief_scale_list = clin_scale_list_1\n",
    "brief_scale_list = ['Age', 'Inhibition', 'Shifting', 'Emotional_control', 'Self_monitoring', 'Initiate', 'Working_memory',\n",
    "       'Planning', 'Task_monitoring', 'Organization']\n",
    "brief_scale_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 13)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df3 = df_cat_1 = df_cat_1_dropna\n",
    "#df3 = df2[['BG_ID'] + ['Gender'] + ['Group'] + brief_scale_list]\n",
    "df3 = df[['BG_ID'] + ['Gender'] + ['Group'] + brief_scale_list]\n",
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_IBS: 44, n_HC: 22\n"
     ]
    }
   ],
   "source": [
    "df3_IBS = df3[df3['Group'] == 'IBS']\n",
    "df3_HC = df3[df3['Group'] == 'HC']\n",
    "\n",
    "n_IBS = len(df3_IBS)\n",
    "n_HC = len(df3_HC)\n",
    "\n",
    "print(f'n_IBS: {n_IBS}, n_HC: {n_HC}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a print to file of a $\\LaTeX$ table on BRIEF variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "Writing to /Users/arvid/Dropbox/Apps/Overleaf/Executive_function_in_IBS/table_1_new.tex for synching of LaTeX table!\n",
      "Latex table wiritten to disk\n"
     ]
    }
   ],
   "source": [
    "OVERLEAF = True\n",
    "table_tex = 'table_1_new.tex'\n",
    "cohend_func = cohen_d\n",
    "\n",
    "print(OVERLEAF)\n",
    "make_latex_table_IBS_HC(OVERLEAF, OVERLEAF_dir, table_tex, brief_scale_list, df3_IBS, df3_HC, cohend_func, max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57, 7)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df[['BG_ID', 'Gender', 'Group', 'HADS_anx', 'HADS_dep', 'HADS_tot', 'IBS-SSS']]\n",
    "df5 = df4.dropna()\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_IBS: 40, n_HC: 17\n"
     ]
    }
   ],
   "source": [
    "df5_IBS = df5[df5['Group'] == 'IBS']\n",
    "df5_HC = df5[df5['Group'] == 'HC']\n",
    "\n",
    "m_IBS = len(df5_IBS)\n",
    "m_HC = len(df5_HC)\n",
    "\n",
    "print(f'n_IBS: {m_IBS}, n_HC: {m_HC}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a print to file of a $\\LaTeX$ table on HADS and IBS-SSS variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "Writing to /Users/arvid/Dropbox/Apps/Overleaf/Executive_function_in_IBS/table_2_new.tex for synching of LaTeX table!\n",
      "Latex table wiritten to disk\n"
     ]
    }
   ],
   "source": [
    "OVERLEAF = True\n",
    "table_tex = 'table_2_new.tex'\n",
    "cohend_func = cohen_d\n",
    "other_scale_list = ['HADS_anx', 'HADS_dep', 'HADS_tot', 'IBS-SSS']\n",
    "\n",
    "print(OVERLEAF)\n",
    "make_latex_table_IBS_HC(OVERLEAF, OVERLEAF_dir, table_tex, other_scale_list, df5_IBS, df5_HC, cohend_func, max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1103.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\", font_scale = 1.3) # palette=\"pastel\")\n",
    "\n",
    "visualize_variable_dist_IBS_HC(df5_IBS, df5_HC, 'IBS-SSS', 'IBS-SSS', cohen_d)\n",
    "\n",
    "plt.savefig(f\"{OVERLEAF_dir}/Figs/IBS-SSS_dist_in_IBS_vs_HC.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}