participant statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import iqr\n",
    "import numpy as np\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "#plt.gca().yaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}')) # No decimal places\n",
    "#plt.gca().yaxis.set_major_formatter(StrMethodFormatter('{x:,.2f}')) # 2 decimal places\n",
    "\n",
    "# use LateX fonts\n",
    "plt.rcParams.update({\n",
    "  \"text.usetex\": True,\n",
    "  \"font.family\": \"Helvetica\"\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PARTICIPANTS_INFO = {}\n",
    "x_phq = []\n",
    "x_phq_male = []\n",
    "x_phq_female = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "with open('extracted_feature_results/results.json', 'r') as jsonFile:\n",
    "    PARTICIPANTS_INFO = json.load(jsonFile)\n",
    "    #print(PARTICIPANTS_INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169\n",
      "105\n",
      "274\n",
      "tt 274\n",
      "tt 169\n",
      "tt 105\n"
     ]
    }
   ],
   "source": [
    "count_m = 0\n",
    "count_f = 0\n",
    "count = 0\n",
    "for v in PARTICIPANTS_INFO.values():\n",
    "    count += 1\n",
    "    x_phq.append(int(v['phq_score']))\n",
    "    if v['gender'] == 'male':\n",
    "        x_phq_male.append(int(v['phq_score']))\n",
    "        count_m += 1\n",
    "    if v['gender'] == 'female':\n",
    "        x_phq_female.append(int(v['phq_score']))\n",
    "        count_f += 1\n",
    "        \n",
    "print(count_m)\n",
    "print(count_f)\n",
    "print(count)\n",
    "def get_bins_for_hist(list_phq):\n",
    "    print(\"tt\", len(list_phq))\n",
    "    bin_width = (2 * iqr(list_phq)) ** (-1/3) #bin-width based on freedman diaconis rule \n",
    "    bins = round((max(list_phq) - min(list_phq)) / bin_width)\n",
    "    return bins\n",
    "\n",
    "bins = get_bins_for_hist(x_phq)\n",
    "bins_male = get_bins_for_hist(x_phq_male)\n",
    "bins_female = get_bins_for_hist(x_phq_female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist(list_phq, bins, x_label, y_label, plot_title):\n",
    "    _, _, _ = plt.hist(list_phq, bins, facecolor='g', alpha=0.75)\n",
    "    \n",
    "    #plt.xticks(list_phq, weight = 'bold')\n",
    "    plt.yticks(fontsize=14)\n",
    "    plt.xticks(fontsize=14)\n",
    "    plt.xlabel(x_label, fontsize=16)\n",
    "    plt.ylabel(y_label, fontsize=16)\n",
    "    plt.title(plot_title, fontsize=16)\n",
    "    plt.grid(True)\n",
    "    #plt.xticks(range(0, 24))\n",
    "    plt.gca().xaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}')) # No decimal places\n",
    "    plt.savefig(\"phq-verteilung-maennlich.svg\")\n",
    "    plt.show()\n",
    "   \n",
    "\n",
    "\n",
    "plot_hist(x_phq_male, bins_male, 'PHQ-Wert', 'Anzahl', 'PHQ-Verteilung - männlich')\n",
    "#x_phq_male.sort()\n",
    "#plot_hist(x_phq_female, bins_female, 'PHQ-Wert', 'Anzahl', 'PHQ-Verteilung - weiblich')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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