gazefocus_analyze.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Analysis of focal point of gaze\n",
    "not used in thesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "19"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "conditions = {\n",
    "    \"LipSync\": [5, 6, 7, 8, 9],\n",
    "    \"FaceTracking\": [1, 2, 3, 4, 10],\n",
    "}\n",
    "\n",
    "source = r\"C:\\Users\\alex_\\OneDrive - Universität Hamburg\\Bachelor\\Resultate\\GazeTracking\\\\\"\n",
    "\n",
    "data = []  # All facetracking data\n",
    "for _, _, files in os.walk(source):\n",
    "    for f in files:\n",
    "        df_subj = pd.read_csv(fr\"{source}\\{f}\", sep='\\t', names=[\"collider\", \"time\"])\n",
    "        name = f.lstrip(\"subj_\").rstrip(\".tsv\")  # Give dataframe subject id as name\n",
    "        ts = df_subj[\"time\"].str.replace(',','.').astype(float).values\n",
    "        s_subj = pd.Series(data=ts, index=df_subj[\"collider\"].values)\n",
    "        s_subj.name = name\n",
    "        data.append(s_subj)\n",
    "\n",
    "len(f\"Read {len(data)} data points\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "HeadCollider (UnityEngine.Transform)             363.112900\nTorsoCollider (UnityEngine.Transform)             49.299820\nUpperLeftArmCollider (UnityEngine.Transform)       7.699995\nLowerLeftArmCollider (UnityEngine.Transform)      11.400010\nLowerRightArmCollider (UnityEngine.Transform)      2.799999\nUpperRightArmCollider (UnityEngine.Transform)      2.799999\nName: 10_1, dtype: float64"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex_\\AppData\\Local\\Temp/ipykernel_15488/3964475515.py:25: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df_sorted.index = df_sorted.index.str.replace(\"\\(UnityEngine.Transform\\)\", \"\")\n"
     ]
    },
    {
     "data": {
      "text/plain": "(UpperRightArmCollider     0.009962\n LowerRightArmCollider     0.018609\n UpperLeftArmCollider           NaN\n TorsoCollider             0.209114\n HeadCollider              0.741262\n LowerLeftArmCollider      0.021053\n Name: LipSync, dtype: float64,\n UpperRightArmCollider     0.016739\n LowerRightArmCollider     0.019813\n UpperLeftArmCollider      0.027329\n TorsoCollider             0.399521\n HeadCollider              0.536598\n LowerLeftArmCollider           NaN\n Name: FaceTracking, dtype: float64)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_ls = None\n",
    "s_ft = None\n",
    "\n",
    "for s in data:\n",
    "    if s.index.isnull().any():\n",
    "        continue\n",
    "    name = s.name.split('_')[0]\n",
    "    if int(name) in conditions[\"LipSync\"]:\n",
    "        if not isinstance(s_ls, pd.Series):\n",
    "            s_ls = s\n",
    "        else:\n",
    "            s_ls += s\n",
    "    if int(name) in conditions[\"FaceTracking\"]:\n",
    "        if not isinstance(s_ft, pd.Series):\n",
    "            s_ft = s\n",
    "        else:\n",
    "            s_ft += s\n",
    "\n",
    "s_ls /= s_ls.sum()\n",
    "s_ft /= s_ft.sum()\n",
    "s_ls.name = \"LipSync\"\n",
    "s_ft.name = \"FaceTracking\"\n",
    "\n",
    "df_sorted = pd.DataFrame(data=[s_ls, s_ft]).transpose().sort_values(by=\"FaceTracking\")\n",
    "df_sorted.index = df_sorted.index.str.replace(\"\\(UnityEngine.Transform\\)\", \"\")\n",
    "s_ls = df_sorted[\"LipSync\"]\n",
    "s_ft = df_sorted[\"FaceTracking\"]\n",
    "\n",
    "s_ls.name = \"LipSync\"\n",
    "s_ft.name = \"FaceTracking\"\n",
    "\n",
    "s_ls, s_ft"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "index = np.arange(6)\n",
    "bar_width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ls_plot = ax.bar(index, s_ls, bar_width,\n",
    "                 label=\"LipSync\")\n",
    "\n",
    "ft_plot = ax.bar(index+bar_width, s_ft, bar_width,\n",
    "                 label=\"FaceTracking\")\n",
    "\n",
    "ax.set_xlabel('Category')\n",
    "ax.set_title('Crime incidence by season, type')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels(s_ls.index, rotation=-90)\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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