{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "51e157d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "import pandas as pd\n",
    "\n",
    "def load_data(user_filter=None):\n",
    "    dic_data = []\n",
    "    \n",
    "    for p in glob('/opt/iui-datarelease3-sose2021/*.csv'):\n",
    "        path = p\n",
    "        filename = path.split('/')[-1]\n",
    "        user = int(filename.split('_')[0][1:])\n",
    "        if (user_filter):\n",
    "            if (user != user_filter):\n",
    "                continue\n",
    "        scenario = filename.split('_')[1][len('Scenario'):]\n",
    "        heightnorm = filename.split('_')[2][len('HeightNormalization'):] == 'True'\n",
    "        armnorm = filename.split('_')[3][len('ArmNormalization'):] == 'True'\n",
    "        rep =  int(filename.split('.')[0].split('_')[4][len('Repetition'):])\n",
    "        data = pd.read_csv(path)\n",
    "        dic_data.append(\n",
    "            {\n",
    "                'filename': path,\n",
    "                'user': user,\n",
    "                'scenario': scenario,\n",
    "                'heightnorm': heightnorm,\n",
    "                'armnorm': armnorm,\n",
    "                'rep': rep,\n",
    "                'data': data \n",
    "            }\n",
    "        )\n",
    "    return dic_data\n",
    "\n",
    "dic_data = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "45e0dcc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "652e33e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fil_dic_data = []\n",
    "for d in dic_data:\n",
    "    if d['scenario'] == 'Sorting':\n",
    "        if d['heightnorm'] == d['armnorm']:\n",
    "            fil_dic_data.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e77a3dec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/iui-datarelease3-sose2021/P1_ScenarioSorting_HeightNormalizationTrue_ArmNormalizationTrue_Repetition2.csv\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "337"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = 1\n",
    "entry = fil_dic_data[index]['data']\n",
    "print(fil_dic_data[index]['filename'])\n",
    "col_of_interst = []\n",
    "for col in entry:\n",
    "    if 'float' in str(entry[col].dtype):\n",
    "        col_of_interst.append(col)\n",
    "len(col_of_interst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "93e18064",
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = []\n",
    "for i in dic_data:\n",
    "    len_list.append(len(i['data']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "67a3c912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      384.000000\n",
       "mean      3053.768229\n",
       "std       2195.831831\n",
       "min        597.000000\n",
       "50%       2395.000000\n",
       "90%       5977.000000\n",
       "91%       6157.600000\n",
       "92%       6239.600000\n",
       "93%       6341.490000\n",
       "94%       6585.200000\n",
       "95%       7561.800000\n",
       "96%       8158.000000\n",
       "97%       8895.250000\n",
       "98%       9942.320000\n",
       "99%      10315.120000\n",
       "max      19371.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len_series = pd.Series(len_list, dtype='int64')\n",
    "len_series.describe(percentiles=[x*0.01 for x in range(90,100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1b473131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0a6d98aa30>]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from matplotlib import pyplot as plt\n",
    "l = []\n",
    "ptiles = [x*0.01 for x in range(100)]\n",
    "for i in ptiles:\n",
    "    l.append(len_series.quantile(i))\n",
    "\n",
    "plt.plot(l, ptiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7dac09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dtype: float64"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}