iui-group-l-name-zensiert/2-second-project/tdt/DataViz.ipynb

{
 "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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
      "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
}