238 lines
16 KiB
Plaintext
238 lines
16 KiB
Plaintext
{
|
|
"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
|
|
}
|