Commit 39b0d1f8 authored by Benedikt Zoennchen's avatar Benedikt Zoennchen
Browse files

add plot notebook, move AnalysisTool, move zhang-2011 OSM tests.

parent 2c720570
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loaded 1 out of 1 output directories. \n"
]
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a15236be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"from vadere_analysis_tool import ScenarioOutput, VadereProject\n",
"sns.set(style=\"darkgrid\")\n",
"#g = sns.FacetGrid(tips, row=\"smoker\", col=\"time\", margin_titles=True)\n",
"\n",
"\n",
"projectFolder = \"./../../../../VadereModelTests/TestOSM_zhang-2011/\"\n",
"project = VadereProject(projectFolder)\n",
"\n",
"#out = project.named_output.C_050_180_180_2018_11_26_16_30_29_355()\n",
"\n",
"# transform data frame\n",
"dataFrames = []\n",
"for outStr in project.output_dirs :\n",
" out = project.output_dirs[outStr]\n",
" for fileStr in out.files :\n",
" if fileStr.endswith(\".fundamentalDiagram\") :\n",
" ndf = pd.DataFrame();\n",
" df = out.files[fileStr]()\n",
" df = df[df.velocity > 0]\n",
" ndf['density'] = df.density.astype(float)\n",
" ndf['velocity'] = df.velocity.astype(float)\n",
" ndf['scenario'] = [out.scenario['name']] * len(df.velocity)\n",
" dataFrames.append(ndf)\n",
"\n",
"concatFrames = pd.concat(dataFrames)\n",
"\n",
"sns.relplot(x=\"density\", y=\"velocity\", hue=\"scenario\", data=concatFrames); "
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['test',\n",
" 'test',\n",
" 'test',\n",
" 'test',\n",
" 'test',\n",
" 'test',\n",
" 'test',\n",
" 'test',\n",
" 'test',\n",
" 'test']"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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