Experiment 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 3\n",
    "Test the influence of the society's sensitivity to flood events on endogenising land-use dynamics.\n",
    "\n",
    "- Policies: No\n",
    "- Climate change scenarios: Yes\n",
    "- Socioeconomic scenarios: No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bjafino\\anaconda3\\envs\\py27\\lib\\site-packages\\ema_workbench\\em_framework\\optimization.py:22: ImportWarning: platypus based optimization not available\n",
      "  warnings.warn(\"platypus based optimization not available\", ImportWarning)\n"
     ]
    }
   ],
   "source": [
    "from ema_workbench import load_results\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "results = load_results('S09_08032018_v18_noLU2.tar.gz')\n",
    "experiments, outcomes = results\n",
    "\n",
    "#outcomes df\n",
    "all_outcomes_df = pd.DataFrame.from_dict(outcomes)\n",
    "all_outcomes_df['integrator'] = all_outcomes_df.index\n",
    "\n",
    "#experiments df\n",
    "experiments_df = pd.DataFrame(experiments)\n",
    "experiments_df['integrator'] = experiments_df.index\n",
    "\n",
    "#results df\n",
    "results_df = pd.merge(all_outcomes_df, experiments_df, on='integrator')\n",
    "\n",
    "#categorize into No ClimateChange, G, and Wp climate scenarios\n",
    "results_df['ClimateChange'] = results_df['ClimScenNo'].apply(lambda x: 'NoCC' if x<11 else 'G' if x<21 else 'Wp')\n",
    "\n",
    "#if ATP==0, it should be 100 (ATP is not reached within the simulation timeframe). \n",
    "results_df['ATP_i'] = results_df['ATP_i'].apply(lambda x: x if x> 0 else 100)\n",
    "results_df['ATP_0'] = results_df['ATP_0'].apply(lambda x: x if x> 0 else 100)\n",
    "\n",
    "#interaction / no interaction ratio for selected KPIs\n",
    "results_df['DamCumFloat_i_0'] = results_df['DamCumFloat_i'] / results_df['DamCumFloat_0']\n",
    "results_df['DamCumFloat_i_0'] = results_df['DamCumFloat_i_0'].fillna(1)\n",
    "results_df['ATP_i_0'] = results_df['ATP_i'] / results_df['ATP_0']\n",
    "results_df['DRFlooded_i_0'] = results_df['DRFlooded_i'] / results_df['DRFlooded_0']\n",
    "results_df['DRFlooded_i_0'] = results_df['DRFlooded_i'] / results_df['DRFlooded_0']\n",
    "results_df['DamAgriKSum_i_0'] = results_df['DamAgriKSum_i'] / results_df['DamAgriKSum_0']\n",
    "results_df['StadFldA_i_0'] = results_df['StadFldA_i'] / results_df['StadFldA_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bjafino\\anaconda3\\envs\\py27\\lib\\site-packages\\seaborn\\categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
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g48BdZvYOd38yzZyksSV1fWUzX1spIiKSL1YBmOASbhcDbcAhuat8mNkbgWui/VW3Mo41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\n96XFOAib9UkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x67e0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(figsize=(9,5))\n",
    "bp0  = sns.boxplot(x=\"floodLUThres\", y=\"DamCumFloat_i_0\", data=results_df, ax=axarr, color='lightblue')\n",
    "bp0.set_xlabel('Flood sensitivity threshold', fontsize=15)\n",
    "bp0.set_ylabel(\"Ratio damage \\n endogenous / exogenous\", fontsize=15)\n",
    "bp0  = sns.despine()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py27]",
   "language": "python",
   "name": "conda-env-py27-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}