CBA-Memba.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import prim\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inputs = pd.read_csv(\"../Results_DMU/Murrumbala/scenario_values.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "low climate change is lower than current so it's 0 instead of 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inputs.Climate_change_scenario.replace(2,0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cost = pd.read_csv(\"../Results_DMU/Memba/COST_d7_v2.csv\")\n",
    "npv = pd.read_csv(\"../Results_DMU/Memba/NPV_d7_v2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reduced_risk = pd.read_csv(\"../Results_DMU/Memba/BEN_ind_d7_v2.csv\",dtype='float64')\n",
    "avoided_repair = pd.read_csv(\"../Results_DMU/Memba/BEN_direct_d7_v2.csv\",dtype='float64')\n",
    "ruc_decrease = pd.read_csv(\"../Results_DMU/Memba/BEN_add_d7_v2.csv\",dtype='float64')\n",
    "isolated_trips = pd.read_csv(\"../Results_DMU/Memba/BEN_isol_d7_v2.csv\",dtype='float64')\n",
    "maintenance_sav = pd.read_csv(\"../Results_DMU/Memba/BEN_sav_d7_v2.csv\",dtype='float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "benef = reduced_risk+avoided_repair+ruc_decrease+maintenance_sav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context(\"notebook\",rc={\"font.size\": 18})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Paving link#1</th>\n",
       "      <th>Paving link#2</th>\n",
       "      <th>Improve drainange all</th>\n",
       "      <th>Gravel link #1 and #2</th>\n",
       "      <th>Gravel alternatives routes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>-1250000</td>\n",
       "      <td>-1700000</td>\n",
       "      <td>-1690000</td>\n",
       "      <td>-1580000</td>\n",
       "      <td>-1520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1390</th>\n",
       "      <td>14800000</td>\n",
       "      <td>12900000</td>\n",
       "      <td>9330000</td>\n",
       "      <td>13300000</td>\n",
       "      <td>13600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>1180000</td>\n",
       "      <td>280000</td>\n",
       "      <td>-127000</td>\n",
       "      <td>458000</td>\n",
       "      <td>572000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Paving link#1  Paving link#2  Improve drainange all  \\\n",
       "394        -1250000       -1700000               -1690000   \n",
       "1390       14800000       12900000                9330000   \n",
       "1790        1180000         280000                -127000   \n",
       "\n",
       "      Gravel link #1 and #2  Gravel alternatives routes  \n",
       "394                -1580000                    -1520000  \n",
       "1390               13300000                    13600000  \n",
       "1790                 458000                      572000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_risk.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def savefig(path, **kwargs):\n",
    "    #Saves in both png and pdf\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    path = path.replace(\".png\",\"\")\n",
    "    path = path.replace(\".pdf\",\"\")\n",
    "\n",
    "    plt.savefig(path+\".png\", )\n",
    "    plt.savefig(path+\".pdf\", )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_names = dict({'Paving link#1':'Pave1', 'Paving link#2':'Pave2', 'Improve drainange all':'drainage',\n",
    "       'Gravel link #1 and #2':'gravel12', 'Gravel alternatives routes':'gravelothers'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_names2 = dict({'Paving link#1':'Inv. 1', 'Paving link#2':'Inv. 2', 'Improve drainange all':'Inv. 3',\n",
    "       'Gravel link #1 and #2':'Inv. 4', 'Gravel alternatives routes':'Inv. 5'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figfolder = \"Memba/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe06ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "plt.subplot(221)\n",
    "sns.boxplot(10**(-6)*reduced_risk.rename(columns=new_names2),width=0.5)\n",
    "plt.ylabel(\"Million USD\")\n",
    "plt.title(\"Reduced flood risk\")\n",
    "plt.subplot(222)\n",
    "sns.boxplot(10**(-6)*avoided_repair.rename(columns=new_names2),width=0.5)\n",
    "plt.title(\"Avoided repair\")\n",
    "plt.subplot(223)\n",
    "sns.boxplot(10**(-6)*ruc_decrease.rename(columns=new_names2),width=0.5)\n",
    "plt.title(\"RUC decrease\")\n",
    "plt.ylabel(\"Million USD\")\n",
    "plt.subplot(224)\n",
    "sns.boxplot(10**(-6)*maintenance_sav.rename(columns=new_names2),width=0.5)\n",
    "plt.ylabel(\"Million USD\")\n",
    "plt.title(\"Difference in maintenance costs\")\n",
    "\n",
    "savefig(figfolder+\"boxplot_benefit_break_down\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def anova_table(varin,data,experiments_cols):\n",
    "    formula = varin+\" ~ \" + \"+\".join(experiments_cols)\n",
    "    olsmodel=ols(formula,data=data).fit()\n",
    "    table=anova_lm(olsmodel)\n",
    "    table['sum_sq_pc']=table['sum_sq']/table['sum_sq'].sum()\n",
    "    table=table.sort(['sum_sq'],ascending=False)\n",
    "    return table['sum_sq_pc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Paving link#1', 'Paving link#2', 'Improve drainange all',\n",
       "       'Gravel link #1 and #2', 'Gravel alternatives routes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_risk.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "anova_reduced_risk=pd.DataFrame(index=reduced_risk.rename(columns=new_names).columns,columns=inputs.columns)\n",
    "for i in reduced_risk.rename(columns=new_names).columns:\n",
    "    anova_reduced_risk.loc[i,:]=anova_table(i,pd.concat([inputs,reduced_risk],axis=1).rename(columns=new_names),inputs.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Repair_time</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Traffic_growth</th>\n",
       "      <th>Flood_duration</th>\n",
       "      <th>Agriculture_elas</th>\n",
       "      <th>Climate_change_scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pave1</th>\n",
       "      <td>0.02675818</td>\n",
       "      <td>0.008999111</td>\n",
       "      <td>0.008055694</td>\n",
       "      <td>0.0001045453</td>\n",
       "      <td>0.006653402</td>\n",
       "      <td>0.1366925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pave2</th>\n",
       "      <td>0.01616976</td>\n",
       "      <td>0.005718416</td>\n",
       "      <td>0.005102175</td>\n",
       "      <td>0.0001238271</td>\n",
       "      <td>0.004215063</td>\n",
       "      <td>0.1331053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drainage</th>\n",
       "      <td>0.01148553</td>\n",
       "      <td>0.004453986</td>\n",
       "      <td>0.003963822</td>\n",
       "      <td>0.0002116404</td>\n",
       "      <td>0.003280641</td>\n",
       "      <td>0.1132017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravel12</th>\n",
       "      <td>0.01840233</td>\n",
       "      <td>0.006406611</td>\n",
       "      <td>0.005733381</td>\n",
       "      <td>0.0001204348</td>\n",
       "      <td>0.004731194</td>\n",
       "      <td>0.1335034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravelothers</th>\n",
       "      <td>0.01980734</td>\n",
       "      <td>0.006856705</td>\n",
       "      <td>0.006122831</td>\n",
       "      <td>0.0001179973</td>\n",
       "      <td>0.005045944</td>\n",
       "      <td>0.1338006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Repair_time Discount_Rate Traffic_growth Flood_duration  \\\n",
       "Pave1         0.02675818   0.008999111    0.008055694   0.0001045453   \n",
       "Pave2         0.01616976   0.005718416    0.005102175   0.0001238271   \n",
       "drainage      0.01148553   0.004453986    0.003963822   0.0002116404   \n",
       "gravel12      0.01840233   0.006406611    0.005733381   0.0001204348   \n",
       "gravelothers  0.01980734   0.006856705    0.006122831   0.0001179973   \n",
       "\n",
       "             Agriculture_elas Climate_change_scenario  \n",
       "Pave1             0.006653402               0.1366925  \n",
       "Pave2             0.004215063               0.1331053  \n",
       "drainage          0.003280641               0.1132017  \n",
       "gravel12          0.004731194               0.1335034  \n",
       "gravelothers      0.005045944               0.1338006  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_reduced_risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anova_avoided_repair=pd.DataFrame(index=avoided_repair.rename(columns=new_names).columns,columns=inputs.columns)\n",
    "for i in avoided_repair.rename(columns=new_names).columns:\n",
    "    anova_avoided_repair.loc[i,:]=anova_table(i,pd.concat([inputs,avoided_repair],axis=1).rename(columns=new_names),inputs.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Repair_time</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Traffic_growth</th>\n",
       "      <th>Flood_duration</th>\n",
       "      <th>Agriculture_elas</th>\n",
       "      <th>Climate_change_scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pave1</th>\n",
       "      <td>0.00213973</td>\n",
       "      <td>0.254151</td>\n",
       "      <td>1.29521e-05</td>\n",
       "      <td>9.050059e-07</td>\n",
       "      <td>9.936684e-07</td>\n",
       "      <td>0.5735489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pave2</th>\n",
       "      <td>0.00213973</td>\n",
       "      <td>0.254151</td>\n",
       "      <td>1.29521e-05</td>\n",
       "      <td>9.050059e-07</td>\n",
       "      <td>9.936684e-07</td>\n",
       "      <td>0.5735489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drainage</th>\n",
       "      <td>0.007117037</td>\n",
       "      <td>0.8518667</td>\n",
       "      <td>4.505544e-05</td>\n",
       "      <td>2.961481e-06</td>\n",
       "      <td>2.501249e-06</td>\n",
       "      <td>0.117037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravel12</th>\n",
       "      <td>0.00213973</td>\n",
       "      <td>0.254151</td>\n",
       "      <td>1.29521e-05</td>\n",
       "      <td>9.050059e-07</td>\n",
       "      <td>9.936684e-07</td>\n",
       "      <td>0.5735489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravelothers</th>\n",
       "      <td>0.004314377</td>\n",
       "      <td>0.515876</td>\n",
       "      <td>2.68742e-05</td>\n",
       "      <td>1.585531e-06</td>\n",
       "      <td>1.575183e-06</td>\n",
       "      <td>0.2854319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Repair_time Discount_Rate Traffic_growth Flood_duration  \\\n",
       "Pave1          0.00213973      0.254151    1.29521e-05   9.050059e-07   \n",
       "Pave2          0.00213973      0.254151    1.29521e-05   9.050059e-07   \n",
       "drainage      0.007117037     0.8518667   4.505544e-05   2.961481e-06   \n",
       "gravel12       0.00213973      0.254151    1.29521e-05   9.050059e-07   \n",
       "gravelothers  0.004314377      0.515876    2.68742e-05   1.585531e-06   \n",
       "\n",
       "             Agriculture_elas Climate_change_scenario  \n",
       "Pave1            9.936684e-07               0.5735489  \n",
       "Pave2            9.936684e-07               0.5735489  \n",
       "drainage         2.501249e-06                0.117037  \n",
       "gravel12         9.936684e-07               0.5735489  \n",
       "gravelothers     1.575183e-06               0.2854319  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_avoided_repair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anova_ruc_decrease=pd.DataFrame(index=ruc_decrease.rename(columns=new_names).columns,columns=inputs.columns)\n",
    "for i in ruc_decrease.rename(columns=new_names).columns:\n",
    "    anova_ruc_decrease.loc[i,:]=anova_table(i,pd.concat([inputs,ruc_decrease],axis=1).rename(columns=new_names),inputs.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Repair_time</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Traffic_growth</th>\n",
       "      <th>Flood_duration</th>\n",
       "      <th>Agriculture_elas</th>\n",
       "      <th>Climate_change_scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pave1</th>\n",
       "      <td>0.01341455</td>\n",
       "      <td>0.3670582</td>\n",
       "      <td>0.3070693</td>\n",
       "      <td>0.000161468</td>\n",
       "      <td>0.2642749</td>\n",
       "      <td>7.736072e-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pave2</th>\n",
       "      <td>0.01339984</td>\n",
       "      <td>0.3670936</td>\n",
       "      <td>0.3071864</td>\n",
       "      <td>0.0001563551</td>\n",
       "      <td>0.2642291</td>\n",
       "      <td>3.893919e-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drainage</th>\n",
       "      <td>0.01340554</td>\n",
       "      <td>0.3673493</td>\n",
       "      <td>0.3069088</td>\n",
       "      <td>0.0001609512</td>\n",
       "      <td>0.2642238</td>\n",
       "      <td>7.618348e-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravel12</th>\n",
       "      <td>0.01347906</td>\n",
       "      <td>0.3670807</td>\n",
       "      <td>0.3072581</td>\n",
       "      <td>0.0001516173</td>\n",
       "      <td>0.2641349</td>\n",
       "      <td>2.147689e-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravelothers</th>\n",
       "      <td>0.01340151</td>\n",
       "      <td>0.3671099</td>\n",
       "      <td>0.3072457</td>\n",
       "      <td>0.0001602797</td>\n",
       "      <td>0.2641771</td>\n",
       "      <td>8.844344e-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Repair_time Discount_Rate Traffic_growth Flood_duration  \\\n",
       "Pave1         0.01341455     0.3670582      0.3070693    0.000161468   \n",
       "Pave2         0.01339984     0.3670936      0.3071864   0.0001563551   \n",
       "drainage      0.01340554     0.3673493      0.3069088   0.0001609512   \n",
       "gravel12      0.01347906     0.3670807      0.3072581   0.0001516173   \n",
       "gravelothers  0.01340151     0.3671099      0.3072457   0.0001602797   \n",
       "\n",
       "             Agriculture_elas Climate_change_scenario  \n",
       "Pave1               0.2642749            7.736072e-29  \n",
       "Pave2               0.2642291            3.893919e-29  \n",
       "drainage            0.2642238            7.618348e-29  \n",
       "gravel12            0.2641349            2.147689e-29  \n",
       "gravelothers        0.2641771            8.844344e-29  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_ruc_decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anova_total=pd.DataFrame(index=benef.rename(columns=new_names).columns,columns=inputs.columns)\n",
    "for i in benef.rename(columns=new_names).columns:\n",
    "    anova_total.loc[i,:]=anova_table(i,pd.concat([inputs,benef],axis=1).rename(columns=new_names),inputs.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Repair_time</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Traffic_growth</th>\n",
       "      <th>Flood_duration</th>\n",
       "      <th>Agriculture_elas</th>\n",
       "      <th>Climate_change_scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pave1</th>\n",
       "      <td>0.02911495</td>\n",
       "      <td>0.007194645</td>\n",
       "      <td>0.04684877</td>\n",
       "      <td>4.995331e-05</td>\n",
       "      <td>0.03997289</td>\n",
       "      <td>0.1203258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pave2</th>\n",
       "      <td>0.01636611</td>\n",
       "      <td>1.127588e-05</td>\n",
       "      <td>0.02614931</td>\n",
       "      <td>8.355994e-05</td>\n",
       "      <td>0.02238672</td>\n",
       "      <td>0.1222202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drainage</th>\n",
       "      <td>0.01459511</td>\n",
       "      <td>0.6040671</td>\n",
       "      <td>0.005576996</td>\n",
       "      <td>1.808817e-05</td>\n",
       "      <td>0.004252986</td>\n",
       "      <td>0.1965781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravel12</th>\n",
       "      <td>0.007263459</td>\n",
       "      <td>0.1786956</td>\n",
       "      <td>0.02182198</td>\n",
       "      <td>8.702316e-05</td>\n",
       "      <td>0.01915208</td>\n",
       "      <td>0.1014257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gravelothers</th>\n",
       "      <td>0.002197621</td>\n",
       "      <td>0.4395601</td>\n",
       "      <td>0.02264873</td>\n",
       "      <td>5.237869e-05</td>\n",
       "      <td>0.02045433</td>\n",
       "      <td>0.07378401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Repair_time Discount_Rate Traffic_growth Flood_duration  \\\n",
       "Pave1          0.02911495   0.007194645     0.04684877   4.995331e-05   \n",
       "Pave2          0.01636611  1.127588e-05     0.02614931   8.355994e-05   \n",
       "drainage       0.01459511     0.6040671    0.005576996   1.808817e-05   \n",
       "gravel12      0.007263459     0.1786956     0.02182198   8.702316e-05   \n",
       "gravelothers  0.002197621     0.4395601     0.02264873   5.237869e-05   \n",
       "\n",
       "             Agriculture_elas Climate_change_scenario  \n",
       "Pave1              0.03997289               0.1203258  \n",
       "Pave2              0.02238672               0.1222202  \n",
       "drainage          0.004252986               0.1965781  \n",
       "gravel12           0.01915208               0.1014257  \n",
       "gravelothers       0.02045433              0.07378401  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cost.name=\"cost\"\n",
    "reduced_risk.name=\"reduced_risk\"\n",
    "avoided_repair.name=\"avoided_repair\"\n",
    "ruc_decrease.name=\"ruc_decrease\"\n",
    "benef.name=\"benef\"\n",
    "isolated_trips.name=\"isolated_trips\"\n",
    "maintenance_sav.name='maintenance_sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = cost.unstack()\n",
    "b = benef.unstack()\n",
    "c = isolated_trips.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a.index.names = [\"intervention\",\"scenario\"]\n",
    "b.index.names = [\"intervention\",\"scenario\"]\n",
    "c.index.names = [\"intervention\",\"scenario\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[[], []],\n",
       "           labels=[[], []],\n",
       "           names=['intervention', 'scenario'])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.index.difference(b.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = pd.concat([pd.DataFrame(a,columns=[\"cost\"]),pd.DataFrame(b,columns=[\"benefits\"]),pd.DataFrame(c,columns=[\"isolated_trips\"])],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inputs.index.name=\"scenario\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = m.reset_index().merge(inputs.reset_index()).set_index([\"intervention\",\"scenario\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cost', 'benefits', 'isolated_trips', 'Repair_time', 'Discount_Rate',\n",
       "       'Traffic_growth', 'Flood_duration', 'Agriculture_elas',\n",
       "       'Climate_change_scenario'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m['npv']=m.benefits-m.cost\n",
    "m['saved_trip_per_dollar'] = m.isolated_trips/m.cost\n",
    "m['benefit_cost_ratio'] = m.benefits/m.cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gravel alternatives routes</th>\n",
       "      <th>Gravel link #1 and #2</th>\n",
       "      <th>Improve drainange all</th>\n",
       "      <th>Paving link#1</th>\n",
       "      <th>Paving link#2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Repair_time</th>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.014662</td>\n",
       "      <td>0.022971</td>\n",
       "      <td>0.010607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount_Rate</th>\n",
       "      <td>0.801115</td>\n",
       "      <td>0.577720</td>\n",
       "      <td>0.523090</td>\n",
       "      <td>0.014798</td>\n",
       "      <td>0.057619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Traffic_growth</th>\n",
       "      <td>0.006961</td>\n",
       "      <td>0.010435</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>0.046027</td>\n",
       "      <td>0.024202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flood_duration</th>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agriculture_elas</th>\n",
       "      <td>0.006716</td>\n",
       "      <td>0.009517</td>\n",
       "      <td>0.005130</td>\n",
       "      <td>0.039868</td>\n",
       "      <td>0.020918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Climate_change_scenario</th>\n",
       "      <td>0.024762</td>\n",
       "      <td>0.051578</td>\n",
       "      <td>0.240324</td>\n",
       "      <td>0.120198</td>\n",
       "      <td>0.115722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>0.160040</td>\n",
       "      <td>0.350329</td>\n",
       "      <td>0.210079</td>\n",
       "      <td>0.756084</td>\n",
       "      <td>0.770850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Gravel alternatives routes  Gravel link #1 and #2  \\\n",
       "Repair_time                                0.000380               0.000368   \n",
       "Discount_Rate                              0.801115               0.577720   \n",
       "Traffic_growth                             0.006961               0.010435   \n",
       "Flood_duration                             0.000026               0.000053   \n",
       "Agriculture_elas                           0.006716               0.009517   \n",
       "Climate_change_scenario                    0.024762               0.051578   \n",
       "Residual                                   0.160040               0.350329   \n",
       "\n",
       "                         Improve drainange all  Paving link#1  Paving link#2  \n",
       "Repair_time                           0.014662       0.022971       0.010607  \n",
       "Discount_Rate                         0.523090       0.014798       0.057619  \n",
       "Traffic_growth                        0.006692       0.046027       0.024202  \n",
       "Flood_duration                        0.000024       0.000054       0.000082  \n",
       "Agriculture_elas                      0.005130       0.039868       0.020918  \n",
       "Climate_change_scenario               0.240324       0.120198       0.115722  \n",
       "Residual                              0.210079       0.756084       0.770850  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame()\n",
    "\n",
    "for i in ['Gravel alternatives routes', 'Gravel link #1 and #2', 'Improve drainange all', 'Paving link#1', 'Paving link#2']:\n",
    "\n",
    "    formula=\"npv ~ Repair_time + Discount_Rate + Traffic_growth + Flood_duration + Agriculture_elas + Climate_change_scenario\"\n",
    "    olsmodel=ols(formula,data=m.loc[i]).fit()\n",
    "    table=anova_lm(olsmodel)\n",
    "    df3[i]=table['sum_sq']/table['sum_sq'].sum()\n",
    "    \n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "\n",
    "for i in ['Gravel alternatives routes', 'Gravel link #1 and #2', 'Improve drainange all', 'Paving link#1', 'Paving link#2']:\n",
    "\n",
    "    formula=\"benefits ~ Repair_time + Discount_Rate + Traffic_growth + Flood_duration + Agriculture_elas + Climate_change_scenario\"\n",
    "    olsmodel=ols(formula,data=m.loc[i]).fit()\n",
    "    table=anova_lm(olsmodel)\n",
    "    df[i]=table['sum_sq']/table['sum_sq'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gravel alternatives routes</th>\n",
       "      <th>Gravel link #1 and #2</th>\n",
       "      <th>Improve drainange all</th>\n",
       "      <th>Paving link#1</th>\n",
       "      <th>Paving link#2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Repair_time</th>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.007263</td>\n",
       "      <td>0.014595</td>\n",
       "      <td>0.029115</td>\n",
       "      <td>0.016366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount_Rate</th>\n",
       "      <td>0.439560</td>\n",
       "      <td>0.178696</td>\n",
       "      <td>0.604067</td>\n",
       "      <td>0.007195</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Traffic_growth</th>\n",
       "      <td>0.022649</td>\n",
       "      <td>0.021822</td>\n",
       "      <td>0.005577</td>\n",
       "      <td>0.046849</td>\n",
       "      <td>0.026149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flood_duration</th>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agriculture_elas</th>\n",
       "      <td>0.020454</td>\n",
       "      <td>0.019152</td>\n",
       "      <td>0.004253</td>\n",
       "      <td>0.039973</td>\n",
       "      <td>0.022387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Climate_change_scenario</th>\n",
       "      <td>0.073784</td>\n",
       "      <td>0.101426</td>\n",
       "      <td>0.196578</td>\n",
       "      <td>0.120326</td>\n",
       "      <td>0.122220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>0.441303</td>\n",
       "      <td>0.671554</td>\n",
       "      <td>0.174912</td>\n",
       "      <td>0.756493</td>\n",
       "      <td>0.812783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Gravel alternatives routes  Gravel link #1 and #2  \\\n",
       "Repair_time                                0.002198               0.007263   \n",
       "Discount_Rate                              0.439560               0.178696   \n",
       "Traffic_growth                             0.022649               0.021822   \n",
       "Flood_duration                             0.000052               0.000087   \n",
       "Agriculture_elas                           0.020454               0.019152   \n",
       "Climate_change_scenario                    0.073784               0.101426   \n",
       "Residual                                   0.441303               0.671554   \n",
       "\n",
       "                         Improve drainange all  Paving link#1  Paving link#2  \n",
       "Repair_time                           0.014595       0.029115       0.016366  \n",
       "Discount_Rate                         0.604067       0.007195       0.000011  \n",
       "Traffic_growth                        0.005577       0.046849       0.026149  \n",
       "Flood_duration                        0.000018       0.000050       0.000084  \n",
       "Agriculture_elas                      0.004253       0.039973       0.022387  \n",
       "Climate_change_scenario               0.196578       0.120326       0.122220  \n",
       "Residual                              0.174912       0.756493       0.812783  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x9b2d470>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "RERERHft/wErMFV1buCA8QAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9b2df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=df.T.plot(kind='bar')\n",
    "ax.legend(bbox_to_anchor=(1.7, 1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame()\n",
    "\n",
    "for i in ['Gravel alternatives routes', 'Gravel link #1 and #2', 'Improve drainange all', 'Paving link#1', 'Paving link#2']:\n",
    "\n",
    "    formula=\"cost ~ Repair_time + Discount_Rate + Traffic_growth + Flood_duration + Agriculture_elas + Climate_change_scenario\"\n",
    "    olsmodel=ols(formula,data=m.loc[i]).fit()\n",
    "    table=anova_lm(olsmodel)\n",
    "    df2[i]=table['sum_sq']/table['sum_sq'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xb982668>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb98af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=df2.T.plot(kind='bar')\n",
    "ax.legend(bbox_to_anchor=(1.7, 1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>intervention</th>\n",
       "      <th>Gravel alternatives routes</th>\n",
       "      <th>Gravel link #1 and #2</th>\n",
       "      <th>Improve drainange all</th>\n",
       "      <th>Paving link#1</th>\n",
       "      <th>Paving link#2</th>\n",
       "      <th>Gravel alternatives routes</th>\n",
       "      <th>Gravel link #1 and #2</th>\n",
       "      <th>Improve drainange all</th>\n",
       "      <th>Paving link#1</th>\n",
       "      <th>Paving link#2</th>\n",
       "      <th>...</th>\n",
       "      <th>Gravel alternatives routes</th>\n",
       "      <th>Gravel link #1 and #2</th>\n",
       "      <th>Improve drainange all</th>\n",
       "      <th>Paving link#1</th>\n",
       "      <th>Paving link#2</th>\n",
       "      <th>Gravel alternatives routes</th>\n",
       "      <th>Gravel link #1 and #2</th>\n",
       "      <th>Improve drainange all</th>\n",
       "      <th>Paving link#1</th>\n",
       "      <th>Paving link#2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>cost</th>\n",
       "      <th>cost</th>\n",
       "      <th>cost</th>\n",
       "      <th>cost</th>\n",
       "      <th>cost</th>\n",
       "      <th>benefits</th>\n",
       "      <th>benefits</th>\n",
       "      <th>benefits</th>\n",
       "      <th>benefits</th>\n",
       "      <th>benefits</th>\n",
       "      <th>...</th>\n",
       "      <th>saved_trip_per_dollar</th>\n",
       "      <th>saved_trip_per_dollar</th>\n",
       "      <th>saved_trip_per_dollar</th>\n",
       "      <th>saved_trip_per_dollar</th>\n",
       "      <th>saved_trip_per_dollar</th>\n",
       "      <th>benefit_cost_ratio</th>\n",
       "      <th>benefit_cost_ratio</th>\n",
       "      <th>benefit_cost_ratio</th>\n",
       "      <th>benefit_cost_ratio</th>\n",
       "      <th>benefit_cost_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scenario</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>61600000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>23500000</td>\n",
       "      <td>23800000</td>\n",
       "      <td>27400000</td>\n",
       "      <td>-38104000</td>\n",
       "      <td>-21104000</td>\n",
       "      <td>51615000</td>\n",
       "      <td>841000</td>\n",
       "      <td>-3542000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.008716</td>\n",
       "      <td>0.019149</td>\n",
       "      <td>0.013403</td>\n",
       "      <td>0.011642</td>\n",
       "      <td>-0.618571</td>\n",
       "      <td>-0.576612</td>\n",
       "      <td>2.196383</td>\n",
       "      <td>0.035336</td>\n",
       "      <td>-0.129270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>43400000</td>\n",
       "      <td>26000000</td>\n",
       "      <td>20600000</td>\n",
       "      <td>20400000</td>\n",
       "      <td>23800000</td>\n",
       "      <td>-24787000</td>\n",
       "      <td>-14037206</td>\n",
       "      <td>31704012</td>\n",
       "      <td>-1148206</td>\n",
       "      <td>-3246206</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003157</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.009320</td>\n",
       "      <td>0.006716</td>\n",
       "      <td>0.005756</td>\n",
       "      <td>-0.571129</td>\n",
       "      <td>-0.539893</td>\n",
       "      <td>1.539030</td>\n",
       "      <td>-0.056285</td>\n",
       "      <td>-0.136395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>49100000</td>\n",
       "      <td>29300000</td>\n",
       "      <td>21500000</td>\n",
       "      <td>21400000</td>\n",
       "      <td>24900000</td>\n",
       "      <td>-29782000</td>\n",
       "      <td>-16588000</td>\n",
       "      <td>31825000</td>\n",
       "      <td>-1361000</td>\n",
       "      <td>-3638000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.002884</td>\n",
       "      <td>0.007442</td>\n",
       "      <td>0.003949</td>\n",
       "      <td>0.003394</td>\n",
       "      <td>-0.606558</td>\n",
       "      <td>-0.566143</td>\n",
       "      <td>1.480233</td>\n",
       "      <td>-0.063598</td>\n",
       "      <td>-0.146104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>64100000</td>\n",
       "      <td>38100000</td>\n",
       "      <td>23900000</td>\n",
       "      <td>24300000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>-35509000</td>\n",
       "      <td>-16667000</td>\n",
       "      <td>55290000</td>\n",
       "      <td>5463000</td>\n",
       "      <td>1953000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001966</td>\n",
       "      <td>0.003307</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.005185</td>\n",
       "      <td>0.004516</td>\n",
       "      <td>-0.553963</td>\n",
       "      <td>-0.437454</td>\n",
       "      <td>2.313389</td>\n",
       "      <td>0.224815</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>49600000</td>\n",
       "      <td>29600000</td>\n",
       "      <td>21600000</td>\n",
       "      <td>21500000</td>\n",
       "      <td>25000000</td>\n",
       "      <td>-31887000</td>\n",
       "      <td>-18275000</td>\n",
       "      <td>31557000</td>\n",
       "      <td>-3200000</td>\n",
       "      <td>-5068000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001972</td>\n",
       "      <td>0.003305</td>\n",
       "      <td>0.007824</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.003913</td>\n",
       "      <td>-0.642883</td>\n",
       "      <td>-0.617399</td>\n",
       "      <td>1.460972</td>\n",
       "      <td>-0.148837</td>\n",
       "      <td>-0.202720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>66200000</td>\n",
       "      <td>39300000</td>\n",
       "      <td>24300000</td>\n",
       "      <td>24700000</td>\n",
       "      <td>28300000</td>\n",
       "      <td>-46946000</td>\n",
       "      <td>-27031000</td>\n",
       "      <td>45670000</td>\n",
       "      <td>-4611000</td>\n",
       "      <td>-7501000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003444</td>\n",
       "      <td>0.005802</td>\n",
       "      <td>0.016132</td>\n",
       "      <td>0.009231</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>-0.709154</td>\n",
       "      <td>-0.687812</td>\n",
       "      <td>1.879424</td>\n",
       "      <td>-0.186680</td>\n",
       "      <td>-0.265053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>64100000</td>\n",
       "      <td>38100000</td>\n",
       "      <td>23900000</td>\n",
       "      <td>24300000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>-42662000</td>\n",
       "      <td>-24014000</td>\n",
       "      <td>53532810</td>\n",
       "      <td>-2295000</td>\n",
       "      <td>-5255000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003136</td>\n",
       "      <td>0.005276</td>\n",
       "      <td>0.011799</td>\n",
       "      <td>0.008272</td>\n",
       "      <td>0.007204</td>\n",
       "      <td>-0.665554</td>\n",
       "      <td>-0.630289</td>\n",
       "      <td>2.239867</td>\n",
       "      <td>-0.094444</td>\n",
       "      <td>-0.188351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>48200000</td>\n",
       "      <td>28800000</td>\n",
       "      <td>21400000</td>\n",
       "      <td>21300000</td>\n",
       "      <td>24800000</td>\n",
       "      <td>-22023000</td>\n",
       "      <td>-9659000</td>\n",
       "      <td>39150000</td>\n",
       "      <td>5921000</td>\n",
       "      <td>2781000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.002424</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>0.003278</td>\n",
       "      <td>0.002815</td>\n",
       "      <td>-0.456909</td>\n",
       "      <td>-0.335382</td>\n",
       "      <td>1.829439</td>\n",
       "      <td>0.277981</td>\n",
       "      <td>0.112137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>58100000</td>\n",
       "      <td>34600000</td>\n",
       "      <td>23000000</td>\n",
       "      <td>23100000</td>\n",
       "      <td>26700000</td>\n",
       "      <td>-35468000</td>\n",
       "      <td>-19730000</td>\n",
       "      <td>47852000</td>\n",
       "      <td>492000</td>\n",
       "      <td>-3386000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004458</td>\n",
       "      <td>0.007486</td>\n",
       "      <td>0.015739</td>\n",
       "      <td>0.011212</td>\n",
       "      <td>0.009700</td>\n",
       "      <td>-0.610465</td>\n",
       "      <td>-0.570231</td>\n",
       "      <td>2.080522</td>\n",
       "      <td>0.021299</td>\n",
       "      <td>-0.126816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>47400000</td>\n",
       "      <td>28300000</td>\n",
       "      <td>21300000</td>\n",
       "      <td>21100000</td>\n",
       "      <td>24600000</td>\n",
       "      <td>-30053000</td>\n",
       "      <td>-17313000</td>\n",
       "      <td>29390000</td>\n",
       "      <td>-3119000</td>\n",
       "      <td>-5004000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002658</td>\n",
       "      <td>0.004452</td>\n",
       "      <td>0.010094</td>\n",
       "      <td>0.005972</td>\n",
       "      <td>0.005122</td>\n",
       "      <td>-0.634030</td>\n",
       "      <td>-0.611767</td>\n",
       "      <td>1.379812</td>\n",
       "      <td>-0.147820</td>\n",
       "      <td>-0.203415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "intervention Gravel alternatives routes Gravel link #1 and #2  \\\n",
       "                                   cost                  cost   \n",
       "scenario                                                        \n",
       "1640                           61600000              36600000   \n",
       "1822                           43400000              26000000   \n",
       "917                            49100000              29300000   \n",
       "1016                           64100000              38100000   \n",
       "292                            49600000              29600000   \n",
       "243                            66200000              39300000   \n",
       "1516                           64100000              38100000   \n",
       "1174                           48200000              28800000   \n",
       "1504                           58100000              34600000   \n",
       "222                            47400000              28300000   \n",
       "\n",
       "intervention Improve drainange all Paving link#1 Paving link#2  \\\n",
       "                              cost          cost          cost   \n",
       "scenario                                                         \n",
       "1640                      23500000      23800000      27400000   \n",
       "1822                      20600000      20400000      23800000   \n",
       "917                       21500000      21400000      24900000   \n",
       "1016                      23900000      24300000      27900000   \n",
       "292                       21600000      21500000      25000000   \n",
       "243                       24300000      24700000      28300000   \n",
       "1516                      23900000      24300000      27900000   \n",
       "1174                      21400000      21300000      24800000   \n",
       "1504                      23000000      23100000      26700000   \n",
       "222                       21300000      21100000      24600000   \n",
       "\n",
       "intervention Gravel alternatives routes Gravel link #1 and #2  \\\n",
       "                               benefits              benefits   \n",
       "scenario                                                        \n",
       "1640                          -38104000             -21104000   \n",
       "1822                          -24787000             -14037206   \n",
       "917                           -29782000             -16588000   \n",
       "1016                          -35509000             -16667000   \n",
       "292                           -31887000             -18275000   \n",
       "243                           -46946000             -27031000   \n",
       "1516                          -42662000             -24014000   \n",
       "1174                          -22023000              -9659000   \n",
       "1504                          -35468000             -19730000   \n",
       "222                           -30053000             -17313000   \n",
       "\n",
       "intervention Improve drainange all Paving link#1 Paving link#2  \\\n",
       "                          benefits      benefits      benefits   \n",
       "scenario                                                         \n",
       "1640                      51615000        841000      -3542000   \n",
       "1822                      31704012      -1148206      -3246206   \n",
       "917                       31825000      -1361000      -3638000   \n",
       "1016                      55290000       5463000       1953000   \n",
       "292                       31557000      -3200000      -5068000   \n",
       "243                       45670000      -4611000      -7501000   \n",
       "1516                      53532810      -2295000      -5255000   \n",
       "1174                      39150000       5921000       2781000   \n",
       "1504                      47852000        492000      -3386000   \n",
       "222                       29390000      -3119000      -5004000   \n",
       "\n",
       "intervention        ...         Gravel alternatives routes  \\\n",
       "                    ...              saved_trip_per_dollar   \n",
       "scenario            ...                                      \n",
       "1640                ...                           0.005179   \n",
       "1822                ...                           0.003157   \n",
       "917                 ...                           0.001721   \n",
       "1016                ...                           0.001966   \n",
       "292                 ...                           0.001972   \n",
       "243                 ...                           0.003444   \n",
       "1516                ...                           0.003136   \n",
       "1174                ...                           0.001449   \n",
       "1504                ...                           0.004458   \n",
       "222                 ...                           0.002658   \n",
       "\n",
       "intervention Gravel link #1 and #2 Improve drainange all  \\\n",
       "             saved_trip_per_dollar saved_trip_per_dollar   \n",
       "scenario                                                   \n",
       "1640                      0.008716              0.019149   \n",
       "1822                      0.005269              0.009320   \n",
       "917                       0.002884              0.007442   \n",
       "1016                      0.003307              0.010377   \n",
       "292                       0.003305              0.007824   \n",
       "243                       0.005802              0.016132   \n",
       "1516                      0.005276              0.011799   \n",
       "1174                      0.002424              0.006402   \n",
       "1504                      0.007486              0.015739   \n",
       "222                       0.004452              0.010094   \n",
       "\n",
       "intervention         Paving link#1         Paving link#2  \\\n",
       "             saved_trip_per_dollar saved_trip_per_dollar   \n",
       "scenario                                                   \n",
       "1640                      0.013403              0.011642   \n",
       "1822                      0.006716              0.005756   \n",
       "917                       0.003949              0.003394   \n",
       "1016                      0.005185              0.004516   \n",
       "292                       0.004550              0.003913   \n",
       "243                       0.009231              0.008057   \n",
       "1516                      0.008272              0.007204   \n",
       "1174                      0.003278              0.002815   \n",
       "1504                      0.011212              0.009700   \n",
       "222                       0.005972              0.005122   \n",
       "\n",
       "intervention Gravel alternatives routes Gravel link #1 and #2  \\\n",
       "                     benefit_cost_ratio    benefit_cost_ratio   \n",
       "scenario                                                        \n",
       "1640                          -0.618571             -0.576612   \n",
       "1822                          -0.571129             -0.539893   \n",
       "917                           -0.606558             -0.566143   \n",
       "1016                          -0.553963             -0.437454   \n",
       "292                           -0.642883             -0.617399   \n",
       "243                           -0.709154             -0.687812   \n",
       "1516                          -0.665554             -0.630289   \n",
       "1174                          -0.456909             -0.335382   \n",
       "1504                          -0.610465             -0.570231   \n",
       "222                           -0.634030             -0.611767   \n",
       "\n",
       "intervention Improve drainange all      Paving link#1      Paving link#2  \n",
       "                benefit_cost_ratio benefit_cost_ratio benefit_cost_ratio  \n",
       "scenario                                                                  \n",
       "1640                      2.196383           0.035336          -0.129270  \n",
       "1822                      1.539030          -0.056285          -0.136395  \n",
       "917                       1.480233          -0.063598          -0.146104  \n",
       "1016                      2.313389           0.224815           0.070000  \n",
       "292                       1.460972          -0.148837          -0.202720  \n",
       "243                       1.879424          -0.186680          -0.265053  \n",
       "1516                      2.239867          -0.094444          -0.188351  \n",
       "1174                      1.829439           0.277981           0.112137  \n",
       "1504                      2.080522           0.021299          -0.126816  \n",
       "222                       1.379812          -0.147820          -0.203415  \n",
       "\n",
       "[10 rows x 60 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.unstack('intervention').swaplevel(i=1,j=0,axis=1).sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "XAAAAPgUAi4AAAB8CgEXAAAAPuX/A8oGqfHVQ6VEAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xea34eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "ax=sns.boxplot(x='intervention',y='benefit_cost_ratio',data=m.rename(index=new_names2).reset_index(),width=0.5)\n",
    "plt.subplot(122)\n",
    "ax=sns.boxplot(x='intervention',y='saved_trip_per_dollar',data=m.rename(index=new_names2).reset_index(),width=0.5)\n",
    "\n",
    "savefig(figfolder+\"cba_results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cc = dict({0:\"CC decreases\\nprecipitations\",1:\"Current\\nclimate\", 3:\"Medium\\nincrease in\\nprecipitations\", 4:\"High increase\\nin precipiations\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd364a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub=m.rename(index=new_names).reset_index()\n",
    "sub.Climate_change_scenario.replace(cc,inplace=True)\n",
    "sns.boxplot(x='Climate_change_scenario',y='benefit_cost_ratio',data=sub[sub.intervention=='drainage'],width=0.5)\n",
    "plt.title(\"Improve drainage all\")\n",
    "#savefig(\"benefit_cost_ratio_drainage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xe78e828>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "A9OxKJX/nAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe14a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub=m.rename(index=new_names).reset_index()\n",
    "sub.Climate_change_scenario.replace(cc,inplace=True)\n",
    "sns.boxplot(x='Climate_change_scenario',y='benefit_cost_ratio',data=sub[sub.intervention=='Pave1'],width=0.5)\n",
    "plt.title(\"Pave link #1\")\n",
    "#savefig(\"benefit_cost_ratio_pave_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regret = m.npv.unstack('intervention').copy()\n",
    "regret = regret.rename(columns=new_names2).sort(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = regret.add(-regret.max(axis=1),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "intervention\n",
       "Inv. 1    -60061578\n",
       "Inv. 2    -66864578\n",
       "Inv. 3            0\n",
       "Inv. 4    -98418578\n",
       "Inv. 5   -145882578\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regret.add(-regret.max(axis=1),axis=0).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "3LhxOH/+/A1t+Pj4YOjQoZg7dy7i4uIAAJGRkfj888/x888/w2w2Izs7G2vWrLnpvpsNHDgQn3zy\n",
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       "1KlTceedd7Y62ouLi8OJEycsjyLp27cvnnvuOTz55JOW1eYnTZrUoq9f/zx06FBkZ2fjq6++suwb\n",
       "NmwYhg0bhvj4eIwZMwZ9+vSBRqNp8VUDdS5czo6ISBBHoEREghigRESCGKBERIIYoEREghigRESC\n",
       "GKBERIIYoEREghigRESC/h8IXSLk9QONggAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe750ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "sns.boxplot(-10**(-6)*regret.add(-regret.max(axis=1),axis=0),width=0.5)\n",
    "plt.ylabel(\"Regret (Million USD)\")\n",
    "savefig(figfolder+\"regret\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "intervention\n",
       "Inv. 1\\n(paving\\nR650)             0\n",
       "Inv. 2\\n(partial\\npaving\\nR650)    0\n",
       "Inv. 3\\n(drainage)                 1\n",
       "Inv. 4\\n(gravel\\nR650)             0\n",
       "Inv. 5\\n(gravel\\nothers)           0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rr=r==0\n",
    "rr.sum(axis=0)/len(rr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import prim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forprim=m.unstack('intervention').swaplevel(i=1,j=0,axis=1).rename(columns=new_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81100000000000005"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(scenarofinterest.npv>0)/2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['Pave1', 'Pave2', 'drainage', 'gravel12', 'gravelothers'], ['Agriculture_elas', 'Climate_change_scenario', 'Discount_Rate', 'Flood_duration', 'Repair_time', 'Traffic_growth', 'benefit_cost_ratio', 'benefits', 'cost', 'isolated_trips', 'npv', 'saved_trip_per_dollar']],\n",
       "           labels=[[4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1, 4, 3, 2, 0, 1], [8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 6, 6, 6, 6, 6]],\n",
       "           names=['intervention', None])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forprim.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x309a04e0>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "KsuWLUNSUhKys7MtlNg6/Omqvr4eU6dORdeuXV3rVwUFBRFjdIQQKCoqwo8//ghATlU7efIkbt68\n",
       "ieHDh2Pv3r0oLS1FfX09hg4dihEjRlgssTX40lPnzp2RlZVleNAbPXo0+vbta5W4luLvM6WyefNm\n",
       "nDt3DlOnTrVKVEvxp6dvv/0WxcXFEEIgPT0dM2fO9Hk8tsMkhBBCLIZNPwghhBCLoTEmhBBCLIbG\n",
       "mBBCCLEYGmNCCCHEYmiMCSGEEIuhMSaEEEIshsaYEEIIsRgaY0IIIcRi/h+IaNle+HJaOQAAAABJ\n",
       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e122ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(scenarofinterest.Repair_time,scenarofinterest.npv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x301c9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scenarofinterest = forprim.Pave1\n",
    "p = prim.Prim(scenarofinterest[inputs.columns], scenarofinterest.npv>0, threshold=0.5, threshold_type=\">\")\n",
    "\n",
    "box = p.find_box()\n",
    "box.show_tradeoff()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box 1 [Peeling Trajectory 8]\n",
      "    Stats\n",
      "        Coverage: 0.761406\n",
      "        Density:  0.941311\n",
      "        Mass:     0.656000\n",
      "        Res Dim:  2.000000\n",
      "        Mean:     0.941311\n",
      "    Limits\n",
      "                               min       max     qp values\n",
      "        Repair_time      -0.227503  0.498351  1.352703e-28\n",
      "        Agriculture_elas  0.500446  1.392167  9.887484e-05\n"
     ]
    }
   ],
   "source": [
    "box.select(8)\n",
    "print(box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x308197b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box.show_details();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "6IzVHTeh5QAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17560780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scenarofinterest = forprim.Pave1\n",
    "p = prim.Prim(scenarofinterest[inputs.columns], forprim.Pave1.npv>forprim.drainage.npv, threshold=0.5, threshold_type=\">\")\n",
    "\n",
    "box = p.find_box()\n",
    "box.show_tradeoff()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box 1 [Peeling Trajectory 5]\n",
      "    Stats\n",
      "        Coverage: 0.829555\n",
      "        Density:  0.811166\n",
      "        Mass:     0.609000\n",
      "        Res Dim:  2.000000\n",
      "        Mean:     0.811166\n",
      "    Limits\n",
      "                                      min       max     qp values\n",
      "        Climate_change_scenario  0.000000  3.000000  5.043476e-26\n",
      "        Repair_time             -0.312422  0.498351  1.117326e-13\n"
     ]
    }
   ],
   "source": [
    "box.select(5)\n",
    "print(box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "vKTz+Vx0Cf8WZ/+7mHQDAAAAhhEZCAAAABhG0w0AAAAYRtMNAAAAGEbTDQAAABhG0w0AAAAY9gYC\n",
       "ROQrdpHJDQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x174d55c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box.show_details();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59550000000000003"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(forprim.Pave1.npv>forprim.drainage.npv)/2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1765e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scenarofinterest = forprim.Pave1\n",
    "p = prim.Prim(scenarofinterest[inputs.columns], forprim.Pave1.npv<forprim.drainage.npv, threshold=0.5, threshold_type=\">\")\n",
    "\n",
    "box = p.find_box()\n",
    "box.show_tradeoff()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box 1 [Peeling Trajectory 15]\n",
      "    Stats\n",
      "        Coverage: 0.724351\n",
      "        Density:  0.645374\n",
      "        Mass:     0.454000\n",
      "        Res Dim:  3.000000\n",
      "        Mean:     0.645374\n",
      "    Limits\n",
      "                               min       max     qp values\n",
      "        Repair_time      -0.498815  0.039926  9.364393e-39\n",
      "        Agriculture_elas  0.585804  1.499111  4.106993e-03\n",
      "        Traffic_growth    0.000080  0.057402  2.475668e-01\n"
     ]
    }
   ],
   "source": [
    "box.select(15)\n",
    "print(box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x174d78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box.show_details();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.3.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}