Prim_and_dimensional_stacking_policies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3ee15bf",
   "metadata": {},
   "source": [
    "# Prim and dimensional stacking on policies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10f1964",
   "metadata": {},
   "source": [
    "Notebook used to play around with prim analysis for interesting policies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "15428ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:50:58.295206Z",
     "start_time": "2021-06-16T21:50:58.285233Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from ema_workbench.analysis import prim\n",
    "import matplotlib.pyplot as plt\n",
    "from ema_workbench.analysis import dimensional_stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a06abc40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:50:59.548646Z",
     "start_time": "2021-06-16T21:50:58.766497Z"
    }
   },
   "outputs": [],
   "source": [
    "experiments = pd.read_csv('Outcomes/reevaluation_promising_experiments.csv')\n",
    "outcomes_total_dikerings_load_DF = pd.read_csv('Outcomes/reevaluation_promising_policies_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d53941b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:00.208403Z",
     "start_time": "2021-06-16T21:51:00.196919Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a6dac6c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:00.858050Z",
     "start_time": "2021-06-16T21:51:00.829893Z"
    }
   },
   "outputs": [],
   "source": [
    "experiments = experiments[experiments['policy'].isin(['scenario option 47', 'scenario option 70'])]\n",
    "experiments.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "efc9dea5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:01.500347Z",
     "start_time": "2021-06-16T21:51:01.487869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 54)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f4c286df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:02.238306Z",
     "start_time": "2021-06-16T21:51:02.215858Z"
    }
   },
   "outputs": [],
   "source": [
    "list_of_policies = experiments['Unnamed: 0'].to_list()\n",
    "experiments.reset_index(inplace = True, drop = True)\n",
    "experiments.drop(columns = ['Unnamed: 0'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d3d4c009",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:02.930469Z",
     "start_time": "2021-06-16T21:51:02.885823Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A.0_ID flood wave shape</th>\n",
       "      <th>A.1_Bmax</th>\n",
       "      <th>A.1_Brate</th>\n",
       "      <th>A.1_pfail</th>\n",
       "      <th>A.2_Bmax</th>\n",
       "      <th>A.2_Brate</th>\n",
       "      <th>A.2_pfail</th>\n",
       "      <th>A.3_Bmax</th>\n",
       "      <th>A.3_Brate</th>\n",
       "      <th>A.3_pfail</th>\n",
       "      <th>A.4_Bmax</th>\n",
       "      <th>A.4_Brate</th>\n",
       "      <th>A.4_pfail</th>\n",
       "      <th>A.5_Bmax</th>\n",
       "      <th>A.5_Brate</th>\n",
       "      <th>A.5_pfail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>108.0</td>\n",
       "      <td>306.204867</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>10.0</td>\n",
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       "      <td>154.082511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.322756</td>\n",
       "      <td>268.823052</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.316942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.0</td>\n",
       "      <td>232.607582</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.155764</td>\n",
       "      <td>90.103364</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.987325</td>\n",
       "      <td>111.572437</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.643120</td>\n",
       "      <td>196.138057</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.557227</td>\n",
       "      <td>107.631245</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.444051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>348.831088</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.196232</td>\n",
       "      <td>265.196139</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.288037</td>\n",
       "      <td>185.085407</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.775696</td>\n",
       "      <td>271.290312</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.177457</td>\n",
       "      <td>72.382141</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.080950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>189.760125</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.339976</td>\n",
       "      <td>204.933490</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.925791</td>\n",
       "      <td>199.394990</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.955059</td>\n",
       "      <td>191.887551</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.267018</td>\n",
       "      <td>65.021861</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.255926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115.0</td>\n",
       "      <td>217.057027</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.826048</td>\n",
       "      <td>290.177029</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.493298</td>\n",
       "      <td>128.670483</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.524991</td>\n",
       "      <td>68.986944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.129549</td>\n",
       "      <td>223.883294</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.863789</td>\n",
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      ],
      "text/plain": [
       "   A.0_ID flood wave shape    A.1_Bmax  A.1_Brate  A.1_pfail    A.2_Bmax  \\\n",
       "0                    108.0  306.204867       10.0   0.200822  162.325922   \n",
       "1                     71.0  232.607582        1.5   0.155764   90.103364   \n",
       "2                     21.0  348.831088        1.0   0.196232  265.196139   \n",
       "3                      9.0  189.760125       10.0   0.339976  204.933490   \n",
       "4                    115.0  217.057027        1.5   0.826048  290.177029   \n",
       "\n",
       "   A.2_Brate  A.2_pfail    A.3_Bmax  A.3_Brate  A.3_pfail    A.4_Bmax  \\\n",
       "0        1.5   0.791249  210.485682       10.0   0.592787  154.082511   \n",
       "1       10.0   0.987325  111.572437       10.0   0.643120  196.138057   \n",
       "2       10.0   0.288037  185.085407       10.0   0.775696  271.290312   \n",
       "3        1.5   0.925791  199.394990       10.0   0.955059  191.887551   \n",
       "4        1.0   0.493298  128.670483        1.5   0.524991   68.986944   \n",
       "\n",
       "   A.4_Brate  A.4_pfail    A.5_Bmax  A.5_Brate  A.5_pfail  \n",
       "0        1.0   0.322756  268.823052       10.0   0.316942  \n",
       "1       10.0   0.557227  107.631245       10.0   0.444051  \n",
       "2        1.0   0.177457   72.382141       10.0   0.080950  \n",
       "3       10.0   0.267018   65.021861        1.5   0.255926  \n",
       "4        1.0   0.129549  223.883294       10.0   0.863789  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments = experiments.iloc[:, :16]\n",
    "experiments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e343f329",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:03.336947Z",
     "start_time": "2021-06-16T21:51:03.311495Z"
    }
   },
   "outputs": [],
   "source": [
    "outcomes_total_dikerings_load_DF = outcomes_total_dikerings_load_DF[outcomes_total_dikerings_load_DF['Unnamed: 0'].isin(list_of_policies)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c1824453",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:03.792823Z",
     "start_time": "2021-06-16T21:51:03.778860Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 12)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes_total_dikerings_load_DF.reset_index(inplace = True, drop = True)\n",
    "outcomes_total_dikerings_load_DF.drop(columns = ['Unnamed: 0'], inplace = True)\n",
    "outcomes_total_dikerings_load_DF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "24196cc8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:04.357064Z",
     "start_time": "2021-06-16T21:51:04.313174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A.1 Total Costs</th>\n",
       "      <th>A.1_Expected Number of Deaths</th>\n",
       "      <th>A.2 Total Costs</th>\n",
       "      <th>A.2_Expected Number of Deaths</th>\n",
       "      <th>A.3 Total Costs</th>\n",
       "      <th>A.3_Expected Number of Deaths</th>\n",
       "      <th>A.4 Total Costs</th>\n",
       "      <th>A.4_Expected Number of Deaths</th>\n",
       "      <th>A.5 Total Costs</th>\n",
       "      <th>A.5_Expected Number of Deaths</th>\n",
       "      <th>RfR Total Costs</th>\n",
       "      <th>Expected Evacuation Costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.254465e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.456855e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.632540e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.111679e+07</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>8.122223e+07</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>190.029291</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.254465e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.456855e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.632540e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.728841e+07</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>8.122223e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>394300000.0</td>\n",
       "      <td>30.527844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.254465e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.456855e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.632540e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.364691e+07</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>8.122223e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>394300000.0</td>\n",
       "      <td>618.029889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.254465e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.456855e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.632540e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000077e+07</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>8.122223e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>394300000.0</td>\n",
       "      <td>182.064530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.254465e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.456855e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.632540e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.577532e+07</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>8.122223e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>394300000.0</td>\n",
       "      <td>818.682561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   A.1 Total Costs  A.1_Expected Number of Deaths  A.2 Total Costs  \\\n",
       "0     2.254465e+08                            0.0     2.456855e+08   \n",
       "1     2.254465e+08                            0.0     2.456855e+08   \n",
       "2     2.254465e+08                            0.0     2.456855e+08   \n",
       "3     2.254465e+08                            0.0     2.456855e+08   \n",
       "4     2.254465e+08                            0.0     2.456855e+08   \n",
       "\n",
       "   A.2_Expected Number of Deaths  A.3 Total Costs  \\\n",
       "0                            0.0     8.632540e+07   \n",
       "1                            0.0     8.632540e+07   \n",
       "2                            0.0     8.632540e+07   \n",
       "3                            0.0     8.632540e+07   \n",
       "4                            0.0     8.632540e+07   \n",
       "\n",
       "   A.3_Expected Number of Deaths  A.4 Total Costs  \\\n",
       "0                            0.0     2.111679e+07   \n",
       "1                            0.0     1.728841e+07   \n",
       "2                            0.0     2.364691e+07   \n",
       "3                            0.0     2.000077e+07   \n",
       "4                            0.0     2.577532e+07   \n",
       "\n",
       "   A.4_Expected Number of Deaths  A.5 Total Costs  \\\n",
       "0                       0.000216     8.122223e+07   \n",
       "1                       0.000041     8.122223e+07   \n",
       "2                       0.000654     8.122223e+07   \n",
       "3                       0.000203     8.122223e+07   \n",
       "4                       0.000846     8.122223e+07   \n",
       "\n",
       "   A.5_Expected Number of Deaths  RfR Total Costs  Expected Evacuation Costs  \n",
       "0                            0.0      394300000.0                 190.029291  \n",
       "1                            0.0      394300000.0                  30.527844  \n",
       "2                            0.0      394300000.0                 618.029889  \n",
       "3                            0.0      394300000.0                 182.064530  \n",
       "4                            0.0      394300000.0                 818.682561  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes_total_dikerings_load_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "df79b68d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:04.907319Z",
     "start_time": "2021-06-16T21:51:04.880180Z"
    }
   },
   "outputs": [],
   "source": [
    "outcomes_total_dikerings_load_DF['Total deaths'] = outcomes_total_dikerings_load_DF['A.1_Expected Number of Deaths'] + outcomes_total_dikerings_load_DF['A.2_Expected Number of Deaths'] + outcomes_total_dikerings_load_DF['A.3_Expected Number of Deaths'] + outcomes_total_dikerings_load_DF['A.4_Expected Number of Deaths'] + outcomes_total_dikerings_load_DF['A.5_Expected Number of Deaths']\n",
    "outcomes_total_dikerings_load_DF['Total costs'] = outcomes_total_dikerings_load_DF['A.1 Total Costs'] + outcomes_total_dikerings_load_DF['A.2 Total Costs'] + outcomes_total_dikerings_load_DF['A.3 Total Costs'] + outcomes_total_dikerings_load_DF['A.4 Total Costs'] + outcomes_total_dikerings_load_DF['A.5 Total Costs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61f80cf",
   "metadata": {},
   "source": [
    "## Deaths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9f5442",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-04T10:30:32.193117Z",
     "start_time": "2021-06-04T10:30:32.177809Z"
    }
   },
   "source": [
    "In this section analysis are performed on the simulated number of deaths by the dike_function.py model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "51694d03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:05.408291Z",
     "start_time": "2021-06-16T21:51:05.395326Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06465357611643033\n",
      "0.0460358314433177\n",
      "0.23909854651014015\n",
      "0.004787884700555595\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(outcomes_total_dikerings_load_DF['A.1_Expected Number of Deaths'].max())\n",
    "print(outcomes_total_dikerings_load_DF['A.2_Expected Number of Deaths'].max())\n",
    "print(outcomes_total_dikerings_load_DF['A.3_Expected Number of Deaths'].max())\n",
    "print(outcomes_total_dikerings_load_DF['A.4_Expected Number of Deaths'].max())\n",
    "print(outcomes_total_dikerings_load_DF['A.5_Expected Number of Deaths'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "651adcba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:44.840898Z",
     "start_time": "2021-06-16T21:51:43.225508Z"
    }
   },
   "outputs": [],
   "source": [
    "data = outcomes_total_dikerings_load_DF['Total deaths']\n",
    "\n",
    "#desirable risk by Dutch government (1/100000) times citizens of Deventer (+/- 100000 in jan 2019)\n",
    "y = data > 0\n",
    "\n",
    "prim_alg = prim.Prim(experiments,y, threshold=0.8)\n",
    "\n",
    "box1 = prim_alg.find_box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "779ad786",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:51:46.111814Z",
     "start_time": "2021-06-16T21:51:45.753149Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = box1.show_tradeoff()\n",
    "fig.suptitle('Dikering 5 threshold of deaths tradeoff', fontsize=16)\n",
    "plt.savefig('Figures_dewy/policies_deaths_tradeoff.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20b10f2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:42.374054Z",
     "start_time": "2021-06-16T21:42:42.346128Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coverage    0.968288\n",
      "density     0.723158\n",
      "id                 1\n",
      "mass            0.95\n",
      "mean        0.723158\n",
      "res_dim            1\n",
      "Name: 1, dtype: object\n",
      "\n",
      "              box 1                                       \n",
      "                min       max                    qp values\n",
      "A.3_pfail  0.000274  0.949699  [-1.0, 0.09879331742426939]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "box1.inspect(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3fe879f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:42.643974Z",
     "start_time": "2021-06-16T21:42:42.377046Z"
    }
   },
   "outputs": [
    {
     "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": [
    "fig = box1.inspect(1,style='graph')\n",
    "fig.suptitle('Dikering 5 threshold of deaths constraints', fontsize=16)\n",
    "plt.savefig('Figures_dewy/policies_deaths_constraints.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "353c3ebd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:42.675199Z",
     "start_time": "2021-06-16T21:42:42.646918Z"
    }
   },
   "outputs": [],
   "source": [
    "box1.select(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1abec712",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:43.024615Z",
     "start_time": "2021-06-16T21:42:42.678193Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 241.25x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = box1.show_pairs_scatter()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "345f1386",
   "metadata": {},
   "source": [
    "#### Dimensinal stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4b3280c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:58:37.448611Z",
     "start_time": "2021-06-16T21:58:35.636173Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python37-32\\lib\\site-packages\\ema_workbench\\analysis\\dimensional_stacking.py:404: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ooi = pd.DataFrame(y[:, np.newaxis], columns=[ooi_label])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = outcomes_total_dikerings_load_DF['Total deaths']\n",
    "y = data <= 0.1 #1.6 is the treshold\n",
    "\n",
    "fig = dimensional_stacking.create_pivot_plot(experiments, y)\n",
    "fig.suptitle('Dimensional stacking no deaths total delta', fontsize=16)\n",
    "plt.savefig('Figures_dewy/policies_deaths_stacking.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858c76ab",
   "metadata": {},
   "source": [
    "Analysis: the dimensional stacking shows as well that most important indicator for the outcomes that go over the treshold is the 'A5_pfail'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80cadab",
   "metadata": {},
   "source": [
    "## Damage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d1bdc1",
   "metadata": {},
   "source": [
    "In this section analysis are performed on the simulated number of deaths by the dike_function.py model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92bdb6b6",
   "metadata": {},
   "source": [
    "### Total damage for all dikerings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0504d4c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:44.926080Z",
     "start_time": "2021-06-16T21:42:44.913115Z"
    }
   },
   "outputs": [],
   "source": [
    "data = outcomes_total_dikerings_load_DF['Total costs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b8d66ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:45.323599Z",
     "start_time": "2021-06-16T21:42:44.933061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sa7kCZ6BxI3OduBUZuLCLp/2RmW02s7yZfcnMZs3sjf0enMQj7MlViHabw1KElo4uvhIZvLDz8P+1c24eeA3eWjoXA7/Rr0FJvErVWsf+eVCpR5maWazUQ5209bZV4IsMWtjAD1o/rwZudc7N9Wk80gdhKvxubjherNb6sl8R6Y+wSyt81sweBpaAXzCzGaDYv2FJnMJU+EFLJ0olHm2WjgJfZNDCLo98M3AdsM85VwEWgNf1c2ASnzAVfnBAiBLMS2GutC0Et0/Umvgigxa2wge4FG8+/sqf+ZtWG5vZHv/7OwAH3OKc+5OuRik9KVZC9PDzXfTwq/W2F3OBKnyRJAm7PPKHgIuAe4Hgf66jTeADVeC/OufuMbNNwN1m9gXn3EM9jFe6UKzW2Tza/q86ajDX645ytfNJ2+CAoB6+yOCFrfD3Ac93zrmwO/bXzD/sf37azL6LtxCbAn+dlSo1RjeNtN0m6myaYLkG9fBFNo6ws3QeAM7t9knMbC9wFXBXk+/dZGb7zWz/7Oxst08hbRQr4da88bYN19JZXq4h5CwdXXglMnBhK/ztwENm9k2gFDzonHttpx80s0ng48Cv+XP5z+KcuwW4BWDfvn2h30FIeOGmZUZrvSyFWIETIJ/NkM+aWjoiCRA28N/Zzc79G5x/HPhb59wnutmH9M6bLx9v6yXMTVUCo7ls5LX2RSR+oQLfOfdVM7sAuMQ590UzGwfa/k83b+GWvwa+q1U1ByvMfPmRyIEfrocPMKobmYskQti1dP4T8DHgL/2HzgM+2eHHXga8CbjezO71/9zY7UClO845r6XTsdceTMsMGfjVoMLv/E+om7X2RSR+YVs6vwhcg3/S1Tn3qJmd0+4HnHP/zPItEWVAlm9+0r4SL2QzZKyLk7YhKnzd5lAkGcLO0ik558rBF/7FVzrBugGEDWYzi3QTlEg9/EK28Y5ARAYnbOB/1cx+C+9m5q8EbgU+079hSVyCir3TtEzw73oVMpiXe/id/wmN5jKq8EUSIGzg3wzMAvcDPw98Dvjtfg1K4rNciYcN5mgtnTAHkrGCevgiSRB2lk7dzD4JfNI5p6ujNpDlk6vxtl6izNIZy2c5pMAXGbi2ZZ953mlmx4BHgEf8u139zvoMT3oVrfWSpRS1hx/ixuTeLB3NwxcZtE4p8Ot40yuvds5tc85tA64FXmZmv9730UnPgt55mGAezWciX2nbabVMbxvNwxdJgk7/W98EvN45dyB4wDn3BPBG4Gf7OTCJR9Ci6TQtE4Jee7hKvFSpYQYjHeb3g1/h66StyMB1+t+ad84dW/2g38fP92dIEqdSpJO2EaZlVuuM5rJ4F1S3N1YI/85BRPqnUwqUu/yeJETUaZlhg9lbriHcJK+xfJZq3VGpqY8vMkidZum80MzWrHCJdwXtaB/GIzGLdIFUPkspwpW2Yfa58rmLlRr5bNiZwCISt7aB75wL9z9aEita4GdCt3SWKvXIgb9UqbFpVJ1AkUFRuTXkitUI0zIjLq0Q5oQtrFh6WTcyFxkoBf6Q62ZaZpg7WUZp6YwVlit8ERkcBf6QK1Zr3kqYmRCzafJZ6g4qtc6Bv1SuMV4I29KJtvSyiPSHAn/IlSr1UBdHwYqTqyGWV1iMFPiq8EWSQIE/5MLcwDzQuOtViIuklio1xgvhbqcwpsAXSQQF/pCL0muf8Cv2xRCBv1iuhq7wgx6+rrYVGSwF/pArVuqhL5AKKvaFcrXjtovlWiPIOwkq/DAHEhHpHwX+kCtWI1T4I+GC2TkXqYc/MRL+QCIi/aPAH3JL5VqoKZmwosIvtQ/mcq1Ore5C9/An/cA/02G/ItJfCvwhV6yGn6UTtsIP5vaHPhmcy5DLWMcDiYj0lwJ/yJUinbQNV+EHB4SwLR0zY2Ikx5miAl9kkBT4Qy7KtMxGrz1s4I+Ea+mA19Y5U9JJW5FBUuAPuWizdLwDw0LIls54yAMJeO0itXREBkuBP+SizNIZyWXIZozFDrNpgtk2YVs64L170CwdkcFS4A+5KBdemRkThSwLHVovjZO2EQLfa+ko8EUGSYE/xGp1R7FSD93DB68S71ThL5+0jdbDV0tHZLAU+EMsWLsmSutlvJDt2MNf7LKlo1k6IoOlwB9ijWCOMJtmIkQl3s2BRC0dkcFT4A+xxVL02TTjhWzj51rut4uWzsSI984hzM1VRKQ/FPhDLAjm4AraMCZDzKZZLFUxC3fbxMDESI5a3VGq6jaHIoOiwB9iQUtnLEIlPl7IdVxaYbHsXcxl1vkuWgGtpyMyeAr8Idao8COdXO18gdRiJfxKmY39hly2QUT6R4E/xBa7mC8/Xghx0jbCWviByVEv8E9rpo7IwCjwh1jQ0pmIdHI1x0K5Rr3e+uTq6WKVyZF8pLFMhlynR0T6R4E/xKKuagkwGSyR3Ob+s2dKFTaNhj+IAI3tVeGLDI4Cf4h1Mw9/86hXuc8vVVpuc7pYZVOEfQJMjXn7nWuzXxHpLwX+EFuMeKMSgM1+MM8XWwfzmVI1coWvwBcZvL4Fvpm938yOmtkD/XoOaW+xXGM0762AGdZyhd+69XK6WG2chA1r06gCX2TQ+lnhfwC4oY/7lw4Wy9VIV8MCbB4Leu1tKvxitRHgYWUzxqbRnAJfZID6FvjOuTuAE/3av3S2WIo+X75R4bcI/GKlRrlWb8y6iWJqLK/AFxmggffwzewmM9tvZvtnZ2cHPZyhsljuIvDH2rd0gitlN0ds6YACX2TQBh74zrlbnHP7nHP7ZmZmBj2cobLQRUsnOBnbapZOMK0yag8fFPgigzbwwJf+Weqiws9nM4wXsi1bOkFvf1PEC69AgS8yaAr8IbbQReCDV+W3bOmowhfZsPo5LfMjwJ3Ac83sGTN7S7+eS5pb6qKlA96J21YV/rwf+FHn4YMCX2TQov+vDck59/p+7VvCOVOqRVoLP7B5rHXgBydtu2npbB7LU67WI91YXUTio5bOEJsvVhrTLKPY3Kal0+jhd1nhgy6+EhkUBf6QKlVrlKv1roK5bYXfQw9/y7gX+KcWFfgig6DAH1KnG732bir81r3206UqI7kM+Wz0fzrbJgoAHD9TivyzItI7Bf6QCgI/WCohiq0TBeaWKlRra+8/e2qx3KjUozpn0wgAswp8kYFQ4A+p4MKpbk6ubp8s4BycbNJ6OblYYet4oasxbZ/0A/+0Al9kEBT4Q+p0D9Mng2A+vrA2mE8ulLsO/KmxPPmscexMuaufF5HeKPCHVDCbJlgbJ4rpRq99bTCfXCyzdaK7lo6ZsX1yhGNq6YgMhAJ/SM33MH1y2q/wmwXzqcUKW7qs8MF796CWjshgKPCHVC+zdLZPeoG+uvVSrztOLpbZ1lPgF1ThiwyIAn9IzRermBH53rPgTcvMZWzN9MnTxSp1R9ezdABmNqmlIzIoCvwhNb9UYbKQIxPh9oaBTMaYniys6eGfWPS+7vakLeD38MvU667rfYhIdxT4Q+p0MfqNxleanlhbiQdfz/jz6bsxs2mEmt8aEpH1pcAfUqeLla5m6ASmJwscWzg7lJ+dLwJwzubuA/+8LWMAPHNyqet9iEh3FPhDqtcKf2bTCLN+wAeenfcq/B2bRrve755t4wA8fXKx632ISHcU+EOq25UyA+dtGePIfJHKiuUVjp4uUshmejppGwT+908o8EXWmwJ/SJ1cKLN1ovuTq7u3jlF3cGRuuco/Ol/inM0jmEU/ERyYHMkxPVHg6RNq6YisNwX+EHLOcXyh3FidshvnbfEq8YOnloP52fkiOzZ3384J7N42ztOq8EXWnQJ/CC1VapSq9d4Cf6t3cvXgipOrz5xcYpd/0rUX528bV0tHZAAU+EMomD/fyxWxu7aMkjF46vgCAOVqnWdOLvKc6fGex3f+tjEOnVqiXF27/LKI9I8CfwgFc9x7qfBHclkumJ7ge8+eAeCZk4vUHVwwPdHz+J577maqdcejR0/3vC8RCU+BP4SO+/PnezlpC/ADOyb5nh/KT/qV/t7tvVf4l+/aDMCDh+Z73peIhKfAH0In/cCf7jnwN/HksQWKlRoPH/GC/6KZyZ7Ht3d6golClgcPzvW8LxEJT4E/hILlh7f3sAQCwJV7tlB3cM9TJ7nnqZNcODPR09LIgUzGuGzXFA+owhdZVwr8IXR4rsjkSI7JLlbKXOma52wjmzHuePQYdz91khefvzWmEcILdk/xwME5ipVabPsUkfYU+EPImy/fW3UP3lr6V+/dyl989XFOLlb4oR+YiWF0nh+8eDulap1vHjgR2z5FpD0F/hA6Ml/k3KneL5AC+JVXXEI+azxv52Zeddm5sewT4NoLt1HIZfjCQ8/Gtk8RaU+BP4SenYvniliA6y7azrfe8SN86hdfRiEX3z+X8UKOf3P5uXzq3oNq64isEwX+kKnVHUdPlzg3psAH2DJeiDXsAz919R7mi1U+fe+h2PctImsp8IfMoVNLVOuO3Vt7ny/fby+9cJoX7p7iPZ9/hDOl6qCHIzL0FPhD5qnj3ho1cVwg1W9mxrtedzmzZ0r8xq336baHIn2mwB8yB/wrYp+zvfclENbDlXu28I4bn8dtDxzhpg/tZ75YGfSQRIaWAn/IPHlsgdF8pqe7Uq23t/6rC3nXay/j9kdmuf49X+UD/3KAuSUFv0jcersyRxLnkSOnuficSTKZ7m9SMgg/d91erjp/C7/7mYd452ce4t23PcxV52/h8l1TnLd1jF1bxtg5NcrOqTGmJwob7vcTSQIF/hBxznH/wTlufEF88+XX0xW7t/CxX7iO+5+Z45P3HuSuA8f58F1PUaycvYxyPmtcNDPJC86b4gW7p7hs1xTP27mJ8cLZ/5ydcxw7U+bEQpnpyQLbJ3u/GE1kI1PgD5GnTywxt1Th8vOmBj2Unrxgtxfk4IX2iYUyh04VOTy3xJH5IgdPLfHw4dN86eGj3Hr3MwCYeectgumoR+a87Uor1ty/5JxJfurqPbzh2gsYK2TX/xcTGTAF/hC584ljALz4gvjWvBk0M2N6coTpyZHGQSDgnOPQXJGHDs3z4KE5Hjo0z8nFMrW643k7N/OK553D7q3jbJsocGSuyOcfOsLv/+N3+YuvPs5//MHn8IZrLmBqxQ3Zi5Uah+eKVGt1dkyN9nQTeJEkMueSMxVu3759bv/+/YMexob18x/az3eemePrN1/f043Gh9m3njzBn37pUb726DFyGePSnZsYy2c5dKp41v17zbwW0+uv3sPrrjxP7wgksczsbufcvjDbqsIfEsfPlLj9kVlef/UehX0bV+/dxofeci0PHprjH79zmPsPzlGu1rnmOdu4YHqcPVvHKeQyPDG7wG0PHObmT9zPu297mH+/bzc/9qLdPHfHJp0wlg2rr4FvZjcAfwJkgfc55/6gn8+XZu/9yuOUq3Xe9NK9gx7KhnDZLu9kbzu/8oqLuevACT5051P8n395kr/62gE2jebYOTXK1FiesUKOiUKWzaN5LtkxyWW7prhi9xQTEZalrtbqnPBvSTle6H1Ja5F2+vavy8yywJ8DrwSeAb5lZp92zj3Ur+dMoxMLZf7mzif5638+wM9cez4Xn9P7HanEY2a85MJpXnLhNEfni9zx6DHuffokx06XmVuqMLdU4fCpJU4ulvno/qcByJh3z96rzt/CVXu2cMH0BJvHclRrjvlihe8fX+TAsQWeOLbAgWMLPHV8gUptua26dTzP3u0TXDwzySU7Jrn4nEkuOWcTu7aMkV3xzsI5R6XmKFVrlKp1StU65Wrd+7pSxwwyZuSyRtaMTMbIZYyMGdng84z3vay/TT6bIZ+1nt4h1uqOSq1Ote6oO0cu4z1f1n9evfscrL718M3spcA7nXOv8r/+TQDn3Ltb/Uy3PfzX/K+vUazUCX6Xxm+04lcLPl29TfDruxUbNx5b9dKsfK1a/bxr+pxnP9J8m+bjavY93PKHYA2a11yxk/f85AsZzavXPAizp0vcf/AU937/FN9+2vt4usX6QIVshvOnx7lw+wQXzkyya8soGTNOF6t8/8QiB46d4bGjCxw7U1rzcyO5DJW6F/D9Ov2Wy3gHCu8A4B0EcpkMhVymcaCp1utUa8vhXq05KvXOY1oZ/sFBp3FQyJz9eMaMtBweto4X+Pu3vbSrn01KD/884OkVXz8DXLt6IzO7CbgJ4Pzzz+/qiS6emVyukuysD2dVFMuPdd5meT921s8038/Z25xdxKz6Hmu3WfPzTcbV2HbVc+7YPMJ1F21fM4NF1tfMphGuv3QH11+6A/Aq3Sdmz3BkvsjcUoV8NsPkSI7zt42vqdZbmVus8NjsaR47eobDc0WKFa+CD4J/JJ/1PuYyjOSyFPzPg5VNq3VHve4a1XYt+LzuqPlfB3+q/sdytU61Xqfih3ml5gV7ueY9ZuAdDDKZxkHBO0AsHxi8x73AXrnvlc9Vd95BolavN8ZSrS2Pq1p3JGlCSb+t14ywflb4PwHc4Jx7q//1m4BrnXO/1OpnNEtHRCSaKBV+P9fSOQjsWfH1bv8xEREZgH4G/reAS8zsOWZWAH4a+HQfn09ERNroWw/fOVc1s18C/h/etMz3O+ce7NfziYhIe32d9Ouc+xzwuX4+h4iIhKP18EVEUkKBLyKSEgp8EZGUUOCLiKREopZHNrNZ4KlVD28Hjg1gOHHYqGPfqOOGjTt2jXv9bdSxrx73Bc65mTA/mKjAb8bM9oe9iixpNurYN+q4YeOOXeNefxt17L2MWy0dEZGUUOCLiKTERgj8WwY9gB5s1LFv1HHDxh27xr3+NurYux534nv4IiISj41Q4YuISAwU+CIiKZG4wDezbWb2BTN71P+4tcV2NTO71/8zsGWXzewGM3vEzB4zs5ubfH/EzD7qf/8uM9s7gGE2FWLsbzaz2RWv81sHMc7VzOz9ZnbUzB5o8X0zsz/1f6/vmNmL1nuMzYQY98vNbG7F6/076z3GZsxsj5ndbmYPmdmDZvarTbZJ3GsectxJfc1HzeybZnafP/Z3NdkmerY45xL1B/gj4Gb/85uBP2yx3ZkEjDULPA5cCBSA+4Dnr9rmPwN/4X/+08BHBz3uCGN/M/Bngx5rk7H/EPAi4IEW378RuA3vbpEvAe4a9JhDjvvlwGcHPc4m49oJvMj/fBPwvSb/VhL3moccd1JfcwMm/c/zwF3AS1ZtEzlbElfhA68DPuh//kHgRwc3lI6uAR5zzj3hnCsDf4c3/pVW/j4fA15hzW5Wu/7CjD2RnHN3ACfabPI64G+c5xvAFjPbuT6jay3EuBPJOXfYOXeP//lp4Lt496xeKXGvechxJ5L/Op7xv8z7f1bPsImcLUkM/B3OucP+50eAHS22GzWz/Wb2DTP70fUZ2hrNbtS++h9UYxvnXBWYA6bXZXTthRk7wI/7b9E/ZmZ7mnw/icL+bkn0Uv9t/G1mdtmgB7Oa3za4Cq/iXCnRr3mbcUNCX3Mzy5rZvcBR4AvOuZavedhs6esNUFoxsy8C5zb51jtWfuGcc2bWat7oBc65g2Z2IfBlM7vfOfd43GNNuc8AH3HOlczs5/GqiesHPKZhdg/ev+szZnYj8EngksEOaZmZTQIfB37NOTc/6PGE1WHciX3NnXM14Eoz2wL8g5ld7pxrev4nrIFU+M65H3HOXd7kz6eAZ4O3gv7Hoy32cdD/+ATwFbyj93oLc6P2xjZmlgOmgOPrMrr2Oo7dOXfcOVfyv3wf8OJ1Gluvwvy9JI5zbj54G++8u8XlzWz7gIcFgJnl8ULzb51zn2iySSJf807jTvJrHnDOnQJuB25Y9a3I2ZLEls6ngZ/zP/854FOrNzCzrWY24n++HXgZ8NC6jXBZmBu1r/x9fgL4svPPsgxYx7Gv6sG+Fq8HuhF8GvhZf+bIS4C5FW3CxDKzc4MerJldg/f/c+DFgT+mvwa+65z74xabJe41DzPuBL/mM35lj5mNAa8EHl61WfRsGfTZ6CZnp6eBLwGPAl8EtvmP7wPe539+HXA/3syS+4G3DHC8N+Kd/X8ceIf/2O8Cr/U/HwVuBR4DvglcOOjXOMLY3w086L/OtwOXDnrM/rg+AhwGKni94rcAbwPe5n/fgD/3f6/7gX2DHnPIcf/Sitf7G8B1gx6zP64fxDth+B3gXv/PjUl/zUOOO6mv+RXAt/2xPwD8jv94T9mipRVERFIiiS0dERHpAwW+iEhKKPBFRFJCgS8ikhIKfBGRAem0oN6qbS8wsy/5V75/xcx2R30+Bb6IyOB8gLUXVLXyHrz1iq7Am5757qhPpsAXERkQ12RBPTO7yMz+yczuNrOvmdml/reeD3zZ//x2uljsUIEvIpIstwC/7Jx7MfB24L3+4/cBP+Z//u+ATWYWaSHGgSyeJiIia/kLvV0H3LpipeMR/+PbgT8zszcDd+CtpVOLsn8FvohIcmSAU865K1d/wzl3CL/C9w8MP+68hdUi7VxERBLAecs3HzCzn4TGrSNf6H++3cyCzP5N4P1R96/AFxEZEDP7CHAn8Fwze8bM3gK8AXiLmd2Ht7BbcHL25cAjZvY9vBtD/Y/Iz6fF00RE0kEVvohISijwRURSQoEvIpISCnwRkZRQ4IuIpIQCX0QkJRT4IiIp8f8BhmM4emI0OZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c4d6033a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T22:03:48.155390Z",
     "start_time": "2021-06-16T22:03:48.139461Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2062376525.7269816\n",
      "421884087.77827096\n",
      "421884087.77827096\n",
      "673543200.2780268\n"
     ]
    }
   ],
   "source": [
    "data = outcomes_total_dikerings_load_DF['Total costs']\n",
    "print(data.max())\n",
    "print(data.min())\n",
    "print(data.quantile(.2))\n",
    "print(data.quantile(.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020ea1f3",
   "metadata": {},
   "source": [
    "As treshold we take 441575627 and 3071536698 since these are the lowest and highest 20% of the damage cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "22aeded1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T22:04:06.151754Z",
     "start_time": "2021-06-16T22:04:01.719265Z"
    }
   },
   "outputs": [],
   "source": [
    "data = outcomes_total_dikerings_load_DF['Total costs']\n",
    "\n",
    "#y = data < 242382297.91068038\n",
    "y = data > 673543200\n",
    "prim_alg_damage_low = prim.Prim(experiments,y, threshold=0.8)\n",
    "#prim_alg_damage_high = prim.Prim(experiments,y1, threshold=0.8)\n",
    "\n",
    "box_damage_low = prim_alg_damage_low.find_box()\n",
    "#box_damage_high = prim_alg_damage_high.find_box()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f2b868",
   "metadata": {},
   "source": [
    "#### Lowest 20% damage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "464cbcbb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:47.436478Z",
     "start_time": "2021-06-16T21:42:47.016623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = box_damage_low.show_tradeoff()\n",
    "fig.suptitle('20% lowest damage delta threshold of deaths tradeoff', fontsize=16)\n",
    "plt.savefig('Figures_dewy/policies_damage_tradeoff.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e201bd04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:47.467427Z",
     "start_time": "2021-06-16T21:42:47.441464Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coverage    0.959748\n",
      "density     0.803158\n",
      "id                 1\n",
      "mass            0.95\n",
      "mean        0.803158\n",
      "res_dim            1\n",
      "Name: 1, dtype: object\n",
      "\n",
      "              box 1                                       \n",
      "                min       max                    qp values\n",
      "A.3_pfail  0.000274  0.949699  [-1.0, 0.19741162165146084]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "box_damage_low.inspect(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d2e00aea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:47.786394Z",
     "start_time": "2021-06-16T21:42:47.473379Z"
    }
   },
   "outputs": [
    {
     "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": [
    "fig = box_damage_low.inspect(1,style='graph')\n",
    "fig.suptitle('20% lowest total damage in delta constraints', fontsize=16)\n",
    "plt.savefig('Figures_dewy/policies_damage_constraints.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8a914d40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:47.833216Z",
     "start_time": "2021-06-16T21:42:47.796367Z"
    }
   },
   "outputs": [],
   "source": [
    "box_damage_low.select(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4e448e4b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T21:42:48.323620Z",
     "start_time": "2021-06-16T21:42:47.846207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 241.25x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = box_damage_low.show_pairs_scatter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c01935c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-16T22:04:13.284047Z",
     "start_time": "2021-06-16T22:04:11.349222Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python37-32\\lib\\site-packages\\ema_workbench\\analysis\\dimensional_stacking.py:404: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ooi = pd.DataFrame(y[:, np.newaxis], columns=[ooi_label])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = outcomes_total_dikerings_load_DF['Total costs']\n",
    "y = data > 673543200\n",
    "\n",
    "fig = dimensional_stacking.create_pivot_plot(experiments, y)\n",
    "fig.suptitle('Dimensional stacking damage total delta', fontsize=16)\n",
    "plt.savefig('Figures_dewy/policies_damage_stacking.png')\n",
    "plt.show()"
   ]
  }
 ],
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