Final Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ECI 273 Final Project\n",
    "#### Chen Zhang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import cvxpy as cvx\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import functions in another Jupyter File\n",
    "from ipynb.fs.full.functions import get_year\n",
    "from ipynb.fs.full.functions import get_obj\n",
    "from ipynb.fs.full.functions import get_forecast\n",
    "from ipynb.fs.full.functions import folsom_complete\n",
    "from ipynb.fs.full.functions import folsom_limit\n",
    "from ipynb.fs.full.functions import folsom_forecast\n",
    "from ipynb.fs.full.functions import SLOP\n",
    "from ipynb.fs.full.functions import visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Preperation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "storage    0\n",
       "outflow    0\n",
       "inflow     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data from Folsom Dam\n",
    "# %run -i cdec-scraper # run interactive .py ref: https://ipython.org/ipython-doc/3/interactive/magics.html#magic-run\n",
    "\n",
    "%run cdec-scraper\n",
    "\n",
    "sensor = [15, 23, 76]\n",
    "c = ['storage','outflow','inflow'] # inflow, outflow are cfs, storage are AF\n",
    "df_folsom = pd.DataFrame(columns=c)  \n",
    "\n",
    "for i,s in enumerate(sensor):\n",
    "    url = build_url('FOL', s, duration='D', sd='1996-10-01', ed='2019-10-01')\n",
    "    df = pd.read_csv(url)\n",
    "    series = reformat_series(df)\n",
    "    df_folsom[c[i]] = series\n",
    "\n",
    "# check if there is NA value\n",
    "df_folsom.isna().sum()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check uncomformed item and forward fill\n",
    "strdata = df_folsom.inflow.loc[-df_folsom.inflow.str.isnumeric()]\n",
    "df_folsom.inflow[strdata.index] = np.NaN\n",
    "df_folsom.inflow.fillna(method = 'ffill', inplace=True)\n",
    "strdata = df_folsom.outflow.loc[-df_folsom.outflow.str.isnumeric()]\n",
    "df_folsom.outflow[strdata.index] = np.NaN\n",
    "df_folsom.outflow.fillna(method = 'ffill', inplace=True)\n",
    "np.all(np.asarray(list(map(type, df_folsom.storage))) == int) # true means all storage values are integer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perfect Foresight Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Test a particular case of perfect foresight optimization\n",
    "# y = 1997 # year\n",
    "# umax = 40 # benchmark (TAF/D) \n",
    "# collective_obj, df_Q = folsom_complete(y, umax, detail='y', solver='ECOS')\n",
    "# visualize(df_Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test wet years of perfect foresight optimization\n",
    "# year (1997, 2006, 2017)\n",
    "\n",
    "umax = 40 # benchmark (TAF/D) \n",
    "obj1997, df1997 = folsom_complete(df_folsom, 1997, umax, detail='y',solver='ECOS')\n",
    "obj2006, df2006 = folsom_complete(df_folsom, 2006, umax, detail='y',solver='ECOS')\n",
    "obj2017, df2017 = folsom_complete(df_folsom, 2017, umax, detail='y',solver='ECOS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize wet years of perfect foresight optimization\n",
    "plt.rcParams['figure.figsize'] = [8, 4]\n",
    "\n",
    "df = df1997.resample('M').agg({'x': 'first', 'inflow': np.sum, 'u': np.sum})\n",
    "x_1997 = df.x + df.inflow\n",
    "plt.scatter(x_1997.values, df.u.values, color='C0') \n",
    "df = df2006.resample('M').agg({'x': 'first', 'inflow': np.sum, 'u': np.sum})\n",
    "x_2006 = df.x + df.inflow\n",
    "plt.scatter(x_2006.values, df.u.values, color='C0')\n",
    "df = df2017.resample('M').agg({'x': 'first', 'inflow': np.sum, 'u': np.sum})\n",
    "x_2007 = df.x + df.inflow\n",
    "plt.scatter(x_2007.values, df.u.values, color='C0')\n",
    "plt.plot([0,1100],[0,1100], color='k', linewidth=2)\n",
    "plt.plot([975, 975+1100],[0,1100], color='k', linewidth=2)\n",
    "plt.plot([0,2000],[150,150], '--', color='k')\n",
    "plt.ylim([0,1100])\n",
    "plt.xlim([0,2000])\n",
    "plt.xlabel('Water Available (S+Q)')\n",
    "plt.ylabel('Release (u)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test dry years of perfect foresight optimization\n",
    "# year (2007-2010, 2012,2015)\n",
    "umax = 80 # benchmark (TAF/D) \n",
    "objdry1, dfdry1 = folsom_complete(df_folsom, 2007, umax, 2010, detail='y', solver='ECOS')\n",
    "objdry2, dfdry2 = folsom_complete(df_folsom, 2012, umax, 2015, detail='y', solver='ECOS')\n",
    "\n",
    "df = dfdry1.resample('M').agg({'x': 'first', 'inflow': np.sum, 'u': np.sum})\n",
    "x_dry1 = df.x + df.inflow\n",
    "plt.scatter(x_dry1.values, df.u.values, color='C1') \n",
    "df = dfdry2.resample('M').agg({'x': 'first', 'inflow': np.sum, 'u': np.sum})\n",
    "x_dry2 = df.x + df.inflow\n",
    "plt.scatter(x_dry2.values, df.u.values, color='C1')\n",
    "plt.plot([0,1000],[0,1000], color='k', linewidth=2)\n",
    "plt.plot([975, 975+1000],[0,1000], color='k', linewidth=2)\n",
    "plt.plot([0,2000],[150,150], '--', color='k')\n",
    "plt.ylim([0,1000])\n",
    "plt.xlim([0,2000])\n",
    "plt.xlabel('Water Available (S+Q)')\n",
    "plt.ylabel('Release (u)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test & Visualize other (normal) years of perfect foresight optimization\n",
    "# year (1998-2005)\n",
    "umax = 80 # benchmark (TAF/D) \n",
    "objnormal, dfnormal = folsom_complete(df_folsom, 1998, umax, 2005, detail='y', solver='ECOS')\n",
    "\n",
    "df = dfnormal.resample('M').agg({'x': 'first', 'inflow': np.sum, 'u': np.sum})\n",
    "x_plot = df.x + df.inflow\n",
    "plt.scatter(x_plot.values, df.u.values, color='C2')\n",
    "plt.plot([0,1000],[0,1000], color='k', linewidth=2)\n",
    "plt.plot([975, 975+1000],[0,1000], color='k', linewidth=2)\n",
    "plt.plot([0,2000],[150,150], '--', color='k')\n",
    "plt.ylim([0,1000])\n",
    "plt.xlim([0,2000])\n",
    "plt.xlabel('Water Available (S+Q)')\n",
    "plt.ylabel('Release (u)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Limited Foresight Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test for a particular case of limited foresight optimization\n",
    "y = 1997 # year\n",
    "h = 7 # foresight horizon (days)\n",
    "umax = 40 # benchmark (TAF/D) \n",
    "collective_obj, last_S, df_Q = folsom_limit(df_folsom, y, h, umax, solver='ECOS')\n",
    "# df_Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x936 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test for a range years of limited foresight optimization\n",
    "y1 = 1997 # year\n",
    "y2 = 2010\n",
    "h = 14 # foresight horizon (days)\n",
    "umax = 80 # benchmark (TAF/D) \n",
    "collective_obj, last_S, df_Q = folsom_limit(df_folsom, y1, h, umax, y2, solver='ECOS')\n",
    "# collective_obj, last_S, df = folsom_limit(y, h, umax, detail = 'y', solver='ECOS')\n",
    "visualize(df_Q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation of lead time and benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success\n"
     ]
    }
   ],
   "source": [
    "## specify horizon, storm benchmark and year we are interested in\n",
    "horizon = [1, 2, 3, 7, 10, 14] # foresight horizon (days)\n",
    "benchmark = [16, 40, 80, 160, 230] # TAF/D \n",
    "year = [1997, 2006, 2017]\n",
    "\n",
    "# initiate the result dataframe\n",
    "df_3yresult = pd.DataFrame(columns=['Q','h','umax', 'total objective','total spill','total shortage', \n",
    "                                  'storm days', 'flood days', 'emergency days', 'shortage days', 'last storage'])  \n",
    "# set up index tracker\n",
    "i = 0\n",
    "# loop for optimization results\n",
    "for y in year:\n",
    "    for h in horizon:\n",
    "        for umax in benchmark:\n",
    "            i+=1\n",
    "#             print(i, y, h, umax)\n",
    "            collective_obj, last_S, df = folsom_limit(df_folsom, y, h, umax, solver='ECOS')\n",
    "            df_3yresult.loc[i,'Q'] = y\n",
    "            df_3yresult.loc[i,'h'] = h\n",
    "            df_3yresult.loc[i,'umax'] = umax\n",
    "            df_3yresult.loc[i,'total objective']= get_obj(df)  # to compare with perfect foresight model \n",
    "            df_3yresult.loc[i,'total spill']= df.spill.sum()\n",
    "            df_3yresult.loc[i,'total shortage']= df.shortage.sum()\n",
    "            df_3yresult.loc[i,'storm days']= sum(df.u.values>40)\n",
    "            df_3yresult.loc[i,'flood days']= sum(df.u.values>80)\n",
    "            df_3yresult.loc[i,'emergency days']= sum(df.u.values>230)\n",
    "            df_3yresult.loc[i,'shortage days']= sum(df.u.values<4.99)   \n",
    "            df_3yresult.loc[i,'last storage']= last_S\n",
    "print('success')            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide by year\n",
    "df_result1997 = df_3yresult.loc[1:30]\n",
    "df_result2006 = df_3yresult.loc[31:60]\n",
    "df_result2017 = df_3yresult.loc[61:90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>h</th>\n",
       "      <th>umax</th>\n",
       "      <th>total objective</th>\n",
       "      <th>total spill</th>\n",
       "      <th>total shortage</th>\n",
       "      <th>storm days</th>\n",
       "      <th>flood days</th>\n",
       "      <th>emergency days</th>\n",
       "      <th>shortage days</th>\n",
       "      <th>last storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>9566.6</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00833375</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>9566.6</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00832085</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>9566.6</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00824917</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>9566.6</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00826537</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>230</td>\n",
       "      <td>9566.6</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00829798</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>4546.45</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00814928</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>4243.44</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00814869</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>4831.99</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00814349</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>7816.27</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00814198</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>230</td>\n",
       "      <td>7795.11</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00813992</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>2238.32</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00804985</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>1710.53</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00804562</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>2796.69</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00804805</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>5129.55</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00804883</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>230</td>\n",
       "      <td>7350.24</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00804647</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2006</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>-624.378</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00764995</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>2006</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>-872.231</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00764999</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>2006</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>-519.224</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00765004</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>2006</td>\n",
       "      <td>7</td>\n",
       "      <td>160</td>\n",
       "      <td>2164.55</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00764926</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>2006</td>\n",
       "      <td>7</td>\n",
       "      <td>230</td>\n",
       "      <td>1247.28</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00764698</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>2006</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>-891.39</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00744998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>2006</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>-891.39</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00745012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>2006</td>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>-719.712</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00744986</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>2006</td>\n",
       "      <td>10</td>\n",
       "      <td>160</td>\n",
       "      <td>755.465</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00745003</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>2006</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>-114.915</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00744954</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>2006</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>-891.39</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00724994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>2006</td>\n",
       "      <td>14</td>\n",
       "      <td>40</td>\n",
       "      <td>-891.39</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00725</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>2006</td>\n",
       "      <td>14</td>\n",
       "      <td>80</td>\n",
       "      <td>-432.842</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00725</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>2006</td>\n",
       "      <td>14</td>\n",
       "      <td>160</td>\n",
       "      <td>753.424</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00724987</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>2006</td>\n",
       "      <td>14</td>\n",
       "      <td>230</td>\n",
       "      <td>-737.839</td>\n",
       "      <td>3362.69</td>\n",
       "      <td>0.00724983</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890.105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q   h umax total objective total spill total shortage  storm days  \\\n",
       "31  2006   1   16          9566.6     3362.69     0.00833375          11   \n",
       "32  2006   1   40          9566.6     3362.69     0.00832085          11   \n",
       "33  2006   1   80          9566.6     3362.69     0.00824917          11   \n",
       "34  2006   1  160          9566.6     3362.69     0.00826537          11   \n",
       "35  2006   1  230          9566.6     3362.69     0.00829798          11   \n",
       "36  2006   2   16         4546.45     3362.69     0.00814928          13   \n",
       "37  2006   2   40         4243.44     3362.69     0.00814869          11   \n",
       "38  2006   2   80         4831.99     3362.69     0.00814349          14   \n",
       "39  2006   2  160         7816.27     3362.69     0.00814198          11   \n",
       "40  2006   2  230         7795.11     3362.69     0.00813992          11   \n",
       "41  2006   3   16         2238.32     3362.69     0.00804985          13   \n",
       "42  2006   3   40         1710.53     3362.69     0.00804562          10   \n",
       "43  2006   3   80         2796.69     3362.69     0.00804805          14   \n",
       "44  2006   3  160         5129.55     3362.69     0.00804883          13   \n",
       "45  2006   3  230         7350.24     3362.69     0.00804647          12   \n",
       "46  2006   7   16        -624.378     3362.69     0.00764995           9   \n",
       "47  2006   7   40        -872.231     3362.69     0.00764999           8   \n",
       "48  2006   7   80        -519.224     3362.69     0.00765004          12   \n",
       "49  2006   7  160         2164.55     3362.69     0.00764926          10   \n",
       "50  2006   7  230         1247.28     3362.69     0.00764698          16   \n",
       "51  2006  10   16         -891.39     3362.69     0.00744998           0   \n",
       "52  2006  10   40         -891.39     3362.69     0.00745012           0   \n",
       "53  2006  10   80        -719.712     3362.69     0.00744986          13   \n",
       "54  2006  10  160         755.465     3362.69     0.00745003          10   \n",
       "55  2006  10  230        -114.915     3362.69     0.00744954           9   \n",
       "56  2006  14   16         -891.39     3362.69     0.00724994           0   \n",
       "57  2006  14   40         -891.39     3362.69        0.00725           0   \n",
       "58  2006  14   80        -432.842     3362.69        0.00725          11   \n",
       "59  2006  14  160         753.424     3362.69     0.00724987           8   \n",
       "60  2006  14  230        -737.839     3362.69     0.00724983           9   \n",
       "\n",
       "    flood days emergency days shortage days last storage  \n",
       "31           1              0             0      890.105  \n",
       "32           1              0             0      890.105  \n",
       "33           1              0             0      890.105  \n",
       "34           1              0             0      890.105  \n",
       "35           1              0             0      890.105  \n",
       "36           0              0             0      890.105  \n",
       "37           0              0             0      890.105  \n",
       "38           0              0             0      890.105  \n",
       "39           3              0             0      890.105  \n",
       "40           3              0             0      890.105  \n",
       "41           0              0             0      890.105  \n",
       "42           0              0             0      890.105  \n",
       "43           0              0             0      890.105  \n",
       "44           0              0             0      890.105  \n",
       "45           1              0             0      890.105  \n",
       "46           0              0             0      890.105  \n",
       "47           0              0             0      890.105  \n",
       "48           0              0             0      890.105  \n",
       "49           0              0             0      890.105  \n",
       "50           0              0             0      890.105  \n",
       "51           0              0             0      890.105  \n",
       "52           0              0             0      890.105  \n",
       "53           0              0             0      890.105  \n",
       "54           0              0             0      890.105  \n",
       "55           0              0             0      890.105  \n",
       "56           0              0             0      890.105  \n",
       "57           0              0             0      890.105  \n",
       "58           0              0             0      890.105  \n",
       "59           0              0             0      890.105  \n",
       "60           0              0             0      890.105  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result2006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAFNCAYAAAC0UO6gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de7xcVX338c83CXcICQYwJlGCDVagGCFgqtUXgmCglmDFClVJlacRHlCpWgV6wSK2eOUpValRIonFAAoIYhDSGKCt3ALEhKsckMshkRiuQWggye/5Y6+RzWHmzMyes8+cmfm++9qvM/Pba++99qnhd9baa6+liMDMzMyKGdXuCpiZmXUyJ1IzM7MWOJGamZm1wInUzMysBU6kZmZmLXAiNTMza4ETqZmZvYyk8yWdWeC43SSFpDE19p8m6but13B4ztsoJ1Izsw4j6UFJ7yqrfFki4p8j4v+0cg5JB0rqH+rztsKJ1MzMrAVOpGZmHUTS94HXAj+R9Kykz6b4EZLulPSUpGslvbFO+R9K+o2kpyVdL2mvBq8/StLfS3pI0lpJCyXtOKDYRyWtlrRG0qdzx35e0n/kvs+U9ItU519KOjC3bydJ30vneVLSjyVtB1wFvCbdy7OSXpM/r6SfSTppQJ1/KenP0+c/lLRE0hOS7pX0F4395mtzIjUz6yAR8WHgYeDPImL7iPiypD2ARcDJwM7AYrLEuWW18ulUVwHTgF2A24ALGqzCX6XtncDuwPbANwaUeWc696HAKdW6lSVNAn4KnAnsBHwGuETSzqnI94Ftgb1SHc+OiN8BhwGr071sHxGrB5z6B8AxuevsCbwO+GlKxEtSmV1SuW81+kdELU6kZmad7wPATyNiSUS8CHwV2AZ4a60DImJ+RKyPiA3A54E3VWlZVvNB4OsR8UBEPAucChw9YIDRP0XE7yJiFfA9cokt50PA4ohYHBGbI2IJsBw4XNJEsoR5fEQ8GREvRsR1DdQN4DJguqTX5ep7abrP9wAPRsT3ImJjRNwGXAIc1eC5q3IiNTPrfK8BHqp8iYjNwCPApGqFJY2WdJak+yU9AzyYdk1o9lrp8xhg11zskQH7X1PlPK8D3p+6dZ+S9BTwJ8BEYArwREQ82UB9XiYi1pO1dI9OoaN5qbX9OuAtA675QeDVzV4nr+oQZTMzG9EGLtu1GvijyhdJIktGj9Yo/5fAbOBdZEl0R+BJQA1cezVZQqp4LbAReAyYnGJTgHty+wd2v0KWbL8fEX89cEdqke4kaVxEPDVgdyNLli0CTpd0PVnLfFnumtdFxCENnKNhbpGamXWex8ieT1ZcDPyppIMlbQF8GtgA/KJG+R3S/sfJnkP+cxPXXgT8jaSpkrZPx14UERtzZf5B0rbp2eNHgIuqnOc/gD+T9O7UQt46vdoyOSLWkD3D/Zak8ZK2kPSO3L28qk439GKyZH9GqtvmFL8S2EPSh9M5t5C0f2VgVlFOpGZmnedfgL9P3ZOfiYh7yZ45/huwDvgzssFFL1QrDywk63J9FLgLuLGJa88nGwh0PfBr4H+Bjw8ocx3QBywFvhoR1ww8SUQ8QtYqPg34LVlr8W95KS99GHiRrGW7lmwgFRFxD1kyfyDdzyu6jdPz0EvJWtw/yMXXkw2AOpqslfwb4EvAVk3c/yvIC3ubmdlwkHQGMDkiPtruugwlt0jNzKx06bntnmSt2K7iwUZmZjYcbiN7LntSvYKdxl27ZmZmLXDXrpmZjTiSpkhaJunuNPXhJ1P8K5LukbRS0mWSxqX4bpKel7Qibf+eO9d+klZJ6pN0TupmrkxDuETSfenn+BRXKteXrrPvYHV1IjUzs5FoI/DpiHgjMBM4MU33twTYOyL2AX5FNrNSxf0RMT1tx+fi5wJzyaYtnAbMSvFTgKURMY1shPEpKX5YruzcdHxNHf2MdAeNjp3Zot3VGHLjJ2/T7iqURjuNbXcVSrH6hefaXYXSPPH8xvqFOtCG519sdxXK8cwG4vkXG5lYYUi8SdvFejY1fdyv2XB1RMyqtT+9S7omfV4v6W5g0oBXaW6kzvR+aXKHsRFxQ/q+EDiS7D3V2cCBqegC4Frgcym+MLJnnzdKGidpYqrTK3R0It2ZLTjzZRNsdIf3fWrvdlehNFsefXC7q1CKf3poRburUJoL7nq83VUoxYMrf9PuKpRi46JfDuv11rOp0H+HP8ivGpmOEMi6bYE3AzcN2PVRXj7Zw1RJtwPPAH8fEf9FNk1ifv3Sfl6aOnHXSnKMiDWSdknxSbx8msPKMd2XSM3MrP1GFXlIuJkJkpbnIvMiYt7AYmn2pEuAkyPimVz878i6fyvz6K4BXhsRj0vaD/hxmlmpWuu83ijbpo5xIjUzs8JE4US6LiJmDHrubLrDS4ALIuLSXHwO2UouB6fu18psRhvS51sl3Q/sQdaanJw77WRemvv3sUqXbeoCXpvi/WTzBVc75hU82MjMzIpTlkib3eqeNhtZex5wd0R8PRefRfYc84iIeC4X31nS6PR5d7KBQg+krtv1yhYRF3AscHk67ApgTvo8Z0D82DR6dybwdK3no+AWqZmZtaBwi7S+t5HNt7tKUmUQwmnAOWRz4y5Jb7HcmEbovgM4Q9JGYBPZWqZPpONOAM4nWwnmqrQBnAVcLOk4ssXP35/ii4HDyeYLfo5s4v2anEjNzKwlo0oYIxwR/031Z5WLa5S/hKwbuNq+5cArRnFGxOPAK0ZApu7iExutqxOpmZkVp9JapB3DidTMzAorsWu3YziRmplZcW6ROpGamVlxbpE6kZqZWYucSM3MzIpy164TqZmZFeeuXSdSMzNrUZoYoWc5kZqZWXHu2nUiNTOz4ty160RqZmatcIvUq7+YmZm1wi1SMzMrzF27TqRmZtYiJ1IzM7Oi/IzUidTMzIpz126bBhtJmi9praQ7BsQ/LuleSXdK+nI76mZmZk1ILdJmt27Srhbp+cA3gIWVgKR3ArOBfSJig6Rd2lQ3MzNrkIBRvT2xUXsSaURcL2m3AeETgLMiYkMqs3a462VmZs3rthZms0bS7e8BvF3STZKuk7R/uytkZmZ1uGt3RA02GgOMB2YC+wMXS9o9IiJfSNJcYC7AhBFVfTOz3uPBRiMrkfYDl6bEebOkzcAE4Lf5QhExD5gHsLu2jlecxczMhpEY1eMPSUfS3xE/Bg4CkLQHsCWwrq01MjOzwQk0Wk1v3aQtLVJJi4ADgQmS+oHTgfnA/PRKzAvAnIHdumZmNrIIkFukwy8ijomIiRGxRURMjojzIuKFiPhQROwdEftGxM/bUTczM2uCskTa7Fb3tNIUScsk3Z3mFvhkiu8kaYmk+9LP8SkuSedI6pO0UtK+uXPNSeXvkzQnF99P0qp0zDlKK5TXukYtI6lr18zMOlBJXbsbgU9HxBvJBqGeKGlP4BRgaURMA5am7wCHAdPSNhc4F7KkSNbr+RbgAOD0XGI8N5WtHDcrxWtdoyonUjMzK07Nt0YbaZFGxJqIuC19Xg/cDUwim7hnQSq2ADgyfZ4NLIzMjcA4SROBdwNLIuKJiHgSWALMSvvGRsQN6THiwgHnqnaNqkbSqF0zM+tAZQ8eShP4vBm4Cdg1ItZAlmxzs+BNAh7JHdafYoPF+6vEGeQaVTmRmplZO0yQtDz3fV56vfFlJG0PXAKcHBHPpMeY1VTbEQXiTXMiNTOzwiSKvke6LiJmDH5ubUGWRC+IiEtT+DFJE1NLcSJQmU62H5iSO3wysDrFDxwQvzbFJ1cpP9g1qvIzUjMza0lJo3YFnAfcHRFfz+26AqiMvJ0DXJ6LH5tG784Enk7ds1cDh0oanwYZHQpcnfatlzQzXevYAeeqdo2q3CI1M7PiVNoz0rcBHwZWSVqRYqcBZ5FNIXsc8DDw/rRvMXA40Ac8B3wEICKekPQF4JZU7oyIeCJ9PoFsNbJtgKvSxiDXqMqJ1MzMWiBUwmS7EfHfVH+OCXBwlfIBnFjjXPPJJv0ZGF8O7F0l/ni1a9TiRGpmZoWpvBZpx3AiNTOzlvT6FIFOpGZmVpycSJ1IzcysBd23mkuznEjNzKywFt4j7RpOpGZm1hJ37ZqZmRXlUbtOpGZm1hq3SM3MzIqSBxs5kZqZWWGCUmY26iROpGZmVpzfI3UiNTOz1ozq8a7d3m6Pm5mZtcgtUjMzK0xqbH3RbuZEasNL3dkJsv7FaHcVSvO7Fza1uwql2NSl90Ub/qfoUbtmZmZFebCRE6mZmbXILVIzM7OC3CJ1IjUzs1YIRnfn2IdGOZGamVlxAtwiNTMzK86jds3MzIpyi9SJ1MzMWiGP2m13BczMrHPJo3adSM3MrEU9Pmq3t+/ezMxak1qkzW51TyvNl7RW0h252EWSVqTtQUkrUnw3Sc/n9v177pj9JK2S1CfpHElK8Z0kLZF0X/o5PsWVyvVJWilp33p1dSI1M7MWpGekzW71nQ/Mygci4gMRMT0ipgOXAJfmdt9f2RcRx+fi5wJzgWlpq5zzFGBpREwDlqbvAIflys5Nxw/KidTMzIoTpSTSiLgeeKLqJbNW5V8AiwatmjQRGBsRN0REAAuBI9Pu2cCC9HnBgPjCyNwIjEvnqcmJ1MzMWlJG124dbwcei4j7crGpkm6XdJ2kt6fYJKA/V6Y/xQB2jYg1AOnnLrljHqlxTFUebGRmZu0wQdLy3Pd5ETGvwWOP4eWt0TXAayPicUn7AT+WtBdZe3mgegvNNX2ME6mZmRVX6dpt3rqImNH05aQxwJ8D+1ViEbEB2JA+3yrpfmAPstbk5Nzhk4HV6fNjkiZGxJrUdbs2xfuBKTWOqcpdu2Zm1gLBqFHNb8W9C7gnIn7fZStpZ0mj0+fdyQYKPZC6bNdLmpmeqx4LXJ4OuwKYkz7PGRA/No3enQk8XekCrsUtUjMzK07lzLUraRFwIFkXcD9wekScBxzNKwcZvQM4Q9JGYBNwfERUBiqdQDYCeBvgqrQBnAVcLOk44GHg/Sm+GDgc6AOeAz5Sr65OpGZm1poSZjaKiGNqxP+qSuwSstdhqpVfDuxdJf44cHCVeAAnNlNXJ1IzMyuu+DPSruFEamZmLRiS11k6mhOpmZkVJ3p+rl0nUjMzK85du+17/UXS6DQLxZXp+1RJN6UJhC+StGW76mZmZo0RbZnZaERpZ3v8k8Ddue9fAs5OEwg/CRzXllqZmVnjVNqk9R2jLYlU0mTgT4Hvpu8CDgJ+lIrkJxA2M7ORbJSa37pIu56R/j/gs8AO6furgKciYmP6XneSYDMzGwFKmpChkwx7i1TSe4C1EXFrPlylaNVJgiXNlbRc0vL1bCqljmZm1oThnSJwxGlHi/RtwBGSDge2BsaStVDHSRqTWqU1JwlOqwPMA9hdW9ebxd/MzErVfV21zRr2Pwsi4tSImBwRu5HNmfjziPggsAw4KhXLTyBsZmYjlej5FulIupvPAZ+S1Ef2zPS8NtfHzMwa0eOJtK0TMkTEtcC16fMDwAHtrI+ZmTVJ7trtrj8LzMzMhpmnCDQzs9Z0WVdts5xIzcysNU6kZmZmBfkZqROpmZm1qMdbpHXvXtJhVWLHl1MdMzPrKH6PtKFRu/8g6aDKF0mfA2aXVyUzM+scBSas77Ku4Ea6do8ArpT0t8As4A9TzMzMrOtamM2qm0gjYp2kI4D/BG4FjooIz3FrZmYvde32sJqJVNJ6Xr4Cy5bA7sBRkiIixpZdOTMzG+lEtqR076qZSCNih7Tg9pSIeHgY62RmZp2kx1ukg9596sK9bJjqYmZmnaakUbuS5ktaK+mOXOzzkh6VtCJth+f2nSqpT9K9kt6di89KsT5Jp+TiUyXdJOk+SRdJ2jLFt0rf+9L+3erVtZE/I26UtH8D5czMrOeUNmr3fLIBrgOdHRHT07YYQNKeZMty7pWO+Zak0ZJGA98EDgP2BI5JZQG+lM41DXgSOC7FjwOejIg/AM5O5QbVSCJ9J3CDpPslrZS0StLKBo4zM7NuV1KLNCKuB55osBazgQsjYkNE/BroI1tN7ACgLyIeiIgXgAuB2emx5UHAj9LxC4Ajc+dakD7/CDhYdR4CN/L6yysmZDAzM/u94X0v9CRJxwLLgU9HxJPAJODGXJn+FAN4ZED8LWRrXj8VERurlJ9UOSYiNkp6OpVfV6tCdf8siIiHIuIh4HmyUbyVzczMep6KtkgnSFqe2+Y2cLFzgdcD04E1wNdeqsQrRIH4YOeqqW6LNL1D+jXgNcBa4HXA3WR90WZm1suKv0e6LiJmNHNARDz2+8tK3wGuTF/7gSm5opOB1elztfg6YJykMalVmi9fOVe/pDHAjtTpYm7k7r8AzAR+FRFTgYOB/2ngODMzsyEjaWLu63uByojeK4Cj04jbqcA04GbgFmBaGqG7JdmApCvSGynLgKPS8XOAy3PnmpM+HwX8vN4kRI08I30xIh6XNErSqIhYJqnuKCYzM+sRJTwjlbQIOJCsC7gfOB04UNJ0sq7WB4GPAUTEnZIuBu4CNgInRsSmdJ6TgKuB0cD8iLgzXeJzwIWSzgRuB85L8fOA70vqI2uJHl2vro0k0qckbQ9cD1wgaW2qqJmZ9TqplAkZIuKYKuHzqsQq5b8IfLFKfDGwuEr8AbJRvQPj/wu8v5m6NpJIZwP/C/wN8EGy/uIzmrmImZl1sR6f2aiRSet/ByBpLPCT0mtkZmadpcuWRWtWI6N2P0bWAn0e2Ew2RivIJrC3Eozafst2V6E8W23f7hqU4tkXu/eNsNjcnffmNayGSEldu52kka7dzwB7RUTNl1HNzKyHyYm0nvuB58quiJmZdSgn0rpOBX4h6SZgQyUYEZ8orVZmZtYZ3LXbUCL9NvBzYBXZM1IzM7OXuEVa18aI+FTpNTEzsw4kJ9IGyixLkwn/hJd37Ta6vI2ZmXUzJ9K6/jL9PDUX8+svZmbWyqT1XaORCRmmDkdFzMysE7lrt5EWKZLeCuyWLx8RC0uqk5mZdRIn0sFJ+j7ZQqorgE0pHIATqZlZz3OLtJEW6Qxgz3rrsZmZWY/q8UTayN3fAby67IqYmZl1opotUkk/IevC3QG4S9LNvPz1lyPKr56ZmY1ontlo0K7drw5bLczMrGNJo9tdhbaqmUgj4joASVOBNWnVcCRtA+w6PNUzM7ORzYONGrn7H/LyOXY3pZiZmfU6kSXSZrcu0sio3TER8ULlS0S8IKmLV542M7OmdFlibFYjd/9bSb8fWCRpNuBFvs3MDEiDjZrdukgjLdLjgQskfYOsEf8IcGyptTIzs87R4y3SRubavR+YKWl7QBGxvvxqmZlZR5AHGzUyReBWwPtIc+1KAiAizii1ZmZm1hl6PJE2cveXA7OBjcDvcpuZmVkpz0glzZe0VtIdudhXJN0jaaWkyySNS/HdJD0vaUXa/j13zH6SVknqk3SOUmtQ0k6Slki6L/0cn+JK5frSdfate/sN/IomR8QHIuLLEfG1ytbAcTVJ+htJd0q6Q9IiSVtLmirppnRTF3lksJlZJ1BZr7+cD8waEFsC7B0R+wC/4uXrZN8fEdPTdnwufi4wF5iWtso5TwGWRsQ0YGn6DnBYruzcdPygGrmbX0j6owbKNUTSJOATwIyI2BsYDRwNfAk4O93Uk8BxQ3VNMzMrSUnvkUbE9cATA2LXRMTG9PVGYPKgVZMmAmMj4oa08MpC4Mi0ezawIH1eMCC+MDI3AuPSeWpqJJH+CXCrpHtTM3eVpJUNHDeYMcA2ksYA2wJrgIOAH6X9+ZsyM7MRq7QWaT0fBa7KfZ8q6XZJ10l6e4pNAvpzZfpTDGDXiFgDkH7ukjvmkRrHVNXI6y+HNVCmYRHxqKSvAg8DzwPXALcCT+X+0qhbcTMzGyGKJcYJkpbnvs+LiHkNXU76O7JxOxek0BrgtRHxuKT9gB9L2ousvTxQvSVBmz6mkddfHpL0J8C0iPiepJ2B7esdV0t6oDsbmAo8RTbdYLVkXbXikuaS9VszoaG/A8zMrExRLfXUty4iZjR7kKQ5wHuAgyvrZEfEBtLqZBFxq6T7gT3IGmX57t/JwOr0+TFJEyNiTeq6XZvi/cCUGsdUVffPCEmnA5/jpYe6WwD/Ue+4QbwL+HVE/DYiXgQuBd5K1g9dyYw1Kx4R8yJiRkTM2IHeXnHAzGwkiNjc9FaEpFlk+eiIiHguF99ZaQkaSbuTDRR6IHXZrpc0M43WPZbsTRSAK4A56fOcAfFj0+jdmcDTlS7gWhppj78XOIL0yktErCZbo7Soh8kmeNg23djBwF3AMuCoVCZ/U2Zm1mMkLQJuAN4gqV/SccA3yPLPkgGvubwDWCnpl2RjbY6PiMpApROA7wJ9wP289Fz1LOAQSfcBh6TvAIuBB1L57wD/t15dG+kbfSEiQlKkm9uugWNqioibJP0IuI2sj/t2YB7wU+BCSWem2HmtXMfMzMoXBJsLtjAHPW/EMVXCVfNCRFwCXFJj33Jg7yrxx8kacgPjAZzYTF0bSaQXS/o2WdfrX5ONlPpOMxcZKCJOB04fEH4AOKCV85qZ2fALhj6RdpJGBht9VdIhwDPAG4B/jIglpdfMzMw6QDkt0k7S0LDXlDiXSJoAPF5ulczMrJP0eou05mCjNMrpWkmXSnpzmu/wDrIhwwOnbTIzsx4UAZtjc9NbNxmsRfoN4DRgR+DnwGERcaOkPwQWAT8bhvqZmdmIFoVfZ+kWgyXSMRFxDYCkM9Kcg0TEPZWl1MzMzDb3eNfuYIk0/5t5fsC+elMsmZlZDwi3SAdNpG+S9AzZvIPbpM+k71uXXjMzM+sI3fbMs1k1E2lEeP49MzOrq9dH7XrWdzMzK6ysmY06iROpmZm1JNjU7iq0lROpmZkVF26RNpxIJY3Nl8/NrG9mZj0qwKN26xWQ9DHgDLJXYCqvvQSwe4n1MjOzjhB+j7SBMp8B9oqIdWVXxszMOk+vt0gbWdj7fuC5uqXMzMx6UCMt0lOBX0i6CdhQCUbEJ0qrlZmZdQS//tJYIv022aT1q6DHO8LNzOwVPCFDfRsj4lOl18TMzDqSW6T1LZM0F/gJL+/a9esvZmY9L9wibaDMX6afp+Zifv3FzMx+v7B3Lxs0kUoaBXwoIv5nmOpjZmYdxcuoDfr6S2S/na8OU13MzKwDbS7wf92kka7dayS9D7g0Iryg9zAYPWGbdlehPNtPaHcNSrHLNo28kt2ZXr3DVu2uQilefP1O7a5CKZ7aanhXwPQUgY0l0k8B2wGbJD1PtrB3RMTYUmtmZmYdINjc422sun9GR8QOETEqIraIiLHpu5OomZlBGmzU7FaPpPmS1kq6IxfbSdISSfeln+NTXJLOkdQnaaWkfXPHzEnl75M0JxffT9KqdMw5kjTYNQbTUH+UpCMkfTVt72nkGDMz634BbI5oemvA+cCsAbFTgKURMQ1Ymr4DHAZMS9tc4FzIkiJwOvAW4ADg9FxiPDeVrRw3q841aqqbSCWdBXwSuCttn0wxMzMzNhNNb/VExPXAwPkKZgML0ucFwJG5+MLI3AiMkzQReDewJCKeiIgngSXArLRvbETckMb+LBxwrmrXqKmRZ6SHA9PTCF4kLQBup4EsbWZm3S2KPyOdIGl57vu8iJhX55hdI2INQESskbRLik8CHsmV60+xweL9VeKDXaOmRhf2HsdLfxns2OAxZmbWAwpOyLAuImYMURVUJRYF4oU0kkj/Bbhd0rJ08XcApxW9oJmZdZdhHLX7mKSJqaU4EVib4v3AlFy5ycDqFD9wQPzaFJ9cpfxg16ipkVG7i4CZwKVp++MUMzOzHpdNEVjKYKNqrgAqI2/nAJfn4sem0bszgadT9+zVwKGSxqdBRocCV6d96yXNTKN1jx1wrmrXqKlui1TS0og4OJ18YMzMzHpaOeuRSlpE1pqcIKmfbPTtWcDFko4DHgben4ovJhvP0wc8B3wEssVVJH0BuCWVOyO34MoJZCODtwGuShuDXKOmmolU0tbAtukmxvNSn/JY4DX1TmxmZlZURBxTY9crGnFp5O2JNc4zH5hfJb4c2LtK/PFq1xjMYC3SjwEnkyXNW3kpkT4DfLOZi5iZWXeqvEfay2om0oj4V+BfJX08Iv5tGOtkZmYdpJH3QrvZYF27+wOPVJKopGOB9wEPAZ/3wt5mZlbWM9JOMtio3W8DLwBIegfZA9iFwNNAvZdmzcysB5Q4RWDHGOwZ6ehcq/MDZLNOXAJcImlF+VUzM7MRL/yMdNBEKmlMRGwkG8E0t8HjzMysR3iw0eAJcRFwnaR1wPPAfwFI+gOy7l0zM+t5fkY62KjdL0paCkwErknv6UD2XPXjw1E5MzMb+dwiHURajmZg7FflVcfMzDqJu3b9rNPMzFq0GXftmpmZFRJd+DpLs5xIzcysJb2eSOsuo1aUpPmS1kq6Y0D845LulXSnpC/n4qdK6kv73l1WvczMbGh5QobynA98g2w2JAAkvROYDewTERsk7ZLiewJHA3uRTZL/n5L2iIhNJdbPzMxalA026u1npKW1SCPiemDgfLwnAGdFxIZUprLy+GzgwojYEBG/JltT7oCy6mZmZkOl+dZot7VIS0ukNewBvF3STZKuSxPjA0wCHsmV608xMzMbwSLctTvcg43GAOOBmcD+ZKuQ785La53mVf1NS5pLmq5wgsdKmZlZmw13JuoHLk2zJN0saTMwIcWn5MpNBlZXO0FEzCOtPrO7tu6uP2vMzDpQt7UwmzXcXbs/Bg4CkLQHsCWwDrgCOFrSVpKmAtOAm4e5bmZm1qQANkXzWzcprUUqaRFwIDBBUj9wOjAfmJ9eiXkBmJNap3dKuhi4C9gInOgRu2ZmnaHXW6SlJdKIOKbGrg/VKP9F4Itl1cfMzIZepUXayzxax8zMigvY7ERqZmZWTNYi7e1M6kRqZmYt6fUW6XCP2jUzsy5S1qhdSW+QtCK3PSPpZEmfl/RoLn547piqc7ZLmpVifZJOycWnpgmC7pN0kaQti/wOnEjNzKwlm6P5rZ6IuDcipkfEdGA/4DngsrT77Mq+iFgMr5izfRbwLUmjJY0GvgkcBuwJHJPKAnwpnWsa8CRwXJH7dyI1M7PCIoJNBbYmHQzcHxEPDVKm1pztBwB9EUcyRoUAAAyOSURBVPFARLwAXAjMliSyeQ1+lI5fABzZbMXAidTMzFq0eXPzW5OOBhblvp8kaWVarnN8itWas71W/FXAUxGxcUC8aU6kZmZWWAvPSCdIWp7b5lY7f3pueQTwwxQ6F3g9MB1YA3ytUrRG9ZqNN82jds3MrCUFR+2ui4gZDZQ7DLgtIh4DqPwEkPQd4Mr0dbA526vF1wHjJI1JrdKac7zX4xapmZkVFkHZz0iPIdetK2libt97gTvS51pztt8CTEsjdLck6ya+Ik1Puww4Kh0/B7i8wK/ALVIzM2tNWe+RStoWOAT4WC78ZUnTybphH6zsi4iac7ZLOgm4GhgNzI+IO9O5PgdcKOlM4HbgvCL1dCI1M7PCypxrNyKeIxsUlI99eJDyVedsT6/ILK4Sf4BsVG9L3LVrZmbWArdIzcysJV5GzczMrCAvo+ZEamZmrfAyak6kZmZWnFukTqRmZtaizT3eJHUiNTOzwtwidSI1M7NW+BmpE6mZmRXnFqkTqZmZtcjvkdrIs/VW7a5BeTY82+4alOK1Y0e3uwql2XPn7dpdhVI89rsX2l2FUowaVW11sPK4RepEamZmLWl6NZeu40RqZmaFRcCmze2uRXs5kZqZWWFZ165bpGZmZoVt6vH3X5xIzcyssAi3SJ1IzcysJb3+jNQLe5uZmbXALVIzMyvMg42cSM3MrAUR4cFG7a6AmZl1NrdIzczMCgo82MiJ1MzMCvMzUidSMzNrhd8j9esvZmZWXNa1G01vjZD0oKRVklZIWp5iO0laIum+9HN8ikvSOZL6JK2UtG/uPHNS+fskzcnF90vn70vHFlo6x4nUzMxasima35rwzoiYHhEz0vdTgKURMQ1Ymr4DHAZMS9tc4FzIEi9wOvAW4ADg9EryTWXm5o6bVeT+nUjNzKywyusvZbRIa5gNLEifFwBH5uILI3MjME7SRODdwJKIeCIingSWALPSvrERcUNEBLAwd66m+BmpmZkVVvJgowCukRTAtyNiHrBrRKwBiIg1knZJZScBj+SO7U+xweL9VeJNcyI1M7OWFGxhTqg890zmpUSZ97aIWJ2S5RJJ9wxyvmrPN6NAvGlOpGZmVljWIi106Lrcc8/q545YnX6ulXQZ2TPOxyRNTK3RicDaVLwfmJI7fDKwOsUPHBC/NsUnVynfND8jNTOz4qKcUbuStpO0Q+UzcChwB3AFUBl5Owe4PH2+Ajg2jd6dCTyduoCvBg6VND4NMjoUuDrtWy9pZhqte2zuXE1xi9TMzAor8RnprsBl6Y2UMcAPIuJnkm4BLpZ0HPAw8P5UfjFwONAHPAd8BCAinpD0BeCWVO6MiHgifT4BOB/YBrgqbU1zIjUzs8KCKCWRRsQDwJuqxB8HDq4SD+DEGueaD8yvEl8O7N1qXZ1IzcysuPBcu6U9I5U0RdIySXdLulPSJ1P8C2nWiRWSrpH0mhSvOSuFmZnZSFXmYKONwKcj4o3ATOBESXsCX4mIfSJiOnAl8I+pfNVZKczMbOSqPCNtdusmpXXtphFRlZdm10u6G5gUEXflim3HS+/t/H5WCuBGSeMqQ5zLqqOZmbXOC3sPA0m7AW8Gbkrfv0g21Php4J2pWK3ZJ5xIzcxGqPDqL+W/Ryppe+AS4OSIeAYgIv4uIqYAFwAnVYpWOfwV/9+RNFfScknL17OprGqbmVmDNm9ufusmpSZSSVuQJdELIuLSKkV+ALwvfa41K8XLRMS8iJgRETN2YPRQV9nMzJoQBLG5+a2blDlqV8B5wN0R8fVcfFqu2BFAZe7EWrNSmJnZSBX0fCIt8xnp24APA6skrUix04DjJL0B2Aw8BByf9lWdlcLMzEa2bkuMzSpz1O5/U/255+Ia5WvOSmFmZiNX9PhgI89sZGZmxYVbpE6kZmZWWGWwUS9zIjUzs+LcInUiNTOz1jiRmpmZtcCJ1MzMrCh37TqRmplZcR5s5ERqZmatcIvUidTMzFrT6xMylL76i5mZWTdzi9TMzFrirl0zM7Oi/IzUidTMzIrzqF0nUjMza1GvJ1IPNjIzs+JKWthb0hRJyyTdLelOSZ9M8c9LelTSirQdnjvmVEl9ku6V9O5cfFaK9Uk6JRefKukmSfdJukjSlkV+BU6kZmbWkjISKbAR+HREvBGYCZwoac+07+yImJ62xQBp39HAXsAs4FuSRksaDXwTOAzYEzgmd54vpXNNA54Ejity/06kZmZWXEkt0ohYExG3pc/rgbuBSYMcMhu4MCI2RMSvgT7ggLT1RcQDEfECcCEwW5KAg4AfpeMXAEcW+RU4kZqZWWFBENH81gxJuwFvBm5KoZMkrZQ0X9L4FJsEPJI7rD/FasVfBTwVERsHxJvmRGpmZi0p2CKdIGl5bptb7dyStgcuAU6OiGeAc4HXA9OBNcDXKkWrVa1AvGketWtmZsUVf490XUTMGKyApC3IkugFEXEpQEQ8ltv/HeDK9LUfmJI7fDKwOn2uFl8HjJM0JrVK8+Wb4hapmZm1pKRRuwLOA+6OiK/n4hNzxd4L3JE+XwEcLWkrSVOBacDNwC3AtDRCd0uyAUlXRNa/vAw4Kh0/B7i8yP27RWpmZi0p6T3StwEfBlZJWpFip5GNup1O1g37IPAxgIi4U9LFwF1kI35PjIhNAJJOAq4GRgPzI+LOdL7PARdKOhO4nSxxN02dPGu/pN8CDw3T5SaQdQV0o269N99X5+nWexvO+3pdROw8TNdC0s/I7q9Z6yJi1lDXpx06OpEOJ0nL6/Xnd6puvTffV+fp1nvr1vuyjJ+RmpmZtcCJ1MzMrAVOpI2b1+4KlKhb78331Xm69d669b4MPyM1MzNriVukZmZmLXAirSPN5bhW0h31S3eOWksUdQNJW0u6WdIv0739U7vrNBQkvSG3dNQKSc9IOrnd9Sqq2r8tSTtJWpKWtVqSm0e1Ywz23wxJn5EUkoq8LmIjlBNpfeeTLcnTbQZboqjTbQAOiog3kc3HOUvSzDbXqWURcW9l6ShgP+A54LI2V6sV5/PKf1unAEvTslZL0/dOcz5V/pshaQpwCPDwcFfIyuVEWkdEXA880e56DLUCSxR1jMg8m75ukbZuGwxwMHB/RAzXhCRDrsa/rdlky1lBC8tatdMg/804G/gs3fe/xZ7nRGrVlijqeGlB3xXAWmBJRHTNvSVHA4vaXYkS7BoRayD7Yw/Ypc31GRKSjgAejYhftrsuNvScSHtclSWKukJEbEpdoJOBAyTt3e46DZU08fYRwA/bXRerT9K2wN8B/9juulg5nEh7WLUlirpNRDwFXEt3Pec+DLgtv5xUF3mssrpH+rm2zfUZCq8HpgK/lPQg2R93t0l6dVtrZUPGibRH1VqiqBtI2lnSuPR5G+BdwD3trdWQOobu7NaFbCmsOelz4WWtRpKIWBURu0TEbhGxG9m6mftGxG/aXDUbIk6kdUhaBNwAvEFSv6Tj2l2nIVJZouig3OsUh7e7UkNkIrBM0kqytQiXRMSVdY7pCKmb8BCg43sQavzbOgs4RNJ9ZPd5VjvrWEQX/zfDavDMRmZmZi1wi9TMzKwFTqRmZmYtcCI1MzNrgROpmZlZC5xIzczMWuBEal1P0rP1SzV9zmslzRgQuyy9RtQn6enca0VvlfTdLloUwMxyxrS7AmbdIiLeCyDpQOAzEfGe3O5ftKVSZlY6t0itJ6XZjy6RdEva3pbiB0j6haTb0883pPg2ki6UtFLSRcA2TV7v9y1YSc9K+pKkWyX9Z7rmtZIeSJObVybd/0qq20pJHxviX4GZDREnUutV/wqcHRH7A+8Dvpvi9wDviIg3k00y/s8pfgLwXETsA3yRbD3QorYDro2I/YD1wJlks/i8FzgjlTkOeDrVb3/gryVNbeGaZlYSd+1ar3oXsGc25TAAYyXtAOwILJA0jWzdyC3S/ncA5wBExMo0/WBRLwA/S59XARsi4kVJq4DdUvxQYB9JR6XvOwLTgF+3cF0zK4ETqfWqUcAfR8Tz+aCkfwOWRcR70zqt1+Z2D9V8mi/GS3NzbgY2AETEZkmVf5MCPh4RVw/RNc2sJO7atV51DXBS5Yuk6enjjsCj6fNf5cpfD3wwld0b2Kfk+l0NnJCWukPSHpK2K/maZlaAE6n1gm3TKhyV7VPAJ4AZaSDPXcDxqeyXgX+R9D/A6Nw5zgW2T126nwVuLrnO3wXuIlu38g7g27gHyWxE8uovZmZmLXCL1MzMrAVOpGZmZi1wIjUzM2uBE6mZmVkLnEjNzMxa4ERqZmbWAidSMzOzFjiRmpmZteD/A/csUzwVPBZeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap for  objective values\n",
    "plt.rcParams['figure.figsize'] = [10, 5]\n",
    "obj_mat = pd.to_numeric(df_result1997['total objective'].values).reshape(6,5)\n",
    "fig, ax = plt.subplots()\n",
    "#heatmap = ax.imshow((np.sign(obj_max)*np.log(abs(obj_max))).T, cmap = plt.cm.get_cmap('Spectral'))\n",
    "heatmap = ax.imshow(obj_mat.T, cmap = plt.cm.get_cmap('RdYlGn_r'))\n",
    "ax.set_xticklabels([0, 1, 2, 3, 7, 10, 14])\n",
    "ax.set_yticklabels([0, 16, 40, 80, 160, 230])\n",
    "cbar = fig.colorbar(heatmap) \n",
    "cbar.ax.set_title('total objective', size=12)\n",
    "plt.xlabel('Lead Time')\n",
    "plt.ylabel('Storm Benchmark')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap for emergency days \n",
    "plt.rcParams['figure.figsize'] = [10, 5]\n",
    "df_result1997['emergency days'] = pd.to_numeric(df_result1997['emergency days'].values)\n",
    "em_mat = df_result1997.pivot(\"h\", \"umax\", \"emergency days\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "heatmap = ax.imshow(em_mat.T, cmap = plt.cm.get_cmap('Blues'))\n",
    "ax.set_xticklabels([0, 1, 2, 3, 7, 10, 14])\n",
    "ax.set_yticklabels([0, 16, 40, 80, 160, 230])\n",
    "cbar = fig.colorbar(heatmap) \n",
    "cbar.ax.set_title('emergency days', size=12)\n",
    "plt.xlabel('Lead Time')\n",
    "plt.ylabel('Storm Benchmark')\n",
    "ax.set_title('Days of over 230 TAF release in 1997 ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap for flood days\n",
    "plt.rcParams['figure.figsize'] = [15, 5]\n",
    "\n",
    "df_result1997['flood days'] = pd.to_numeric(df_result1997['flood days'].values)\n",
    "df_result2006['flood days'] = pd.to_numeric(df_result2006['flood days'].values)\n",
    "df_result2017['flood days'] = pd.to_numeric(df_result2017['flood days'].values)\n",
    "\n",
    "obj_mat1 = df_result1997.pivot(\"h\", \"umax\", \"flood days\")\n",
    "obj_mat2 = df_result2006.pivot(\"h\", \"umax\", \"flood days\")\n",
    "obj_mat3 = df_result2017.pivot(\"h\", \"umax\", \"flood days\")\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3)\n",
    "\n",
    "# f,(ax1,ax2,ax3) = plt.subplots(1,3,sharey=True)\n",
    "g1 = sns.heatmap(obj_mat1,cmap=\"Blues\",ax=ax1)\n",
    "g1.set_ylabel('Lead Time')\n",
    "g1.set_xlabel('Storm Benchmark')\n",
    "g1.set_ylim(6.5, -0.5)\n",
    "g1.set_title('Days of over 80 TAF release in 1997 ')\n",
    "\n",
    "g2 = sns.heatmap(obj_mat2,cmap=\"Blues\",ax=ax2)\n",
    "g2.set_ylabel('Lead Time')\n",
    "g2.set_xlabel('Storm Benchmark')\n",
    "g2.set_ylim(6.5, -0.5)\n",
    "g2.set_title('Days of over 80 TAF release in 2006')\n",
    "\n",
    "g3 = sns.heatmap(obj_mat3,cmap=\"Blues\",ax=ax3)\n",
    "g3.set_ylabel('Lead Time')\n",
    "g3.set_xlabel('Storm Benchmark')\n",
    "g3.set_ylim(6.5, -0.5)\n",
    "g3.set_title('Days of over 80 TAF release in 2017')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap for storm days\n",
    "plt.rcParams['figure.figsize'] = [15, 5]\n",
    "\n",
    "df_result1997['storm days'] = pd.to_numeric(df_result1997['storm days'].values)\n",
    "df_result2006['storm days'] = pd.to_numeric(df_result2006['storm days'].values)\n",
    "df_result2017['storm days'] = pd.to_numeric(df_result2017['storm days'].values)\n",
    "\n",
    "obj_mat1 = df_result1997.pivot(\"h\", \"umax\", \"storm days\")\n",
    "obj_mat2 = df_result2006.pivot(\"h\", \"umax\", \"storm days\")\n",
    "obj_mat3 = df_result2017.pivot(\"h\", \"umax\", \"storm days\")\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3)\n",
    "\n",
    "# f,(ax1,ax2,ax3) = plt.subplots(1,3,sharey=True)\n",
    "g1 = sns.heatmap(obj_mat1,cmap=\"Blues\",ax=ax1)\n",
    "g1.set_ylabel('Lead Time')\n",
    "g1.set_xlabel('Storm Benchmark')\n",
    "g1.set_ylim(6.5, -0.5)\n",
    "g1.set_title('Days of over 40 TAF release in 1997 ')\n",
    "\n",
    "g2 = sns.heatmap(obj_mat2,cmap=\"Blues\",ax=ax2)\n",
    "g2.set_ylabel('Lead Time')\n",
    "g2.set_xlabel('Storm Benchmark')\n",
    "g2.set_ylim(6.5, -0.5)\n",
    "g2.set_title('Days of over 40 TAF release in 2006')\n",
    "\n",
    "g3 = sns.heatmap(obj_mat3,cmap=\"Blues\",ax=ax3)\n",
    "g3.set_ylabel('Lead Time')\n",
    "g3.set_xlabel('Storm Benchmark')\n",
    "g3.set_ylim(6.5, -0.5)\n",
    "g3.set_title('Days of over 40 TAF release in 2017')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare data for parallel axis plot \n",
    "df1997 = df_result1997.copy()\n",
    "df1997.rename(columns={'total objective':'total objective 1997',\n",
    "                       'storm days':'storm days 1997',\n",
    "                       'flood days':'flood days 1997',\n",
    "                       'emergency days':'emergency days 1997'}, \n",
    "              inplace=True)\n",
    "\n",
    "\n",
    "df2006 = df_result2006[['total objective', 'storm days', 'flood days', 'emergency days']]\n",
    "df2006.index = df2006.index-30\n",
    "df2006.rename(columns={'total objective':'total objective 2006',\n",
    "                       'storm days':'storm days 2006',\n",
    "                       'flood days':'flood days 2006',\n",
    "                       'emergency days':'emergency days 2006'},\n",
    "              inplace=True)\n",
    "\n",
    "df2017 = df_result2017[['total objective', 'storm days', 'flood days', 'emergency days']]\n",
    "df2017.index = df2017.index-60\n",
    "\n",
    "df2017.rename(columns={'total objective':'total objective 2017',\n",
    "                       'storm days':'storm days 2017',\n",
    "                       'flood days':'flood days 2017',\n",
    "                       'emergency days':'emergency days 2017'},\n",
    "              inplace=True)\n",
    "\n",
    "df_result = pd.concat([df1997, df2006], axis=1)\n",
    "df_result = pd.concat([df_result, df2017], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "dimensions": [
          {
           "constraintrange": [
            0,
            0.5
           ],
           "label": "emergency days 1997",
           "range": [
            0,
            3
           ],
           "values": [
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0
           ]
          },
          {
           "constraintrange": [
            0,
            100000
           ],
           "label": "total objective 1997",
           "range": [
            0,
            250000
           ],
           "values": [
            226513.29126193887,
            226490.40365799764,
            226490.53842358568,
            226490.29932159703,
            226490.2983059303,
            175120.43472952847,
            175015.0548646601,
            171388.02916199405,
            164373.4311561016,
            161666.0313181097,
            140900.9433423737,
            139708.91850348903,
            134979.69313887207,
            127432.72406956859,
            132139.96509555238,
            71649.24226131177,
            67620.18842440698,
            63291.67380858531,
            70188.69309154003,
            80344.52864752694,
            45921.37830803699,
            42265.09351318569,
            41652.62750575769,
            48965.32891046028,
            60277.50210491486,
            26040.72889661936,
            23688.811163926985,
            22256.256695195123,
            37529.77645443864,
            42711.34737426024
           ]
          },
          {
           "constraintrange": [
            0,
            10
           ],
           "label": "flood days 1997",
           "range": [
            0,
            17
           ],
           "values": [
            8,
            8,
            8,
            8,
            8,
            8,
            8,
            5,
            9,
            7,
            6,
            6,
            6,
            8,
            9,
            9,
            9,
            9,
            12,
            10,
            12,
            12,
            12,
            11,
            10,
            14,
            0,
            0,
            7,
            5
           ]
          },
          {
           "constraintrange": [
            -1000,
            5000
           ],
           "label": "total objective 2006",
           "range": [
            -1000,
            20000
           ],
           "values": [
            9566.603758143565,
            9566.60197227772,
            9566.603910396349,
            9566.597284964904,
            9566.599698087062,
            4546.453565151713,
            4243.437899999808,
            4831.987403371004,
            7816.2713209251315,
            7795.1112744073525,
            2238.319945554536,
            1710.5304668816452,
            2796.687559255788,
            5129.545193277093,
            7350.239780365622,
            -624.3778896236988,
            -872.2310059364777,
            -519.2239359673167,
            2164.5476341209232,
            1247.2818194347183,
            -891.3900395940279,
            -891.3900396600241,
            -719.7117167173662,
            755.4645923355902,
            -114.91459377204637,
            -891.3900405967105,
            -891.3900406483777,
            -432.84171162940635,
            753.4242323594659,
            -737.8390742077478
           ]
          },
          {
           "constraintrange": [
            0,
            3
           ],
           "label": "flood days 2006",
           "range": [
            0,
            5
           ],
           "values": [
            1,
            1,
            1,
            1,
            1,
            0,
            0,
            0,
            3,
            3,
            0,
            0,
            0,
            0,
            1,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0
           ]
          },
          {
           "constraintrange": [
            0,
            70000
           ],
           "label": "total objective 2017",
           "range": [
            0,
            250000
           ],
           "values": [
            153317.19652030218,
            153317.1511908709,
            153317.1689907165,
            153317.14870944552,
            153317.1447536547,
            121928.03994726548,
            121241.60704635215,
            118807.62404424344,
            122986.17419408502,
            134720.68941495154,
            97876.42033743425,
            96423.29709950981,
            94262.22823409627,
            97779.82625124003,
            114031.68967382215,
            45003.40073604211,
            41736.3140111516,
            41907.45136224403,
            58864.48422151312,
            60325.64760548469,
            28500.642019198014,
            25361.307515153232,
            27104.844531104995,
            44236.00972732208,
            42485.38233209479,
            18244.915600465494,
            16352.702982363788,
            18366.781644995488,
            43272.47162061038,
            38024.38048647163
           ]
          },
          {
           "constraintrange": [
            0,
            10
           ],
           "label": "flood days 2017",
           "range": [
            0,
            17
           ],
           "values": [
            10,
            10,
            10,
            10,
            10,
            12,
            12,
            12,
            13,
            12,
            15,
            14,
            10,
            16,
            11,
            9,
            9,
            8,
            13,
            10,
            11,
            11,
            10,
            12,
            9,
            0,
            0,
            0,
            9,
            6
           ]
          }
         ],
         "line": {
          "color": [
           1,
           1,
           1,
           1,
           1,
           2,
           2,
           2,
           2,
           2,
           3,
           3,
           3,
           3,
           3,
           7,
           7,
           7,
           7,
           7,
           10,
           10,
           10,
           10,
           10,
           14,
           14,
           14,
           14,
           14
          ],
          "colorscale": [
           [
            0,
            "#440154"
           ],
           [
            0.1111111111111111,
            "#482878"
           ],
           [
            0.2222222222222222,
            "#3e4989"
           ],
           [
            0.3333333333333333,
            "#31688e"
           ],
           [
            0.4444444444444444,
            "#26828e"
           ],
           [
            0.5555555555555556,
            "#1f9e89"
           ],
           [
            0.6666666666666666,
            "#35b779"
           ],
           [
            0.7777777777777778,
            "#6ece58"
           ],
           [
            0.8888888888888888,
            "#b5de2b"
           ],
           [
            1,
            "#fde725"
           ]
          ],
          "showscale": true
         },
         "type": "parcoords"
        }
       ],
       "layout": {
        "paper_bgcolor": "white",
        "plot_bgcolor": "white",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      },
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"935c703f-2aa4-40f3-bf77-9fc96bf1ff54\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"935c703f-2aa4-40f3-bf77-9fc96bf1ff54\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        '935c703f-2aa4-40f3-bf77-9fc96bf1ff54',\n",
       "                        [{\"dimensions\": [{\"constraintrange\": [0, 0.5], \"label\": \"emergency days 1997\", \"range\": [0, 3], \"values\": [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {\"constraintrange\": [0, 100000], \"label\": \"total objective 1997\", \"range\": [0, 250000], \"values\": [226513.29126193887, 226490.40365799764, 226490.53842358568, 226490.29932159703, 226490.2983059303, 175120.43472952847, 175015.0548646601, 171388.02916199405, 164373.4311561016, 161666.0313181097, 140900.9433423737, 139708.91850348903, 134979.69313887207, 127432.72406956859, 132139.96509555238, 71649.24226131177, 67620.18842440698, 63291.67380858531, 70188.69309154003, 80344.52864752694, 45921.37830803699, 42265.09351318569, 41652.62750575769, 48965.32891046028, 60277.50210491486, 26040.72889661936, 23688.811163926985, 22256.256695195123, 37529.77645443864, 42711.34737426024]}, {\"constraintrange\": [0, 10], \"label\": \"flood days 1997\", \"range\": [0, 17], \"values\": [8, 8, 8, 8, 8, 8, 8, 5, 9, 7, 6, 6, 6, 8, 9, 9, 9, 9, 12, 10, 12, 12, 12, 11, 10, 14, 0, 0, 7, 5]}, {\"constraintrange\": [-1000, 5000], \"label\": \"total objective 2006\", \"range\": [-1000, 20000], \"values\": [9566.603758143565, 9566.60197227772, 9566.603910396349, 9566.597284964904, 9566.599698087062, 4546.453565151713, 4243.437899999808, 4831.987403371004, 7816.2713209251315, 7795.1112744073525, 2238.319945554536, 1710.5304668816452, 2796.687559255788, 5129.545193277093, 7350.239780365622, -624.3778896236988, -872.2310059364777, -519.2239359673167, 2164.5476341209232, 1247.2818194347183, -891.3900395940279, -891.3900396600241, -719.7117167173662, 755.4645923355902, -114.91459377204637, -891.3900405967105, -891.3900406483777, -432.84171162940635, 753.4242323594659, -737.8390742077478]}, {\"constraintrange\": [0, 3], \"label\": \"flood days 2006\", \"range\": [0, 5], \"values\": [1, 1, 1, 1, 1, 0, 0, 0, 3, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {\"constraintrange\": [0, 70000], \"label\": \"total objective 2017\", \"range\": [0, 250000], \"values\": [153317.19652030218, 153317.1511908709, 153317.1689907165, 153317.14870944552, 153317.1447536547, 121928.03994726548, 121241.60704635215, 118807.62404424344, 122986.17419408502, 134720.68941495154, 97876.42033743425, 96423.29709950981, 94262.22823409627, 97779.82625124003, 114031.68967382215, 45003.40073604211, 41736.3140111516, 41907.45136224403, 58864.48422151312, 60325.64760548469, 28500.642019198014, 25361.307515153232, 27104.844531104995, 44236.00972732208, 42485.38233209479, 18244.915600465494, 16352.702982363788, 18366.781644995488, 43272.47162061038, 38024.38048647163]}, {\"constraintrange\": [0, 10], \"label\": \"flood days 2017\", \"range\": [0, 17], \"values\": [10, 10, 10, 10, 10, 12, 12, 12, 13, 12, 15, 14, 10, 16, 11, 9, 9, 8, 13, 10, 11, 11, 10, 12, 9, 0, 0, 0, 9, 6]}], \"line\": {\"color\": [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 7, 7, 7, 7, 7, 10, 10, 10, 10, 10, 14, 14, 14, 14, 14], \"colorscale\": [[0.0, \"#440154\"], [0.1111111111111111, \"#482878\"], [0.2222222222222222, \"#3e4989\"], [0.3333333333333333, \"#31688e\"], [0.4444444444444444, \"#26828e\"], [0.5555555555555556, \"#1f9e89\"], [0.6666666666666666, \"#35b779\"], [0.7777777777777778, \"#6ece58\"], [0.8888888888888888, \"#b5de2b\"], [1.0, \"#fde725\"]], \"showscale\": true}, \"type\": \"parcoords\"}],\n",
       "                        {\"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"white\", \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('935c703f-2aa4-40f3-bf77-9fc96bf1ff54');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parallel axis plot for lead time\n",
    "fig = go.Figure(data=\n",
    "    go.Parcoords(\n",
    "        line = dict(color = df_result['h'],\n",
    "                    colorscale = \"Viridis\",\n",
    "                    showscale = True),\n",
    "        dimensions = list([\n",
    "            dict(range = [0,3], constraintrange = [0,0.5], label = 'emergency days 1997', values = df_result['emergency days 1997']),\n",
    "            dict(range = [0,250000], constraintrange = [0,100000], label = 'total objective 1997', values = df_result['total objective 1997']),\n",
    "            dict(range = [0,17], constraintrange = [0,10], label = 'flood days 1997', values = df_result['flood days 1997']),\n",
    "            dict(range = [-1000,20000], constraintrange = [-1000,5000], label = 'total objective 2006', values = df_result['total objective 2006']),\n",
    "            dict(range = [0,5], constraintrange = [0,3], label = 'flood days 2006', values = df_result['flood days 2006']),\n",
    "            dict(range = [0,250000], constraintrange = [0,70000], label = 'total objective 2017', values = df_result['total objective 2017']),\n",
    "            dict(range = [0,17], constraintrange = [0,10], label = 'flood days 2017', values = df_result['flood days 2017'])\n",
    "                 ])            \n",
    "        )\n",
    "     )\n",
    "\n",
    "fig.update_layout(plot_bgcolor = 'white', paper_bgcolor = 'white')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "dimensions": [
          {
           "constraintrange": [
            0,
            0.5
           ],
           "label": "emergency days 1997",
           "range": [
            0,
            3
           ],
           "values": [
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            2,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0
           ]
          },
          {
           "constraintrange": [
            0,
            100000
           ],
           "label": "total objective 1997",
           "range": [
            0,
            250000
           ],
           "values": [
            226513.29126193887,
            226490.40365799764,
            226490.53842358568,
            226490.29932159703,
            226490.2983059303,
            175120.43472952847,
            175015.0548646601,
            171388.02916199405,
            164373.4311561016,
            161666.0313181097,
            140900.9433423737,
            139708.91850348903,
            134979.69313887207,
            127432.72406956859,
            132139.96509555238,
            71649.24226131177,
            67620.18842440698,
            63291.67380858531,
            70188.69309154003,
            80344.52864752694,
            45921.37830803699,
            42265.09351318569,
            41652.62750575769,
            48965.32891046028,
            60277.50210491486,
            26040.72889661936,
            23688.811163926985,
            22256.256695195123,
            37529.77645443864,
            42711.34737426024
           ]
          },
          {
           "constraintrange": [
            0,
            10
           ],
           "label": "flood days 1997",
           "range": [
            0,
            17
           ],
           "values": [
            8,
            8,
            8,
            8,
            8,
            8,
            8,
            5,
            9,
            7,
            6,
            6,
            6,
            8,
            9,
            9,
            9,
            9,
            12,
            10,
            12,
            12,
            12,
            11,
            10,
            14,
            0,
            0,
            7,
            5
           ]
          },
          {
           "constraintrange": [
            -1000,
            5000
           ],
           "label": "total objective 2006",
           "range": [
            -1000,
            20000
           ],
           "values": [
            9566.603758143565,
            9566.60197227772,
            9566.603910396349,
            9566.597284964904,
            9566.599698087062,
            4546.453565151713,
            4243.437899999808,
            4831.987403371004,
            7816.2713209251315,
            7795.1112744073525,
            2238.319945554536,
            1710.5304668816452,
            2796.687559255788,
            5129.545193277093,
            7350.239780365622,
            -624.3778896236988,
            -872.2310059364777,
            -519.2239359673167,
            2164.5476341209232,
            1247.2818194347183,
            -891.3900395940279,
            -891.3900396600241,
            -719.7117167173662,
            755.4645923355902,
            -114.91459377204637,
            -891.3900405967105,
            -891.3900406483777,
            -432.84171162940635,
            753.4242323594659,
            -737.8390742077478
           ]
          },
          {
           "constraintrange": [
            0,
            3
           ],
           "label": "flood days 2006",
           "range": [
            0,
            5
           ],
           "values": [
            1,
            1,
            1,
            1,
            1,
            0,
            0,
            0,
            3,
            3,
            0,
            0,
            0,
            0,
            1,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0
           ]
          },
          {
           "constraintrange": [
            0,
            70000
           ],
           "label": "total objective 2017",
           "range": [
            0,
            250000
           ],
           "values": [
            153317.19652030218,
            153317.1511908709,
            153317.1689907165,
            153317.14870944552,
            153317.1447536547,
            121928.03994726548,
            121241.60704635215,
            118807.62404424344,
            122986.17419408502,
            134720.68941495154,
            97876.42033743425,
            96423.29709950981,
            94262.22823409627,
            97779.82625124003,
            114031.68967382215,
            45003.40073604211,
            41736.3140111516,
            41907.45136224403,
            58864.48422151312,
            60325.64760548469,
            28500.642019198014,
            25361.307515153232,
            27104.844531104995,
            44236.00972732208,
            42485.38233209479,
            18244.915600465494,
            16352.702982363788,
            18366.781644995488,
            43272.47162061038,
            38024.38048647163
           ]
          },
          {
           "constraintrange": [
            0,
            10
           ],
           "label": "flood days 2017",
           "range": [
            0,
            17
           ],
           "values": [
            10,
            10,
            10,
            10,
            10,
            12,
            12,
            12,
            13,
            12,
            15,
            14,
            10,
            16,
            11,
            9,
            9,
            8,
            13,
            10,
            11,
            11,
            10,
            12,
            9,
            0,
            0,
            0,
            9,
            6
           ]
          }
         ],
         "line": {
          "color": [
           16,
           40,
           80,
           160,
           230,
           16,
           40,
           80,
           160,
           230,
           16,
           40,
           80,
           160,
           230,
           16,
           40,
           80,
           160,
           230,
           16,
           40,
           80,
           160,
           230,
           16,
           40,
           80,
           160,
           230
          ],
          "colorscale": [
           [
            0,
            "#440154"
           ],
           [
            0.1111111111111111,
            "#482878"
           ],
           [
            0.2222222222222222,
            "#3e4989"
           ],
           [
            0.3333333333333333,
            "#31688e"
           ],
           [
            0.4444444444444444,
            "#26828e"
           ],
           [
            0.5555555555555556,
            "#1f9e89"
           ],
           [
            0.6666666666666666,
            "#35b779"
           ],
           [
            0.7777777777777778,
            "#6ece58"
           ],
           [
            0.8888888888888888,
            "#b5de2b"
           ],
           [
            1,
            "#fde725"
           ]
          ],
          "showscale": true
         },
         "type": "parcoords"
        }
       ],
       "layout": {
        "paper_bgcolor": "white",
        "plot_bgcolor": "white",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      },
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"fd2423e8-77a1-458e-ad67-eed8c11128f9\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"fd2423e8-77a1-458e-ad67-eed8c11128f9\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        'fd2423e8-77a1-458e-ad67-eed8c11128f9',\n",
       "                        [{\"dimensions\": [{\"constraintrange\": [0, 0.5], \"label\": \"emergency days 1997\", \"range\": [0, 3], \"values\": [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {\"constraintrange\": [0, 100000], \"label\": \"total objective 1997\", \"range\": [0, 250000], \"values\": [226513.29126193887, 226490.40365799764, 226490.53842358568, 226490.29932159703, 226490.2983059303, 175120.43472952847, 175015.0548646601, 171388.02916199405, 164373.4311561016, 161666.0313181097, 140900.9433423737, 139708.91850348903, 134979.69313887207, 127432.72406956859, 132139.96509555238, 71649.24226131177, 67620.18842440698, 63291.67380858531, 70188.69309154003, 80344.52864752694, 45921.37830803699, 42265.09351318569, 41652.62750575769, 48965.32891046028, 60277.50210491486, 26040.72889661936, 23688.811163926985, 22256.256695195123, 37529.77645443864, 42711.34737426024]}, {\"constraintrange\": [0, 10], \"label\": \"flood days 1997\", \"range\": [0, 17], \"values\": [8, 8, 8, 8, 8, 8, 8, 5, 9, 7, 6, 6, 6, 8, 9, 9, 9, 9, 12, 10, 12, 12, 12, 11, 10, 14, 0, 0, 7, 5]}, {\"constraintrange\": [-1000, 5000], \"label\": \"total objective 2006\", \"range\": [-1000, 20000], \"values\": [9566.603758143565, 9566.60197227772, 9566.603910396349, 9566.597284964904, 9566.599698087062, 4546.453565151713, 4243.437899999808, 4831.987403371004, 7816.2713209251315, 7795.1112744073525, 2238.319945554536, 1710.5304668816452, 2796.687559255788, 5129.545193277093, 7350.239780365622, -624.3778896236988, -872.2310059364777, -519.2239359673167, 2164.5476341209232, 1247.2818194347183, -891.3900395940279, -891.3900396600241, -719.7117167173662, 755.4645923355902, -114.91459377204637, -891.3900405967105, -891.3900406483777, -432.84171162940635, 753.4242323594659, -737.8390742077478]}, {\"constraintrange\": [0, 3], \"label\": \"flood days 2006\", \"range\": [0, 5], \"values\": [1, 1, 1, 1, 1, 0, 0, 0, 3, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {\"constraintrange\": [0, 70000], \"label\": \"total objective 2017\", \"range\": [0, 250000], \"values\": [153317.19652030218, 153317.1511908709, 153317.1689907165, 153317.14870944552, 153317.1447536547, 121928.03994726548, 121241.60704635215, 118807.62404424344, 122986.17419408502, 134720.68941495154, 97876.42033743425, 96423.29709950981, 94262.22823409627, 97779.82625124003, 114031.68967382215, 45003.40073604211, 41736.3140111516, 41907.45136224403, 58864.48422151312, 60325.64760548469, 28500.642019198014, 25361.307515153232, 27104.844531104995, 44236.00972732208, 42485.38233209479, 18244.915600465494, 16352.702982363788, 18366.781644995488, 43272.47162061038, 38024.38048647163]}, {\"constraintrange\": [0, 10], \"label\": \"flood days 2017\", \"range\": [0, 17], \"values\": [10, 10, 10, 10, 10, 12, 12, 12, 13, 12, 15, 14, 10, 16, 11, 9, 9, 8, 13, 10, 11, 11, 10, 12, 9, 0, 0, 0, 9, 6]}], \"line\": {\"color\": [16, 40, 80, 160, 230, 16, 40, 80, 160, 230, 16, 40, 80, 160, 230, 16, 40, 80, 160, 230, 16, 40, 80, 160, 230, 16, 40, 80, 160, 230], \"colorscale\": [[0.0, \"#440154\"], [0.1111111111111111, \"#482878\"], [0.2222222222222222, \"#3e4989\"], [0.3333333333333333, \"#31688e\"], [0.4444444444444444, \"#26828e\"], [0.5555555555555556, \"#1f9e89\"], [0.6666666666666666, \"#35b779\"], [0.7777777777777778, \"#6ece58\"], [0.8888888888888888, \"#b5de2b\"], [1.0, \"#fde725\"]], \"showscale\": true}, \"type\": \"parcoords\"}],\n",
       "                        {\"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"white\", \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('fd2423e8-77a1-458e-ad67-eed8c11128f9');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parallel axis plot for storm benchmark\n",
    "fig = go.Figure(data=\n",
    "    go.Parcoords(\n",
    "        line = dict(color = df_result['umax'],\n",
    "                    colorscale = \"Viridis\",\n",
    "                    showscale = True),\n",
    "        dimensions = list([\n",
    "            dict(range = [0,3], constraintrange = [0,0.5], label = 'emergency days 1997', values = df_result['emergency days 1997']),\n",
    "            dict(range = [0,250000], constraintrange = [0,100000], label = 'total objective 1997', values = df_result['total objective 1997']),\n",
    "            dict(range = [0,17], constraintrange = [0,10], label = 'flood days 1997', values = df_result['flood days 1997']),\n",
    "            dict(range = [-1000,20000], constraintrange = [-1000,5000], label = 'total objective 2006', values = df_result['total objective 2006']),\n",
    "            dict(range = [0,5], constraintrange = [0,3], label = 'flood days 2006', values = df_result['flood days 2006']),\n",
    "            dict(range = [0,250000], constraintrange = [0,70000], label = 'total objective 2017', values = df_result['total objective 2017']),\n",
    "            dict(range = [0,17], constraintrange = [0,10], label = 'flood days 2017', values = df_result['flood days 2017'])\n",
    "                 ])                  \n",
    "        )\n",
    "     )\n",
    "\n",
    "fig.update_layout(plot_bgcolor = 'white', paper_bgcolor = 'white')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Forecast Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # test for a particular range of the forecast model\n",
    "\n",
    "# y1 = 1997 #year\n",
    "# y2 = 2010\n",
    "# h = 14 # foresight horizon (days)\n",
    "# umax = 40 # benchmark (TAF/D) \n",
    "# deviation = 1 #[0.2, 0.4, 0.6, 0.8, 1]\n",
    "# collective_obj, last_S, df_Q = folsom_forecast(y1, h, umax, deviation, y2, solver='ECOS')\n",
    "# # collective_obj, last_S, df = folsom_limit(y, h, umax, detail = 'y', solver='ECOS')\n",
    "# visualize(df_Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # test for a particular case of the forecast model\n",
    "\n",
    "# y = 1997 #year\n",
    "# h = 14 # foresight horizon (days)\n",
    "# umax = 40 # benchmark (TAF/D) \n",
    "# deviation = 1 #[0.2, 0.4, 0.6, 0.8, 1]\n",
    "# collective_obj, last_S, df_Q = folsom_forecast(y, h, umax, deviation, solver='ECOS')\n",
    "# # collective_obj, last_S, df = folsom_limit(y, h, umax, detail = 'y', solver='ECOS')\n",
    "# df_Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success\n"
     ]
    }
   ],
   "source": [
    "# running forecast model\n",
    "np.random.seed(7)\n",
    "y = 1997 #year\n",
    "h = 3 # foresight horizon (days)\n",
    "umax = 40 # benchmark (TAF/D) \n",
    "deviation = [0.2, 0.6, 1]#[0.2, 0.4, 0.6, 0.8, 1]\n",
    "rep = 10\n",
    "\n",
    "# initiate the result dataframe\n",
    "forecast_result = pd.DataFrame(columns=['Q','deviation', 'total objective','total spill','total shortage', \n",
    "                                  'storm days', 'flood days', 'emergency days', 'shortage days', 'last storage'])  \n",
    "# initiate a list to save the result, better way might be to use dictionary...\n",
    "save = [0]*len(deviation)*rep\n",
    "\n",
    "# loop for optimization results\n",
    "i=0\n",
    "for d in deviation:\n",
    "    for r in range(rep):\n",
    "        collective_obj, last_S, df = folsom_forecast(df_folsom, y, h, umax, d, solver='ECOS')\n",
    "        save[i] = df.copy()\n",
    "        i+=1\n",
    "        # collective_obj, last_S, df = folsom_limit(y, h, umax, detail = 'y', solver='ECOS')\n",
    "        forecast_result.loc[i,'Q'] = y\n",
    "        forecast_result.loc[i,'deviation'] = d\n",
    "        forecast_result.loc[i,'total objective']= get_obj(df)  # to compare across the board \n",
    "        forecast_result.loc[i,'total spill']= df.spill.sum()\n",
    "        forecast_result.loc[i,'total shortage']= df.shortage.sum()\n",
    "        forecast_result.loc[i,'storm days']= sum(df.u.values>40)\n",
    "        forecast_result.loc[i,'flood days']= sum(df.u.values>80)\n",
    "        forecast_result.loc[i,'emergency days']= sum(df.u.values>230)\n",
    "        forecast_result.loc[i,'shortage days']= sum(df.u.values<4.99)   \n",
    "        forecast_result.loc[i,'last storage']= last_S \n",
    "print('success')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize \n",
    "plt.rcParams['figure.figsize'] = [12, 12] \n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "for i in range(rep*(len(deviation)-1)+1,rep*len(deviation)):\n",
    "    if i == rep*len(deviation)-5:\n",
    "        plt.plot(save[i].inflow, label='historical')\n",
    "        plt.plot(save[i].forecast, color='C1', linewidth=0.5, alpha=0.5, label='forecast scenarios')\n",
    "    else:\n",
    "        plt.plot(save[i].forecast, color='C1', linewidth=0.5, alpha=0.5)\n",
    "plt.ylabel('inflow (TAF)')  \n",
    "plt.legend()\n",
    "plt.title('Historical Inflow v.s. Forecast')\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "for i in range(rep):\n",
    "    if i == rep-1:\n",
    "        plt.plot(save[i].x, color='C1', linewidth=0.5, alpha=0.5, label='forecast with 0.2 deviation')\n",
    "    else:\n",
    "        plt.plot(save[i].x, color='C1', linewidth=0.5, alpha=0.5)\n",
    "\n",
    "for i in range(rep*(len(deviation)-1)+1,rep*len(deviation)):\n",
    "    if i == rep*len(deviation)-1:\n",
    "        plt.plot(save[i].x, color='C2', linewidth=0.5, alpha=0.5, label='forecast with 1 deviation')\n",
    "    else:\n",
    "        plt.plot(save[i].x, color='C2', linewidth=0.5, alpha=0.5)\n",
    "plt.legend()    \n",
    "plt.ylabel('storage (TAF)')  \n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "for i in range(rep):\n",
    "    if i == rep-1:\n",
    "        plt.plot(save[i].spill, color='C1', linewidth=0.5, alpha=0.5, label='forecast with 0.2 deviation')\n",
    "    else:\n",
    "        plt.plot(save[i].spill, color='C1', linewidth=0.5, alpha=0.5)\n",
    "for i in range(rep*(len(deviation)-1)+1,rep*len(deviation)):\n",
    "    if i == rep*len(deviation)-1:\n",
    "        plt.plot(save[i].spill, color='C2', linewidth=0.5, alpha=0.5, label='forecast with 1 deviation')\n",
    "    else:\n",
    "        plt.plot(save[i].spill, color='C2', linewidth=0.5, alpha=0.5)\n",
    "plt.ylabel('spill (TAF)')  \n",
    "    \n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#forecast_result.boxplot(column=[])\n",
    "#forecast_plot = forecast_result.melt(id_vars='deviation', value_vars=['total objective', 'storm days', 'flood days', 'emergency days'])\n",
    "plt.rcParams['figure.figsize'] = [16, 4] \n",
    "\n",
    "fig, ax_new = plt.subplots(1,4, sharey=False)\n",
    "forecast_result.boxplot(column=['total objective', 'storm days', 'flood days'], \n",
    "                        by='deviation', ax=ax_new[:3], layout=(1,3), figsize=(9,4))\n",
    "\n",
    "# count number of sub-scanarios with emergency day\n",
    "x = forecast_result[['deviation', 'emergency days']].pivot_table(index='deviation', columns='emergency days', aggfunc=len) \n",
    "# total number of emergency day within a deviation\n",
    "x = forecast_result[['deviation', 'emergency days']].pivot_table(index='deviation', values='emergency days', aggfunc=np.sum) \n",
    "x.plot.bar(ax=ax_new[3], title='total number of emergency days', legend=False)\n",
    "#[ax_tmp.set_ylim(0, 20) for ax_tmp in ax_new[1]]\n",
    "fig.suptitle('Comparison of 1997 forcast model results between different forecast accuracy with 3-day horizon', y=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison between historical / perfect / limit / forcast / SLOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Historical Outflow\n",
    "unit_conv = 60*60*24/43560/1000\n",
    "test = get_year(1997,0)\n",
    "test.inflow = test.inflow.values.astype(int)*unit_conv\n",
    "test.outflow = test.outflow.values.astype(int)*unit_conv\n",
    "dftest = test.resample('M').agg({'storage': 'first', 'inflow': np.sum, 'outflow': np.sum})\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulated outflow with 14 day lead time\n",
    "df = df_Q.resample('M').agg({'x': 'first', 'inflow': np.sum, 'u': np.sum})\n",
    "df"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}