33_EMA_Dimstack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Workbench imports\n",
    "import math\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.optimize import brentq\n",
    "\n",
    "from workbench_version import * #load the model\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ema_workbench import load_results\n",
    "\n",
    "target = Path(\"temp\",\"experiments\")\n",
    "experiments, outcomes = load_results(target / \"2021_2_23_EMA_experiments_five_hundred.tar.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "      <th>policy</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395995</th>\n",
       "      <td>rational</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_430.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98995</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395996</th>\n",
       "      <td>rational</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_453.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98996</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395997</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_157.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98997</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395998</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_32.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98998</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395999</th>\n",
       "      <td>rational</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_308.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98999</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Housing_market SLR implementation_time  \\\n",
       "395995            rational   1                   4   \n",
       "395996            rational   3                  13   \n",
       "395997  boundedly_rational   3                  10   \n",
       "395998  boundedly_rational   5                  11   \n",
       "395999            rational   6                   4   \n",
       "\n",
       "                                            transient      Mayor scenario  \\\n",
       "395995  SurgeHeight\\five_hundred\\five_hundred_430.csv  Lawkeeper    98995   \n",
       "395996  SurgeHeight\\five_hundred\\five_hundred_453.csv  Lawkeeper    98996   \n",
       "395997  SurgeHeight\\five_hundred\\five_hundred_157.csv  Lawkeeper    98997   \n",
       "395998   SurgeHeight\\five_hundred\\five_hundred_32.csv  Lawkeeper    98998   \n",
       "395999  SurgeHeight\\five_hundred\\five_hundred_308.csv  Lawkeeper    98999   \n",
       "\n",
       "       policy  model  \n",
       "395995      3  Rotty  \n",
       "395996      3  Rotty  \n",
       "395997      3  Rotty  \n",
       "395998      3  Rotty  \n",
       "395999      3  Rotty  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_119.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_90.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_220.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_141.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rational</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_7.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Housing_market SLR implementation_time  \\\n",
       "0  boundedly_rational   4                  12   \n",
       "1  boundedly_rational   4                  14   \n",
       "2  boundedly_rational   2                  10   \n",
       "3  boundedly_rational   9                   9   \n",
       "4            rational   5                  13   \n",
       "\n",
       "                                       transient      Mayor scenario  \n",
       "0  SurgeHeight\\five_hundred\\five_hundred_119.csv  R. Active        0  \n",
       "1   SurgeHeight\\five_hundred\\five_hundred_90.csv  R. Active        1  \n",
       "2  SurgeHeight\\five_hundred\\five_hundred_220.csv  R. Active        2  \n",
       "3  SurgeHeight\\five_hundred\\five_hundred_141.csv  R. Active        3  \n",
       "4    SurgeHeight\\five_hundred\\five_hundred_7.csv  R. Active        4  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments2 = experiments.drop(['policy','model'],axis=1)\n",
    "experiments2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99000"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select only one mayor, because mayor does not affect behaviour here\n",
    "mask = experiments2['Mayor'] == 'Lawkeeper'\n",
    "experiments3 = experiments2.loc[mask]\n",
    "for key, value in outcomes.items():\n",
    "    outcomes[key] = value[mask]\n",
    "len(outcomes['HP_first_SETP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments3 = experiments2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing market (R1)</th>\n",
       "      <th>SLR scenario (X1)</th>\n",
       "      <th>Implementation time (X3)</th>\n",
       "      <th>Transient storm surge (X2)</th>\n",
       "      <th>Management strategy (L1)</th>\n",
       "      <th>scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>119</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>90</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>220</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>141</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "0                   0                 4                        12   \n",
       "1                   0                 4                        14   \n",
       "2                   0                 2                        10   \n",
       "3                   0                 9                         9   \n",
       "4                   1                 5                        13   \n",
       "\n",
       "   Transient storm surge (X2) Management strategy (L1) scenario  \n",
       "0                         119                R. Active        0  \n",
       "1                          90                R. Active        1  \n",
       "2                         220                R. Active        2  \n",
       "3                         141                R. Active        3  \n",
       "4                           7                R. Active        4  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#experiments3 = experiments2.copy()\n",
    "\n",
    "#The discretization function is slightly altered, if you now provide numbers as strings, the categories will\n",
    "#ordered in ascending order\n",
    "map_house_market = {'rational' : '1', \n",
    "             'boundedly_rational' :'0'}\n",
    "\n",
    "map_house_market_invert = {'1':'R', \n",
    "                           '0':'BR'}\n",
    "\n",
    "\n",
    "rename_cols = {'Housing_market' : 'Housing market (R1)',\n",
    "               'SLR' : 'SLR scenario (X1)',\n",
    "               'implementation_time' : 'Implementation time (X3)',\n",
    "               'transient' : 'Transient storm surge (X2)',\n",
    "               'Mayor' : 'Management strategy (L1)'}\n",
    "\n",
    "experiments3['Housing_market'].replace(map_house_market,inplace=True)\n",
    "experiments3['transient'] = experiments3['transient'].apply(lambda s : int(s.split('_')[-1].split('.')[0]))\n",
    "cols_convert = ['transient', 'implementation_time']\n",
    "experiments3[cols_convert] = experiments3[cols_convert].astype('int')\n",
    "experiments3 = experiments3.rename(columns=rename_cols)\n",
    "#experiments3 = experiments3.drop(columns=['Management strategy (L1)'],axis=1)\n",
    "experiments3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396000 entries, 0 to 395999\n",
      "Data columns (total 6 columns):\n",
      " #   Column                      Non-Null Count   Dtype   \n",
      "---  ------                      --------------   -----   \n",
      " 0   Housing market (R1)         396000 non-null  object  \n",
      " 1   SLR scenario (X1)           396000 non-null  category\n",
      " 2   Implementation time (X3)    396000 non-null  int32   \n",
      " 3   Transient storm surge (X2)  396000 non-null  int32   \n",
      " 4   Management strategy (L1)    396000 non-null  category\n",
      " 5   scenario                    396000 non-null  category\n",
      "dtypes: category(3), int32(2), object(1)\n",
      "memory usage: 11.6+ MB\n"
     ]
    }
   ],
   "source": [
    "experiments3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outerdike area before 2100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ema_workbench.analysis.dimensional_stacking as ds \n",
    "import ema_workbench.analysis.feature_scoring as feature_scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = experiments3\n",
    "threshold_year = 2100\n",
    "y = outcomes['HP_first_SETP'] <threshold_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.create_pivot_plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the colormap, inspired from:\n",
    "https://stackoverflow.com/questions/49367144/modify-matplotlib-colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "#say we have 1000 different values\n",
    "n_vals = 256\n",
    "\n",
    "#variant with black, and color grading from blue to yellow \n",
    "upper = mpl.cm.cividis(np.arange(n_vals))\n",
    "upper[0] = [0,0,0,1] #set the first rgb*alpha* to black\n",
    "\n",
    "#variant from green to red via yellow\n",
    "#upper = mpl.cm.YlOrRd(np.arange(n_vals))\n",
    "#upper[0] = [0,0,0,1] #set the first rgb*alpha* to black\n",
    "\n",
    "\n",
    "cmap = upper\n",
    "cmap = mpl.colors.ListedColormap(cmap, name='newmap', N=cmap.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot convert column_data to integer for Management strategy (L1), will therefore rename categories\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "def discretize_new(data, nbins=3, with_labels=False):\n",
    "    ### Adapted version of the EMA-workbench\n",
    "    \n",
    "    ''' Discretize the data, using the number of bins specified.\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : DataFrame\n",
    "    nbins : int, optional\n",
    "            the number of bins to use (default is 3)\n",
    "    with_labels : bool, optional\n",
    "    Returns\n",
    "    -------\n",
    "    discretized\n",
    "        the discretized data frame\n",
    "    note:: nbins is currently a constant for all float and integer columns.\n",
    "           Categorical data is not discretized. If the number of integers is\n",
    "           lower than the number of bins, the integer variable is also not\n",
    "           discretized.\n",
    "    '''\n",
    "    discretized = data.copy()\n",
    "\n",
    "    for i, entry in enumerate(data.dtypes):\n",
    "        column = data.columns[i]\n",
    "        column_data = data[column]\n",
    "        n = nbins\n",
    "\n",
    "\n",
    "        if entry.name == 'category':\n",
    "            n_unique = column_data.unique().shape[0]\n",
    "            n = n_unique\n",
    "            try:\n",
    "                column_data = column_data.astype('int')\n",
    "                indices = column_data\n",
    "            except:\n",
    "                print('Cannot convert column_data to integer for {}, will therefore rename categories'.format(column_data.name))\n",
    "                column_data = column_data.cat.rename_categories(\n",
    "                        [x for x in range(1, n + 1)])\n",
    "                indices = column_data\n",
    "            \n",
    "        else:\n",
    "            if issubclass(entry.type, np.integer):\n",
    "                n_unique = column_data.unique().shape[0]\n",
    "                if n_unique <= n:\n",
    "                    n = n_unique\n",
    "\n",
    "            if with_labels:\n",
    "                indices = pd.cut(column_data, n, precision=2,\n",
    "                                 retbins=True)[0]\n",
    "            else:\n",
    "                indices = pd.cut(column_data, n, retbins=False,\n",
    "                                 labels=False, precision=2)\n",
    "\n",
    "        discretized[column] = indices\n",
    "\n",
    "    return discretized\n",
    "\n",
    "def create_pivot_plot_new(x, y, nr_levels=3, labels=True, categories=True,\n",
    "                      nbins=3, bin_labels=True,cmap='cividis'):\n",
    "    #This is a slightly adapted version of the convenience function in the EMA-workbench\n",
    "    \n",
    "    \n",
    "    x = ds._prepare_experiments(x)\n",
    "    scores = feature_scoring.get_ex_feature_scores(x, y)[0]\n",
    "    x = x[scores.index]\n",
    "\n",
    "    n = nr_levels * 2\n",
    "    scores = scores.index.tolist()\n",
    "\n",
    "    #MANUALLY OVERRULLED THESE TO CONTROL LAY-OUT OF FIGURE\n",
    "    rows = [entry for entry in scores[0:n:2]]\n",
    "    columns = [entry for entry in scores[1:n:2]]\n",
    "    #print(rows,columns)\n",
    "    #rows = ['SLR scenario (X1)', 'Housing market (R1)'] \n",
    "    #columns = ['Management strategy (L1)', 'Implementation time (X3)']\n",
    "    \n",
    "    discretized_x = discretize_new(x, nbins=nbins, with_labels=bin_labels)\n",
    "\n",
    "    ooi_label = 'y'\n",
    "    ooi = pd.DataFrame(y[:, np.newaxis], columns=[ooi_label])\n",
    "    #ooi = 100*ooi\n",
    "\n",
    "    x_y_concat = pd.concat([discretized_x, ooi], axis=1)\n",
    "    \n",
    "    pvt = pd.pivot_table(x_y_concat, values=ooi_label, index=rows,\n",
    "                         columns=columns, dropna=False)\n",
    "    \n",
    "    #Added to make percentags\n",
    "    pvt = 100*pvt\n",
    "    \n",
    "    #changed vmax \n",
    "    fig = plot_pivot_table_new(pvt, plot_labels=labels,\n",
    "        plot_cats=categories,figsize=(11,11),\n",
    "        cmap=cmap,vmax=100,annot=False)\n",
    "    \n",
    "    return fig,pvt,discretized_x\n",
    "\n",
    "def plot_pivot_table_new(table, plot_labels=True, plot_cats=True,\n",
    "                     figsize=(10, 10), cmap='viridis', **kwargs):\n",
    "    ''' visualize a pivot table using colors\n",
    "    Parameters\n",
    "    ----------\n",
    "    table : Pandas DataFrame\n",
    "    plot_labels : bool, optional\n",
    "                 if true, display uncertain factor names\n",
    "    plot_cats : bool, optional\n",
    "                 if true, display category labels for each uncertain factor\n",
    "    fig_size : tuple of 2 ints, optional\n",
    "               size of the figure to create\n",
    "    cmap : matplotlib colormap name or object, optional\n",
    "           default is viridis (requires matplotlib 1.5 or higher)\n",
    "    kwargs : other keyword arguments\n",
    "             All other keyword arguments are passed to ax.pcolormesh.\n",
    "    Returns\n",
    "    -------\n",
    "    Figure\n",
    "    '''\n",
    "\n",
    "    with sns.axes_style('white'):\n",
    "\n",
    "        fig = plt.figure(figsize=figsize)\n",
    "\n",
    "        width_ratios = ds.dim_ratios(figsize=figsize, axis=1)\n",
    "        height_ratios = ds.dim_ratios(figsize=figsize, axis=0)\n",
    "\n",
    "        gs = mpl.gridspec.GridSpec(3, 3, wspace=0.01, hspace=0.01,\n",
    "                                   width_ratios=width_ratios,\n",
    "                                   height_ratios=height_ratios)\n",
    "\n",
    "        ax_plot = fig.add_subplot(gs[2, 2])\n",
    "        ax_rows = fig.add_subplot(gs[2, 0:2], facecolor=\"white\")\n",
    "        ax_cols = fig.add_subplot(gs[0:2, 2], facecolor=\"white\")\n",
    "        cax = fig.add_subplot(gs[0, 0])\n",
    "\n",
    "        # actual plotting\n",
    "        plot_data = table.values\n",
    "        sns.heatmap(plot_data, ax=ax_plot, cbar_ax=cax, cmap=cmap, \n",
    "                    vmin=0, **kwargs)\n",
    "\n",
    "        # set the tick labels\n",
    "        ax_plot.set_xticks([])\n",
    "        ax_plot.set_yticks([])\n",
    "\n",
    "        # plot row labeling\n",
    "        ax_rows.set_ylim(ax_plot.get_ylim())\n",
    "        ax_rows.set_xlim(0, 1)\n",
    "        ds.plot_index(ax_rows, ax_plot, axis=1, index=table.index,\n",
    "                   plot_labels=plot_labels, plot_cats=plot_cats)\n",
    "\n",
    "        # plot column labeling\n",
    "        ax_cols.set_xlim(ax_plot.get_xlim())\n",
    "        ax_cols.set_ylim(0, 1)\n",
    "        ds.plot_index(ax_cols, ax_plot, axis=0, index=table.columns,\n",
    "                   plot_labels=plot_labels, plot_cats=plot_cats)\n",
    "\n",
    "    return fig\n",
    "\n",
    "fig,pvt,discretized_x = create_pivot_plot_new(x,y, nr_levels=2, nbins=2,categories=True,bin_labels=True,cmap=cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we make the plot slightly nicer by inverting the mapping "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(10.0, 12.0], (12.0, 14.0], (8.0, 10.0], (3.99, 6.0], (6.0, 8.0]]\n",
      "Categories (5, interval[float64]): [(3.99, 6.0] < (6.0, 8.0] < (8.0, 10.0] < (10.0, 12.0] < (12.0, 14.0]]\n"
     ]
    }
   ],
   "source": [
    "ITs = discretized_x['Implementation time (X3)'].unique() #unique values of implementation times\n",
    "print(ITs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Provide nicer categories for the ITs\n",
    "map_ITs = {str(ITs[3]) : '4-6',\n",
    "           str(ITs[4]) : '7-8',\n",
    "           str(ITs[1]) : '13-14',\n",
    "           str(ITs[2]) : '9-10',\n",
    "           str(ITs[0]) : '11-12'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizontal_texts = fig._axstack._elements[2][1][1].texts\n",
    "for i,text in enumerate(horizontal_texts):\n",
    "    if i % (len(ITs)+1) == 0: #it's a mayor text\n",
    "        text._text = map_mayor_invert[text._text]\n",
    "        \n",
    "    else: #It is an implementation time\n",
    "            text._text = map_ITs[text._text]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 4 Axes>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertical_texts = fig._axstack._elements[1][1][1].texts\n",
    "for i, text in enumerate(vertical_texts):\n",
    "    if i % 3 == 0: #number can be divided by three\n",
    "        pass #don't change the SLR labels\n",
    "    else:\n",
    "        text._text = map_house_market_invert[text._text]\n",
    "        #if text._text == '0': text._text = 'R'\n",
    "        #elif text._text == '1': text._text =  'BR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 4 Axes>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "today = datetime.date(datetime.now())\n",
    "date = \"{}_{}_{}\".format(today.year,today.month,today.day)\n",
    "\n",
    "target = Path(date)\n",
    "\n",
    "if not Path.exists(target): Path.mkdir(target)\n",
    "\n",
    "fig.savefig((target / \"Dimstack_fivehundred_CC_below_{}.png\".format(threshold_year)),bbox_inches=\"tight\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}