06.TimeSeries_feature_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fcb869dd",
   "metadata": {},
   "source": [
    "# Automatic extraction of time series features "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9b938d",
   "metadata": {},
   "source": [
    "In order to correlate the CNN model performance with the properties of the groundwater time series, different libraries are applied ( kats, tsfresh, tsfel and tsfeatures) to rapidly obtain different parameters that allow us to understand and interpret the modeled results. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "43b78d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)\n",
    "from tsfeatures import tsfeatures\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from tsfresh import extract_features\n",
    "\n",
    "from tsfeatures import stability, series_length, flat_spots, stl_features,acf_features,pacf_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f49719f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read Pickle\n",
    "rpath=\"D:/Erasmus/Thesis/data\"\n",
    "GWfilldatamod=pd.read_pickle(rpath+\"/Pickle/GWfilldatamod2.pkl\")\n",
    "gw_sel_int=gpd.read_file(rpath+\"/SHP/SHP2/gw_sel_int.shp\")\n",
    "gw_sel_int['r2+NSE']=gw_sel_int['r2']+gw_sel_int['NSE']\n",
    "gw_sel_int['r2_2+NSE_2']=gw_sel_int['r2_2']+gw_sel_int['NSE_2']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15daa878",
   "metadata": {},
   "source": [
    "## Extract features available in TSfeatures library"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e175a0de",
   "metadata": {},
   "source": [
    "__Stability , lumpiness__: Stability and lumpiness are two time series features based on tiled (non-overlapping) windows. Means or variances are produced for all tiled windows. Then stability is the variance of the means, while lumpiness is the variance of the variances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bf148049",
   "metadata": {},
   "outputs": [],
   "source": [
    "#List of available features\n",
    "ltsfeatures=[ 'series_length', 'stability', 'trend', \n",
    "             'seasonal_strength', 'seas_pacf', 'flat_spots', \n",
    "             'x_acf1', 'x_acf10', 'seas_acf1']\n",
    "for i in ltsfeatures:\n",
    "    gw_sel_int[i]=np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4472b281",
   "metadata": {},
   "source": [
    "### Feature extraction per well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "74cd5da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cou=0\n",
    "for dfraw in GWfilldatamod.GW_NN:\n",
    "    \n",
    "    df=dfraw[[dfraw.columns[0],dfraw.columns[-1]]]\n",
    "    df[\"unique_id\"]=[dfraw.columns[-1]]*len(df)  #give the unique code as the library requested it \n",
    "    \n",
    "    #well id as an integer to search in the gw_sel file--> shapefile witht the well locations\n",
    "    wellid=int(dfraw.columns[-1].split(\"_\")[-1])\n",
    "    cou+=1\n",
    "    indv=gw_sel_int[gw_sel_int.MEST_ID==wellid].index[0]\n",
    "    \n",
    "    #Copy the dataframe to avoid modifications in the original one. \n",
    "    dfc=df.copy()\n",
    "    dfc.rename(columns={dfraw.columns[0]: \"ds\", dfraw.columns[-1]: \"y\"}, inplace=True)\n",
    "    feat=tsfeatures(dfc, freq=30, features=[series_length,stability, flat_spots, stl_features,acf_features,pacf_features])\n",
    "    #print(feat)\n",
    "    for c in ltsfeatures:\n",
    "        gw_sel_int.at[indv,c]=feat[c][0] \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d229c7d",
   "metadata": {},
   "source": [
    "## Extract features available in tsfresh library"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec226a7",
   "metadata": {},
   "source": [
    "__Sample entropy__: Approximate Entropy and Sample Entropy are two algorithms for determining the regularity of series of data based on the existence of patterns. (ncbi.nlm.nih.gov/pmc/articles/PMC7515030/) \n",
    "\n",
    "__cid_ce__: This function calculator is an estimate for a time series complexity [1] (A more complex time series has more peaks, valleys etc.). \n",
    "\n",
    "__autocorrelation__:Calculates the autocorrelation of the specified lag\n",
    "\n",
    "__Partial correlation__: is a method used to describe the relationship between two variables whilst taking away the effects of another variable, or several other variables, on this relationship.\n",
    "\n",
    "__number_of_peaks__: Calculates the number of peaks of at least support n in the time series x. A peak of support n is defined as a subsequence of x where a value occurs, which is bigger than its n neighbours to the left and to the right.\n",
    "\n",
    "__Lempel–Ziv complexity__ : The Lempel-Ziv complexity (LZ) is a popular tool to quantify the uncertainty contained in time series data. In particular, LZ measures how “diverse” are the patterns that are present in a particular signal. (https://information-dynamics.github.io/complexity/information/2019/06/26/lempel-ziv.html)\n",
    "\n",
    "__Matrix profile__ : The distance profile is a vector of minimum Z-Normalized Euclidean Distances.The profile index contains the index of its first nearest-neighbor. In other words, it is the location of its most similar sub-sequence. (https://towardsdatascience.com/introduction-to-matrix-profiles-5568f3375d90) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8ca58345",
   "metadata": {},
   "outputs": [],
   "source": [
    "#List of features with high-correlation according to previous checks\n",
    "lfeatures=['y__longest_strike_below_mean','y__longest_strike_above_mean',\n",
    "              'y__fourier_entropy__bins_3', 'y__matrix_profile__feature_\"mean\"__threshold_0.98',\n",
    "                'y__sample_entropy', 'y__cid_ce__normalize_True', 'y__autocorrelation__lag_6', \n",
    "               'y__lempel_ziv_complexity__bins_3','y__partial_autocorrelation__lag_3', \n",
    "               'y__number_peaks__n_10', 'y__approximate_entropy__m_2__r_0.5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "34707991",
   "metadata": {
    "collapsed": true
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    },
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      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.81s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.81s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.50s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.36s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.84s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.14s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.87s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.79s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.23s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.79s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.87s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.97s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.19s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.09s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.70s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.23s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.82s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.18s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.83s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.65s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.36s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.99s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.18s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.94s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.05s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.06s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.45s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.42s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.52s/it]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.49s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.84s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.84s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.88s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.06s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.77s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.55s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.30s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.51s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.57s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.34s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.47s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.24s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.72s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.88s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.67s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.64s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.63s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.98s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.74s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.74s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.70s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.83s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.74s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.66s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.62s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.71s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.13s/it]\n",
      "Feature Extraction: 100%|████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.06s/it]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for dfraw in GWfilldatamod.GW_NN:\n",
    "    df=dfraw[[dfraw.columns[0],dfraw.columns[-1]]]\n",
    "    df[\"id\"]=[dfraw.columns[-1]]*len(df)  #give the unique code as the library requested it \n",
    "    \n",
    "    #well id as an integer to search in the gw_sel file--> shapefile witht the well locations\n",
    "    wellid=int(dfraw.columns[-1].split(\"_\")[-1])\n",
    "    indv=gw_sel_int[gw_sel_int.MEST_ID==wellid].index[0]\n",
    "    \n",
    "    #Copy the dataframe to avoid modifications in the original one. \n",
    "    dfc=df.copy()\n",
    "    dfc.rename(columns={dfraw.columns[0]: \"time\", dfraw.columns[-1]: \"y\"}, inplace=True)\n",
    "    feat=extract_features(dfc, column_id=\"id\", column_sort=\"time\")\n",
    "    \n",
    "    for c in lfeatures:\n",
    "        gw_sel_int.at[indv,c]=feat[c][0] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a7c0a4",
   "metadata": {},
   "source": [
    "## Extract Spearmann coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d9b2b8ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>series_length</th>\n",
       "      <th>stability</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonal_strength</th>\n",
       "      <th>flat_spots</th>\n",
       "      <th>seas_acf1</th>\n",
       "      <th>y__longest_strike_below_mean</th>\n",
       "      <th>y__longest_strike_above_mean</th>\n",
       "      <th>y__fourier_entropy__bins_3</th>\n",
       "      <th>y__matrix_profile__feature_\"mean\"__threshold_0.98</th>\n",
       "      <th>y__sample_entropy</th>\n",
       "      <th>y__autocorrelation__lag_6</th>\n",
       "      <th>y__lempel_ziv_complexity__bins_3</th>\n",
       "      <th>y__partial_autocorrelation__lag_3</th>\n",
       "      <th>y__number_peaks__n_10</th>\n",
       "      <th>y__approximate_entropy__m_2__r_0.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>592.0</td>\n",
       "      <td>0.082573</td>\n",
       "      <td>0.134237</td>\n",
       "      <td>0.087312</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.437108</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.079983</td>\n",
       "      <td>1.810894</td>\n",
       "      <td>1.469238</td>\n",
       "      <td>-0.372559</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>-0.241762</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.838546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>476.0</td>\n",
       "      <td>0.059715</td>\n",
       "      <td>0.071331</td>\n",
       "      <td>0.103283</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.535319</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.079983</td>\n",
       "      <td>1.687081</td>\n",
       "      <td>1.710678</td>\n",
       "      <td>-0.508121</td>\n",
       "      <td>0.220588</td>\n",
       "      <td>-0.228476</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.979764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>379.0</td>\n",
       "      <td>0.098787</td>\n",
       "      <td>0.290659</td>\n",
       "      <td>0.117548</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.374308</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.183378</td>\n",
       "      <td>1.262183</td>\n",
       "      <td>1.317955</td>\n",
       "      <td>-0.092880</td>\n",
       "      <td>0.203166</td>\n",
       "      <td>-0.133828</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.708506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>573.0</td>\n",
       "      <td>0.121807</td>\n",
       "      <td>0.211958</td>\n",
       "      <td>0.113806</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-0.393054</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.125256</td>\n",
       "      <td>1.870703</td>\n",
       "      <td>1.303873</td>\n",
       "      <td>-0.272862</td>\n",
       "      <td>0.188482</td>\n",
       "      <td>-0.297885</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.766135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>598.0</td>\n",
       "      <td>0.169460</td>\n",
       "      <td>0.219871</td>\n",
       "      <td>0.081691</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.417612</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.090729</td>\n",
       "      <td>1.669564</td>\n",
       "      <td>1.494476</td>\n",
       "      <td>-0.272368</td>\n",
       "      <td>0.187291</td>\n",
       "      <td>-0.140223</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.789043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>278.0</td>\n",
       "      <td>0.106293</td>\n",
       "      <td>0.110449</td>\n",
       "      <td>0.149600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.479030</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.090729</td>\n",
       "      <td>1.609610</td>\n",
       "      <td>1.210662</td>\n",
       "      <td>-0.464895</td>\n",
       "      <td>0.230216</td>\n",
       "      <td>-0.218563</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.804259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>256.0</td>\n",
       "      <td>0.133299</td>\n",
       "      <td>0.215428</td>\n",
       "      <td>0.314928</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.213675</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.125256</td>\n",
       "      <td>1.977888</td>\n",
       "      <td>0.830880</td>\n",
       "      <td>-0.288653</td>\n",
       "      <td>0.238281</td>\n",
       "      <td>-0.257583</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.551084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>256.0</td>\n",
       "      <td>0.188779</td>\n",
       "      <td>0.308242</td>\n",
       "      <td>0.409320</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.080863</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.155665</td>\n",
       "      <td>2.431115</td>\n",
       "      <td>0.690597</td>\n",
       "      <td>-0.128197</td>\n",
       "      <td>0.230469</td>\n",
       "      <td>-0.016490</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.501710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>564.0</td>\n",
       "      <td>0.418150</td>\n",
       "      <td>0.687198</td>\n",
       "      <td>0.132386</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-0.117699</td>\n",
       "      <td>91.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.247940</td>\n",
       "      <td>2.244444</td>\n",
       "      <td>0.613914</td>\n",
       "      <td>0.530416</td>\n",
       "      <td>0.138298</td>\n",
       "      <td>0.076496</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.376934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>524.0</td>\n",
       "      <td>0.334756</td>\n",
       "      <td>0.375976</td>\n",
       "      <td>0.107027</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.202556</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.159721</td>\n",
       "      <td>2.033412</td>\n",
       "      <td>1.622510</td>\n",
       "      <td>-0.026569</td>\n",
       "      <td>0.166031</td>\n",
       "      <td>-0.148032</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.870415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     series_length  stability     trend  seasonal_strength  flat_spots  \\\n",
       "0            592.0   0.082573  0.134237           0.087312         6.0   \n",
       "1            476.0   0.059715  0.071331           0.103283         9.0   \n",
       "2            379.0   0.098787  0.290659           0.117548         7.0   \n",
       "3            573.0   0.121807  0.211958           0.113806         8.0   \n",
       "4            598.0   0.169460  0.219871           0.081691         7.0   \n",
       "..             ...        ...       ...                ...         ...   \n",
       "500          278.0   0.106293  0.110449           0.149600         6.0   \n",
       "501          256.0   0.133299  0.215428           0.314928         5.0   \n",
       "502          256.0   0.188779  0.308242           0.409320         4.0   \n",
       "503          564.0   0.418150  0.687198           0.132386        11.0   \n",
       "504          524.0   0.334756  0.375976           0.107027         6.0   \n",
       "\n",
       "     seas_acf1  y__longest_strike_below_mean  y__longest_strike_above_mean  \\\n",
       "0    -0.437108                          11.0                          15.0   \n",
       "1    -0.535319                          17.0                          16.0   \n",
       "2    -0.374308                          20.0                          18.0   \n",
       "3    -0.393054                           9.0                          11.0   \n",
       "4    -0.417612                          22.0                          19.0   \n",
       "..         ...                           ...                           ...   \n",
       "500  -0.479030                           9.0                           9.0   \n",
       "501  -0.213675                          10.0                          19.0   \n",
       "502  -0.080863                          18.0                          19.0   \n",
       "503  -0.117699                          91.0                          70.0   \n",
       "504  -0.202556                          10.0                          26.0   \n",
       "\n",
       "     y__fourier_entropy__bins_3  \\\n",
       "0                      0.079983   \n",
       "1                      0.079983   \n",
       "2                      0.183378   \n",
       "3                      0.125256   \n",
       "4                      0.090729   \n",
       "..                          ...   \n",
       "500                    0.090729   \n",
       "501                    0.125256   \n",
       "502                    0.155665   \n",
       "503                    0.247940   \n",
       "504                    0.159721   \n",
       "\n",
       "     y__matrix_profile__feature_\"mean\"__threshold_0.98  y__sample_entropy  \\\n",
       "0                                             1.810894           1.469238   \n",
       "1                                             1.687081           1.710678   \n",
       "2                                             1.262183           1.317955   \n",
       "3                                             1.870703           1.303873   \n",
       "4                                             1.669564           1.494476   \n",
       "..                                                 ...                ...   \n",
       "500                                           1.609610           1.210662   \n",
       "501                                           1.977888           0.830880   \n",
       "502                                           2.431115           0.690597   \n",
       "503                                           2.244444           0.613914   \n",
       "504                                           2.033412           1.622510   \n",
       "\n",
       "     y__autocorrelation__lag_6  y__lempel_ziv_complexity__bins_3  \\\n",
       "0                    -0.372559                          0.189189   \n",
       "1                    -0.508121                          0.220588   \n",
       "2                    -0.092880                          0.203166   \n",
       "3                    -0.272862                          0.188482   \n",
       "4                    -0.272368                          0.187291   \n",
       "..                         ...                               ...   \n",
       "500                  -0.464895                          0.230216   \n",
       "501                  -0.288653                          0.238281   \n",
       "502                  -0.128197                          0.230469   \n",
       "503                   0.530416                          0.138298   \n",
       "504                  -0.026569                          0.166031   \n",
       "\n",
       "     y__partial_autocorrelation__lag_3  y__number_peaks__n_10  \\\n",
       "0                            -0.241762                   26.0   \n",
       "1                            -0.228476                   20.0   \n",
       "2                            -0.133828                   17.0   \n",
       "3                            -0.297885                   23.0   \n",
       "4                            -0.140223                   32.0   \n",
       "..                                 ...                    ...   \n",
       "500                          -0.218563                   15.0   \n",
       "501                          -0.257583                   13.0   \n",
       "502                          -0.016490                    9.0   \n",
       "503                           0.076496                   19.0   \n",
       "504                          -0.148032                   26.0   \n",
       "\n",
       "     y__approximate_entropy__m_2__r_0.5  \n",
       "0                              0.838546  \n",
       "1                              0.979764  \n",
       "2                              0.708506  \n",
       "3                              0.766135  \n",
       "4                              0.789043  \n",
       "..                                  ...  \n",
       "500                            0.804259  \n",
       "501                            0.551084  \n",
       "502                            0.501710  \n",
       "503                            0.376934  \n",
       "504                            0.870415  \n",
       "\n",
       "[505 rows x 16 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Join list of features\n",
    "ltsfeatures=[ 'series_length', 'stability', 'trend', \n",
    "             'seasonal_strength', 'flat_spots', 'seas_acf1']\n",
    "lfeatures=['y__longest_strike_below_mean','y__longest_strike_above_mean',\n",
    "              'y__fourier_entropy__bins_3', 'y__matrix_profile__feature_\"mean\"__threshold_0.98',\n",
    "                'y__sample_entropy',  'y__autocorrelation__lag_6', \n",
    "               'y__lempel_ziv_complexity__bins_3','y__partial_autocorrelation__lag_3', \n",
    "               'y__number_peaks__n_10', 'y__approximate_entropy__m_2__r_0.5']\n",
    "all_features=ltsfeatures+lfeatures\n",
    "gw_sel_int[all_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "37a8f5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "perfor=['r2','NSE', 'r2_2', 'NSE_2','r2+NSE','r2_2+NSE_2']\n",
    "\n",
    "#Create dataframe to store the correlations\n",
    "lzero=np.zeros(len(all_features))\n",
    "d = {'variable': all_features, 'r2': lzero,'NSE':lzero, 'r2_2':lzero, 'NSE_2':lzero}\n",
    "dfrval = pd.DataFrame(data=d)\n",
    "dfslope = pd.DataFrame(data=d)\n",
    "spearm =pd.DataFrame(data=d)\n",
    "kendalltau= pd.DataFrame(data=d)\n",
    "#gw_sel_intc=gw_sel_int.copy()\n",
    "\n",
    "for p in perfor:\n",
    "    lslope, lrvalue,spearv, kendv = [],[],[],[]\n",
    "    \n",
    "    for col in all_features:\n",
    "        slope, intercept, r_value, p_value, std_err = stats.linregress(gw_sel_int[col], \n",
    "                                                                       gw_sel_int[p])\n",
    "        lrvalue.append(round(r_value,2)) if p_value <= 0.1 else lrvalue.append(0)\n",
    "        lslope.append(round(slope,2))\n",
    "        \n",
    "        \n",
    "        #Spearman's rank correlation coefficient\n",
    "        spear, pvalspear=stats.spearmanr(gw_sel_int[col], gw_sel_int[p])\n",
    "        spearv.append(round(spear,2)) if pvalspear <= 0.1 else spearv.append(0)\n",
    "        \n",
    "        #Kendall's tau \n",
    "        kendall, pvalkendall=stats.kendalltau(gw_sel_int[col], gw_sel_int[p])\n",
    "        kendv.append(round(kendall,2)) if pvalkendall <= 0.1 else kendv.append(0)\n",
    "                        \n",
    "    dfslope[p]=lslope\n",
    "    dfrval[p]=lrvalue\n",
    "    \n",
    "    spearm[p]=spearv\n",
    "    kendalltau[p]=kendv\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "135decfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfrvalcc=spearm.copy()\n",
    "dfrvalcc.rename(columns={\"r2\": \"r$^2$_rh\", \"NSE\": \"NSE_rh\",\n",
    "                         \"r2_2\":\"r$^2$\",\"NSE_2\":\"NSE\",\n",
    "                         \"r2+NSE\":\"r$^2$+NSE_rh\",\"r2_2+NSE_2\":\"r$^2$+NSE\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4b577c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfaux=dfrvalcc[(dfrvalcc[\"r$^2$\"] > -0.2)]\n",
    "dfaux2=dfaux[(dfaux[\"r$^2$\"] < 0.2)]\n",
    "dfaux3=dfaux2[(dfaux2[\"NSE\"] > -0.2)]\n",
    "dfaux4=dfaux3[(dfaux3[\"NSE\"] < 0.2)]\n",
    "dfrvalcc2 = dfrvalcc.drop(dfaux4.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "fb38e9f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary={\n",
    "            'series_length': 'Series length', \n",
    "             'stability': 'Stability',\n",
    "            'trend':  'Seasonal trend', \n",
    "             'seasonal_strength':'Seasonal strength', \n",
    "            'flat_spots': 'Flat spots',  \n",
    "            'x_acf10': 'Sum of squares of autocorrelation coefficients', \n",
    "            'seas_acf1': 'Seasonal autocorrelation', \n",
    "    'y__longest_strike_below_mean': 'Longest strike below mean',\n",
    "    'y__longest_strike_above_mean': 'Longest strike above mean',\n",
    "    'y__fourier_entropy__bins_3': 'Fourier entropy',\n",
    "    'y__matrix_profile__feature_\"mean\"__threshold_0.98': 'Matrix profile',\n",
    "    'y__sample_entropy': 'Sample entropy', \n",
    "    'y__autocorrelation__lag_6':'Autocorrelation', \n",
    "     'y__lempel_ziv_complexity__bins_3': 'Lempel ziv complexity',\n",
    "    'y__partial_autocorrelation__lag_3':'Partial autocorrelation', \n",
    "    'y__number_peaks__n_10': 'Number of peaks',\n",
    "    'y__approximate_entropy__m_2__r_0.5': 'Approximate entropy'\n",
    "}\n",
    "\n",
    "dictionary2={\n",
    "            'Seasonal autocorrelation':1, 'Seasonal strength':4, 'Autocorrelation':-1,\n",
    "       'Partial autocorrelation':0, 'Flat spots':6, 'Stability':3, 'Seasonal trend':5,\n",
    "       'Longest strike below mean':7, 'Fourier entropy':9,\n",
    "       'Longest strike above mean':8, 'Matrix profile':10,\n",
    "       'Sum of squares of autocorrelation coefficients':2, 'Sample entropy':12,\n",
    "       'Approximate entropy':13, 'Lempel ziv complexity':14, 'Series length':11,\n",
    "       'Number of peaks':15\n",
    "}\n",
    "\n",
    "dfrvalcc[\"variables_d\"]=dfrvalcc['variable'].map(dictionary)\n",
    "dfrvalcc[\"order\"]=dfrvalcc['variables_d'].map(dictionary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "323856ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfrvalccf1=dfrvalcc.sort_values(by=\"order\")\n",
    "dfrvalccf2=dfrvalccf1.set_index('variables_d')\n",
    "dfrvalccf=dfrvalccf2[dfrvalccf2.columns[1:-3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "002b8368",
   "metadata": {},
   "outputs": [
    {
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       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "cmap = sns.diverging_palette(20, 220, as_cmap=True)\n",
    "sns.heatmap(dfrvalccf, annot=True,cmap=cmap, center=0,  annot_kws={\"size\":8})\n",
    "plt.ylabel(' ')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72a1a077",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}