{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amazon deforestation causes strong regional warming\n",
    "\n",
    "Edward W. Butt1*, Jessica C. A. Baker1, Francisco G. Silva Bezerra2, Celso von Randow2, Ana P. D. Aguiar2, 3 and Dominick V. Spracklen1\n",
    "\n",
    "1. School of Earth and Environment, University of Leeds, Leeds, UK\n",
    "2. National Institute for Space Research (INPE), São José dos Campos, Brazil.\n",
    "3. Stockholm Resilience Centre, Stockholm, Sweden.\n",
    "\n",
    "*Correspondence to Edward W. Butt: e.butt@leeds.ac.uk\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code to run the XGBoost model\n",
    "\n",
    "* Data used to run this code can be downloaded from: https://doi.org/10.5518/1325\n",
    "* README.txt found at the same address describes the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this code, you will need the correct packages installed on your system. You may want to create the following conda environment:\n",
    "\n",
    "conda create -n runmodel -c conda-forge -c python jupyterlab numpy pandas matplotlib seaborn jupyter scikit-learn xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import math\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.base import clone\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Dataset_butt_etal.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>Latitude_rescale</th>\n",
       "      <th>Elevation_rescale</th>\n",
       "      <th>Distance_coast_rescale</th>\n",
       "      <th>local_0-2km_start</th>\n",
       "      <th>regional_2-5km_start</th>\n",
       "      <th>regional_5-10km_start</th>\n",
       "      <th>regional_10-25km_start</th>\n",
       "      <th>regional_25-50km_start</th>\n",
       "      <th>...</th>\n",
       "      <th>regional_5-10km_end</th>\n",
       "      <th>regional_10-25km_end</th>\n",
       "      <th>regional_25-50km_end</th>\n",
       "      <th>regional_50-100km_end</th>\n",
       "      <th>Delta_T</th>\n",
       "      <th>regional_2-10km_start</th>\n",
       "      <th>regional_2-10km_end</th>\n",
       "      <th>regional_10-100km_start</th>\n",
       "      <th>regional_10-100km_end</th>\n",
       "      <th>train_test_split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.825</td>\n",
       "      <td>-68.335</td>\n",
       "      <td>0.766579</td>\n",
       "      <td>0.242639</td>\n",
       "      <td>0.508327</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.727778</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.995</td>\n",
       "      <td>-48.025</td>\n",
       "      <td>0.542679</td>\n",
       "      <td>0.253007</td>\n",
       "      <td>0.159400</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.532636</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.486667</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-7.315</td>\n",
       "      <td>-55.255</td>\n",
       "      <td>0.433684</td>\n",
       "      <td>0.528292</td>\n",
       "      <td>0.527255</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.89</td>\n",
       "      <td>...</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.826663</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.906667</td>\n",
       "      <td>0.636667</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-8.955</td>\n",
       "      <td>-54.245</td>\n",
       "      <td>0.379842</td>\n",
       "      <td>0.758576</td>\n",
       "      <td>0.582676</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.088342</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-16.405</td>\n",
       "      <td>-60.475</td>\n",
       "      <td>0.135259</td>\n",
       "      <td>0.720493</td>\n",
       "      <td>0.706369</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.943321</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      lat     lon  Latitude_rescale  Elevation_rescale  \\\n",
       "0   2.825 -68.335          0.766579           0.242639   \n",
       "1  -3.995 -48.025          0.542679           0.253007   \n",
       "2  -7.315 -55.255          0.433684           0.528292   \n",
       "3  -8.955 -54.245          0.379842           0.758576   \n",
       "4 -16.405 -60.475          0.135259           0.720493   \n",
       "\n",
       "   Distance_coast_rescale  local_0-2km_start  regional_2-5km_start  \\\n",
       "0                0.508327              0.985                  0.99   \n",
       "1                0.159400              0.860                  0.79   \n",
       "2                0.527255              0.770                  0.79   \n",
       "3                0.582676              0.980                  0.99   \n",
       "4                0.706369              0.925                  0.91   \n",
       "\n",
       "   regional_5-10km_start  regional_10-25km_start  regional_25-50km_start  ...  \\\n",
       "0                   0.99                    0.99                    0.97  ...   \n",
       "1                   0.73                    0.75                    0.75  ...   \n",
       "2                   0.77                    0.87                    0.89  ...   \n",
       "3                   0.99                    0.99                    0.96  ...   \n",
       "4                   0.92                    0.89                    0.70  ...   \n",
       "\n",
       "   regional_5-10km_end  regional_10-25km_end  regional_25-50km_end  \\\n",
       "0                 0.99                  0.99                  0.97   \n",
       "1                 0.33                  0.44                  0.50   \n",
       "2                 0.39                  0.51                  0.64   \n",
       "3                 0.99                  0.98                  0.92   \n",
       "4                 0.80                  0.67                  0.56   \n",
       "\n",
       "   regional_50-100km_end   Delta_T  regional_2-10km_start  \\\n",
       "0                   0.95 -0.727778                  0.990   \n",
       "1                   0.52  0.532636                  0.760   \n",
       "2                   0.76  1.826663                  0.780   \n",
       "3                   0.83  0.088342                  0.990   \n",
       "4                   0.60  0.943321                  0.915   \n",
       "\n",
       "   regional_2-10km_end  regional_10-100km_start  regional_10-100km_end  \\\n",
       "0                0.990                 0.973333               0.970000   \n",
       "1                0.450                 0.740000               0.486667   \n",
       "2                0.460                 0.906667               0.636667   \n",
       "3                0.990                 0.950000               0.910000   \n",
       "4                0.835                 0.760000               0.610000   \n",
       "\n",
       "   train_test_split  \n",
       "0             train  \n",
       "1             train  \n",
       "2             train  \n",
       "3             train  \n",
       "4             train  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print data columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lat', 'lon', 'Latitude_rescale', 'Elevation_rescale',\n",
       "       'Distance_coast_rescale', 'local_0-2km_start', 'regional_2-5km_start',\n",
       "       'regional_5-10km_start', 'regional_10-25km_start',\n",
       "       'regional_25-50km_start', 'regional_50-100km_start', 'local_0-2km_end',\n",
       "       'regional_2-5km_end', 'regional_5-10km_end', 'regional_10-25km_end',\n",
       "       'regional_25-50km_end', 'regional_50-100km_end', 'Delta_T',\n",
       "       'regional_2-10km_start', 'regional_2-10km_end',\n",
       "       'regional_10-100km_start', 'regional_10-100km_end', 'train_test_split'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create XGBoost model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load model parameters that we will be using, which have been selected as a result of both a cv search algorithm and trial and error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_params =  np.load('best_params.npy',allow_pickle='TRUE').item()\n",
    "\n",
    "model = xgb.XGBRegressor(colsample_bytree = best_params['colsample_bytree'],\n",
    "                         gamma = best_params['gamma'],\n",
    "                         reg_lambda = best_params['lambda'],    \n",
    "                         learning_rate = best_params['learning_rate'],\n",
    "                         max_depth = best_params['max_depth'],\n",
    "                         min_child_weight = best_params['min_child_weight'],\n",
    "                         n_estimators = best_params['n_estimators'],\n",
    "                         objective = best_params['objective'],\n",
    "                         subsample = best_params['subsample'],\n",
    "                         random_state = 42\n",
    "                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model parameters that we will be using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.7,\n",
       " 'gamma': 0,\n",
       " 'lambda': 0.5,\n",
       " 'learning_rate': 0.05,\n",
       " 'max_depth': 18,\n",
       " 'min_child_weight': 4,\n",
       " 'n_estimators': 100,\n",
       " 'objective': 'reg:squarederror',\n",
       " 'subsample': 1}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create model class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model():\n",
    "    \n",
    "    def __init__(self, model, X_train, y_train, X_test, y_test):\n",
    "        self.X_train = X_train\n",
    "        self.y_train = y_train\n",
    "        self.X_test = X_test\n",
    "        self.y_test = y_test\n",
    "        self.model = model.fit(X_train.values, y_train.values)\n",
    "            \n",
    "    def predict(self):\n",
    "        pred_train = self.model.predict(self.X_train.values)\n",
    "        pred_test = self.model.predict(self.X_test.values)\n",
    "        pred_dict = {'train':pred_train,\n",
    "                     'test':pred_test\n",
    "                    }\n",
    "        return pred_dict\n",
    "\n",
    "    def predict_metric_test(self):\n",
    "        pred_dict = self.predict()\n",
    "        X_dict = {'train': self.X_train,\n",
    "                 'test': self.X_test\n",
    "                 }\n",
    "        y_dict = {'train': self.y_train,\n",
    "                 'test': self.y_test\n",
    "                 }   \n",
    "        pred_list_labels = ['train','test'] \n",
    "        for p in pred_list_labels:\n",
    "            mae = abs(pred_dict[p] - y_dict[p])\n",
    "            rmse = round(math.sqrt( ( (mae) **2).mean() ), 6)\n",
    "            r2 = round(r2_score(pred_dict[p],y_dict[p]), 6)\n",
    "            metrics = pd.DataFrame({'MAE':[np.mean(mae)],\n",
    "                        'RMSE':[rmse],\n",
    "                        'R2':[r2]\n",
    "                            })   \n",
    "            # Save predictions and metric dataframes \n",
    "            X_copy = X_dict[p].copy()\n",
    "            X_copy['y_obs'] = y_dict[p].values\n",
    "            X_copy['y_preds'] = np.array(pred_dict[p])\n",
    "            X_copy.to_csv('DataFrame_' + p + '_obs_preds_baseline.csv', \n",
    "                           index=False)\n",
    "            metrics.to_csv('DataFrame_obs_preds_' + p + '_baseline_metrics.csv',\n",
    "                       index=False)\n",
    "            print(''+ p +' metrics are:')\n",
    "            print(metrics)\n",
    "            \n",
    "            # Save model\n",
    "            self.model.save_model('baseline_0001.model')\n",
    "\n",
    "    def plot_test_121(self):\n",
    "        prediction = self.predict()\n",
    "        prediction = prediction['test']\n",
    "        mae = abs(prediction - self.y_test)\n",
    "        met = pd.DataFrame({'MAE':[np.mean(mae)],\n",
    "                        'RMSE':[ round(math.sqrt( ( (mae) **2).mean() ), 6) ],\n",
    "                        'R2':[ round(r2_score(prediction,self.y_test), 6) ]\n",
    "                            })\n",
    "        # Plot\n",
    "        fig, ax = plt.subplots()\n",
    "        sns.scatterplot(x=np.array(prediction), y=np.array(self.y_test), s=80)\n",
    "        ax.set_ylabel('Observed \\u0394T (K)', size=15)\n",
    "        ax.set_xlabel('Predicted \\u0394T (K)', size=15)        \n",
    "        plt.xlim(-8., 11.)\n",
    "        plt.ylim(-8., 11.)\n",
    "        plt.grid(color = 'black', linestyle = '--', linewidth = 0.9, alpha=0.2)\n",
    "        style = dict(size=15, color='black')\n",
    "        ax.text(-7.,10., 'MAE: '+ str(round(met['MAE'][0],3)), **style)\n",
    "        ax.text(-7.,9, 'RMSE: '+ str(round(met['RMSE'][0],3)), **style)\n",
    "        ax.text(-7.,8., 'R$^{2}$: '+ str(round(met['R2'][0],3)), **style)\n",
    "        \n",
    "        plt.savefig('Plot_121_test_prediction_observation_data.pdf')\n",
    "        return plt.show()\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare model features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate forest fraction change or loss between start and end features for local and regional distance features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['local_0-2km_change'] = (df['local_0-2km_start'] - df['local_0-2km_end'])\n",
    "df['regional_2-5km_change'] = (df['regional_2-5km_start'] - df['regional_2-5km_end'])\n",
    "df['regional_5-10km_change'] = (df['regional_5-10km_start'] - df['regional_5-10km_end'])\n",
    "df['regional_10-25km_change'] = (df['regional_10-25km_start'] - df['regional_10-25km_end'])\n",
    "df['regional_25-50km_change'] = (df['regional_25-50km_start'] - df['regional_25-50km_end'])\n",
    "df['regional_50-100km_change'] = (df['regional_50-100km_start'] - df['regional_50-100km_end'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\n",
    "            'Latitude_rescale','Elevation_rescale','Distance_coast_rescale',\n",
    "            'local_0-2km_change',\n",
    "            'regional_2-5km_change',\n",
    "            'regional_5-10km_change', \n",
    "            'regional_10-25km_change',\n",
    "            'regional_25-50km_change',\n",
    "            'regional_50-100km_change' \n",
    "            ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target feature: Surface temperature change calculated by subtracting the average surface temperature of the driest month for two periods at the end (2018 to 2020) and start (2001 to 2003) of the study period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Delta_T'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model features are:\n",
      "Index(['Latitude_rescale', 'Elevation_rescale', 'Distance_coast_rescale',\n",
      "       'local_0-2km_change', 'regional_2-5km_change', 'regional_5-10km_change',\n",
      "       'regional_10-25km_change', 'regional_25-50km_change',\n",
      "       'regional_50-100km_change'],\n",
      "      dtype='object')\n",
      "Split into train and test datasets\n",
      "Length of train test sets are:\n",
      "3507049\n",
      "184582\n",
      "Target range for the train and test sets are:\n",
      "-9.529809511872372 11.616497620957096\n",
      "-7.522275384748882 10.641018961318537\n"
     ]
    }
   ],
   "source": [
    "# Get X,Y datasets\n",
    "X = df[features]\n",
    "y = df[target]\n",
    "    \n",
    "print('Model features are:')\n",
    "print(X.columns)\n",
    "    \n",
    "print('Split into train and test datasets')\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, random_state=42)\n",
    "    \n",
    "print('Length of train test sets are:')\n",
    "print(len(X_train))\n",
    "print(len(X_test))\n",
    "\n",
    "print('Target range for the train and test sets are:')\n",
    "print(np.min(y_train), np.max(y_train))\n",
    "print(np.min(y_test), np.max(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initiate model class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mc = Model(model, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate model evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating model evaluation metrics\n",
      "train metrics are:\n",
      "        MAE      RMSE        R2\n",
      "0  0.242419  0.340063  0.901108\n",
      "test metrics are:\n",
      "        MAE      RMSE        R2\n",
      "0  0.311401  0.453522  0.811948\n"
     ]
    }
   ],
   "source": [
    "print('Calculating model evaluation metrics')\n",
    "mc.predict_metric_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "Plot 121 for prediction on test set and observed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc.plot_test_121() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
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