{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a22039c2",
   "metadata": {},
   "source": [
    "# Import tools/models be used\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "733e2915",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "import joblib   \n",
    "import os\n",
    "import errno\n",
    "from sklearn.model_selection import train_test_split  # this is very important"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8121e032",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "115ed86d",
   "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>$M_{inv}$</th>\n",
       "      <th>$rapidity$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.079631</td>\n",
       "      <td>-0.304619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196.219429</td>\n",
       "      <td>-0.401002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>478.422633</td>\n",
       "      <td>-1.010334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.018601</td>\n",
       "      <td>0.097534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>423.950224</td>\n",
       "      <td>2.223391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>128.145991</td>\n",
       "      <td>0.246759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>576.782956</td>\n",
       "      <td>-0.554320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>35.437849</td>\n",
       "      <td>0.628855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>189.229229</td>\n",
       "      <td>0.462469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>443.127754</td>\n",
       "      <td>-1.858528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        $M_{inv}$  $rapidity$\n",
       "0      121.079631   -0.304619\n",
       "1      196.219429   -0.401002\n",
       "2      478.422633   -1.010334\n",
       "3       99.018601    0.097534\n",
       "4      423.950224    2.223391\n",
       "...           ...         ...\n",
       "9996   128.145991    0.246759\n",
       "9997   576.782956   -0.554320\n",
       "9998    35.437849    0.628855\n",
       "9999   189.229229    0.462469\n",
       "10000  443.127754   -1.858528\n",
       "\n",
       "[10001 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory = '../data/'\n",
    "filein    = 'checkLO.txt'\n",
    "file      = directory+filein\n",
    "data = np.genfromtxt(file)\n",
    "\n",
    "m        = data[:,0:1]\n",
    "rap      = data[:,1:2]\n",
    "x2       = data[:,2:3]\n",
    "\n",
    "m = np.concatenate((m),axis=0) \n",
    "rap = np.concatenate((rap),axis=0) \n",
    "x2 = np.concatenate((x2),axis=0) \n",
    "\n",
    "Data_ave = np.stack((m,rap),axis=-1)\n",
    "df = pd.DataFrame(Data_ave)\n",
    "df.columns = ['$M_{inv}$','$rapidity$']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b03b275",
   "metadata": {},
   "source": [
    "# Split data into training and testing sub-sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ecb92d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for reproducibility\n",
    "#X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6 , random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6)\n",
    "\n",
    "\n",
    "# rename data for convenience\n",
    "x2_train   = y_train\n",
    "m_train    = X_train[:,0:1]\n",
    "rap_train  = X_train[:,1:2]\n",
    "\n",
    "x2_test   = y_test\n",
    "m_test    = X_test[:,0:1]\n",
    "rap_test  = X_test[:,1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a964ef4f",
   "metadata": {},
   "source": [
    "# Now let's do some training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d93a4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Training and prediction'''\n",
    "#-----------------------------------------------------------------------------#\n",
    "# Input:                                                                      #\n",
    "# X_train  := training data set                                               #\n",
    "# y_train  := targets for training                                            #\n",
    "# X_trest  := testing data set                                                #\n",
    "# y_test   := targets for testing                                             #\n",
    "# filename := name for training storage                                       #\n",
    "# model    := name of the mode we want to use                                 #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output:                                                                     #  \n",
    "# loaded_model := trained model                                               #\n",
    "# R2 := quality of the training on the testing set                            #\n",
    "# y_fit := predicted targets                                                  #\n",
    "# y_est := estimated targets                                                  #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output labelling :                                                          #\n",
    "# Training(X,y,filename)[0] -> R2                                             #\n",
    "# Training(X,y,filename)[1] -> y_new                                          #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Note: not all models have the same characteristics\n",
    "\n",
    "def training (X_train, y_train, X_test, y_test,filename,model):\n",
    "\n",
    "    # train \n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # save trained model\n",
    "    joblib.dump(model, filename)\n",
    "\n",
    "    # load model \n",
    "    loaded_model = joblib.load(filename)\n",
    "\n",
    "    # R^2\n",
    "    R2   = loaded_model.score(X_test, y_test)\n",
    "    \n",
    "    # predicted targets \n",
    "    y_fit = loaded_model.predict(X_test)\n",
    "    # estimated targets\n",
    "    y_est = loaded_model.predict(X_train)\n",
    "\n",
    "    \n",
    "    return R2, y_fit,y_est,model.coefs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d9c2d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model\n",
    "filename = 'storage/x2_LO_NN_MPL.sav'\n",
    "\n",
    "# training data \n",
    "\n",
    "kinematic = np.stack((m,rap),axis=-1)\n",
    "kinematic_train = np.stack((m_train,rap_train),axis=-1)\n",
    "kinematic_test = np.stack((m_test,rap_test),axis=-1)\n",
    "\n",
    "n=kinematic.shape[-1]\n",
    "\n",
    "# reshape to correct dimentions\n",
    "kinematic=np.reshape(kinematic,(-1,n))\n",
    "kinematic_train=np.reshape(kinematic_train,(-1,n))\n",
    "kinematic_test=np.reshape(kinematic_test,(-1,n))\n",
    "\n",
    "X_train = kinematic_train\n",
    "X_test = kinematic_test\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,\n",
    "                                    MLPRegressor(hidden_layer_sizes=(100,100,100), tol=1e-6, max_iter=1000,activation='logistic'))\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "18f7e03d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "R2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "88b017c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-1.27582136e-04,  1.29505991e-02, -4.92851608e-03,\n",
       "          1.02782115e-03,  4.02948677e-03,  8.06902454e-04,\n",
       "          1.75571554e-01, -1.09979092e-03, -6.10789211e-04,\n",
       "         -1.07297550e-04,  3.96644099e-03, -8.16010022e-04,\n",
       "          7.26690963e-02,  7.70193260e-02, -3.20071059e-03,\n",
       "         -1.75145204e-03,  3.07647926e-05, -4.30094141e-03,\n",
       "         -9.84792988e-04,  1.05303914e-01, -4.83406286e-03,\n",
       "          3.76188166e-04,  9.92858788e-03,  3.83167400e-03,\n",
       "          5.23586242e-04, -6.44846442e-04,  8.20709892e-03,\n",
       "          1.38073589e-02,  7.36913781e-04,  7.36012952e-04,\n",
       "          4.55205534e-03, -3.22551764e-03,  5.90155065e-04,\n",
       "          2.80970697e-03,  5.17284858e-02,  1.73212739e-01,\n",
       "         -1.13448217e-03, -4.28964605e-03,  7.23958736e-04,\n",
       "          3.23307577e-03, -1.17074775e-03,  4.69900625e-03,\n",
       "          3.12161840e-03,  1.05495257e-02,  8.90446171e-03,\n",
       "         -3.96409023e-03, -1.51792866e-03, -2.55337424e-04,\n",
       "         -6.40997243e-04,  4.55886697e-03,  4.58208396e-03,\n",
       "          2.90451014e-03, -8.63786872e-04,  2.57097081e-04,\n",
       "         -3.08052189e-03, -3.01787795e-04, -1.09804283e-03,\n",
       "         -1.41688218e-03,  4.29225663e-04,  1.37336285e-02,\n",
       "          4.70783850e-03,  2.84897147e-03,  3.12280850e-03,\n",
       "         -4.63358035e-03, -8.65980077e-03,  4.73855398e-03,\n",
       "          4.96044855e-03,  3.99206403e-03,  5.34753598e-04,\n",
       "         -6.38435840e-04,  1.05962531e-01,  3.55700403e-03,\n",
       "         -9.21457009e-05,  2.13600130e-04, -5.90827104e-04,\n",
       "          4.12231897e-03, -8.44265315e-03, -1.14657155e-03,\n",
       "          1.70019661e-01,  1.46389513e-03, -4.64674608e-03,\n",
       "          1.29382891e-03,  4.12646931e-02,  5.00174865e-04,\n",
       "         -5.05063359e-03,  9.00146288e-03,  1.38413484e-02,\n",
       "         -1.19993372e-03, -1.15323353e-03, -8.62914246e-04,\n",
       "          4.16246714e-03, -5.10193157e-03,  3.71772669e-03,\n",
       "         -5.08031755e-04, -1.44379530e-04, -1.23244833e-03,\n",
       "         -3.77713649e-04, -3.35909419e-05, -2.96986931e-02,\n",
       "          3.64800331e-03],\n",
       "        [-5.94332524e-01, -1.79460287e-01,  8.99136640e-01,\n",
       "          7.15499171e-01,  1.51133221e-01,  6.93192873e-01,\n",
       "         -7.55849225e-02, -7.23675570e-01, -6.68183287e-01,\n",
       "          5.06440162e-01,  7.42464892e-02, -6.72955924e-01,\n",
       "         -8.27096601e-02, -1.04742924e-01,  3.28666362e-02,\n",
       "         -8.88371165e-01,  5.15780290e-01, -1.53511703e-01,\n",
       "         -7.41057017e-01, -1.09240218e-01, -2.33255282e-01,\n",
       "          6.03336919e-01, -2.09610082e-01,  1.22578755e-01,\n",
       "          6.38755132e-01,  4.31828048e-01, -3.12263310e-01,\n",
       "         -2.79211633e-01,  6.41471435e-01,  6.80144719e-01,\n",
       "          1.49904710e-01,  3.69110937e-02,  7.02745973e-01,\n",
       "         -1.72648663e-01,  5.56480377e-01, -3.61831734e-02,\n",
       "          1.28416972e+00, -1.48660970e-01,  6.74768791e-01,\n",
       "         -1.25993518e-01,  3.58188604e-01,  1.94013759e-01,\n",
       "         -4.34951958e-02, -2.71608890e-01, -3.05898453e-01,\n",
       "         -7.64516010e-02,  2.93733877e-01,  4.86159508e-01,\n",
       "          4.24285608e-01,  1.99840414e-01,  1.02657950e-01,\n",
       "         -6.59574514e-02, -6.86326181e-01,  5.86056910e-01,\n",
       "          1.91797290e-01, -6.52888729e-01, -7.77727451e-01,\n",
       "         -8.34491813e-01,  6.04801621e-01, -2.49983999e-01,\n",
       "          1.74853090e-01, -7.37851722e-02,  4.50183170e-03,\n",
       "         -1.96408792e-01,  3.36286530e-01,  1.96439654e-01,\n",
       "         -8.21184011e-01,  6.51543944e-02,  6.27912190e-01,\n",
       "          4.32917655e-01,  8.98375591e-02,  7.12336774e-02,\n",
       "         -6.11524293e-01,  5.46974884e-01, -6.59212881e-01,\n",
       "          2.45474216e-01,  5.31933416e-01,  3.64051464e-01,\n",
       "         -6.97619946e-02,  7.52872427e-01, -1.98544850e-01,\n",
       "         -3.08933413e-01,  4.00126400e-01, -4.47458974e-01,\n",
       "         -2.55432576e-01, -3.68472061e-01, -2.44279528e-01,\n",
       "          1.32049044e+00, -7.48358224e-01, -6.71246649e-01,\n",
       "          1.20030256e-01, -2.67284246e-01,  1.26291299e-01,\n",
       "         -6.14177970e-01,  5.05671217e-01,  3.41534545e-01,\n",
       "          4.63457514e-01,  5.05003688e-01, -1.03558815e+00,\n",
       "         -4.39366195e-03]]),\n",
       " array([[-0.01532351, -0.07703656,  0.09118878, ...,  0.09637003,\n",
       "          0.02022136,  0.0071871 ],\n",
       "        [ 0.09988894, -0.04571951, -0.03456586, ...,  0.0721587 ,\n",
       "          0.00134964,  0.07091945],\n",
       "        [-0.12334658, -0.11061914,  0.00497291, ...,  0.18097682,\n",
       "         -0.34199028,  0.14778376],\n",
       "        ...,\n",
       "        [ 0.13796049,  0.25438391, -0.30075877, ..., -0.16586296,\n",
       "          0.0784923 , -0.1950303 ],\n",
       "        [ 0.32629894,  0.1726482 , -0.53193966, ..., -0.25064884,\n",
       "          0.02809089, -0.22502171],\n",
       "        [-0.10622529, -0.02309463,  0.09176722, ...,  0.01902527,\n",
       "         -0.0587229 ,  0.02353777]]),\n",
       " array([[ 0.0742702 , -0.06088054, -0.07966346, ..., -0.09302455,\n",
       "          0.10364047, -0.05850897],\n",
       "        [ 0.04786509, -0.03686849, -0.05213381, ..., -0.07236031,\n",
       "          0.07890675, -0.00013982],\n",
       "        [ 0.02407885,  0.09975424,  0.12670214, ...,  0.06195936,\n",
       "         -0.06683956, -0.09044849],\n",
       "        ...,\n",
       "        [ 0.04171317,  0.03614853,  0.0390352 , ..., -0.06498448,\n",
       "          0.03466999,  0.03053216],\n",
       "        [ 0.0866517 , -0.03096999,  0.01353375, ..., -0.07220222,\n",
       "         -0.04630325, -0.036102  ],\n",
       "        [-0.03310578, -0.03038011,  0.11068236, ..., -0.09668933,\n",
       "         -0.10786122, -0.05576002]]),\n",
       " array([[-0.12978471],\n",
       "        [ 0.06618527],\n",
       "        [ 0.11600379],\n",
       "        [ 0.06376233],\n",
       "        [-0.07202644],\n",
       "        [ 0.15663417],\n",
       "        [-0.0796388 ],\n",
       "        [-0.1275447 ],\n",
       "        [-0.11060202],\n",
       "        [-0.04151449],\n",
       "        [-0.00885641],\n",
       "        [ 0.04748158],\n",
       "        [-0.00830514],\n",
       "        [-0.05311909],\n",
       "        [-0.11415185],\n",
       "        [ 0.04972478],\n",
       "        [ 0.01377297],\n",
       "        [ 0.08072527],\n",
       "        [ 0.11861068],\n",
       "        [-0.11598823],\n",
       "        [ 0.12047718],\n",
       "        [-0.06162617],\n",
       "        [ 0.12709434],\n",
       "        [-0.12141122],\n",
       "        [ 0.1330626 ],\n",
       "        [ 0.05084524],\n",
       "        [-0.06398825],\n",
       "        [-0.09618102],\n",
       "        [-0.12315776],\n",
       "        [ 0.02723006],\n",
       "        [ 0.07723798],\n",
       "        [-0.09086782],\n",
       "        [ 0.15017186],\n",
       "        [-0.08163758],\n",
       "        [-0.13661104],\n",
       "        [-0.01861524],\n",
       "        [ 0.05893895],\n",
       "        [ 0.08699144],\n",
       "        [-0.08932561],\n",
       "        [ 0.0195434 ],\n",
       "        [-0.03990794],\n",
       "        [ 0.07716506],\n",
       "        [ 0.04566134],\n",
       "        [ 0.02038554],\n",
       "        [ 0.00246713],\n",
       "        [ 0.11158459],\n",
       "        [-0.02923659],\n",
       "        [-0.11145232],\n",
       "        [ 0.14934996],\n",
       "        [ 0.14541796],\n",
       "        [ 0.01129477],\n",
       "        [-0.01518798],\n",
       "        [ 0.05200196],\n",
       "        [ 0.08160615],\n",
       "        [-0.02329021],\n",
       "        [-0.10269473],\n",
       "        [ 0.11825226],\n",
       "        [ 0.13670568],\n",
       "        [-0.0729188 ],\n",
       "        [ 0.03588089],\n",
       "        [-0.05907975],\n",
       "        [-0.09468634],\n",
       "        [ 0.02529566],\n",
       "        [ 0.10155311],\n",
       "        [-0.03921575],\n",
       "        [-0.08121752],\n",
       "        [ 0.1359033 ],\n",
       "        [-0.03212328],\n",
       "        [ 0.07462585],\n",
       "        [-0.01975196],\n",
       "        [ 0.0027455 ],\n",
       "        [-0.04205008],\n",
       "        [-0.04813764],\n",
       "        [ 0.12272232],\n",
       "        [-0.02283347],\n",
       "        [-0.0516751 ],\n",
       "        [ 0.04328655],\n",
       "        [ 0.10535032],\n",
       "        [ 0.04853492],\n",
       "        [-0.03136284],\n",
       "        [-0.01893061],\n",
       "        [ 0.09696487],\n",
       "        [ 0.14357793],\n",
       "        [ 0.04843211],\n",
       "        [ 0.10029236],\n",
       "        [ 0.03903645],\n",
       "        [-0.00314604],\n",
       "        [-0.00344162],\n",
       "        [-0.10893754],\n",
       "        [-0.05740873],\n",
       "        [-0.11467991],\n",
       "        [ 0.04072335],\n",
       "        [ 0.04863568],\n",
       "        [ 0.03114893],\n",
       "        [-0.05503828],\n",
       "        [ 0.03695958],\n",
       "        [-0.06858202],\n",
       "        [-0.00130368],\n",
       "        [-0.06895613],\n",
       "        [-0.03813571]])]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f69a65c",
   "metadata": {},
   "source": [
    "# Let's plot and see what we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02c82f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "ax1.scatter(m, x2, label='data')\n",
    "ax1.scatter(m_test, y_fit,label='prediction')\n",
    "ax1.scatter(m_train, y_est,label='estimated')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "ax2.scatter(rap, x2, label='data')\n",
    "ax2.scatter(rap_test, y_fit,label='prediction')\n",
    "ax2.scatter(rap_train, y_est,label='estimated')\n",
    "\n",
    "ax2.set_xlabel(xlabel='rapidity',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_LO_NN_MLP_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f63de988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1, 1)\n",
    "\n",
    "plt.ylim(-100,100)\n",
    "ax1.scatter(m_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(m_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_LO_NN_MLP_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d28fb9b1",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}