docs/source/_notebooks/postprocessing/pred_analysis_rgr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\numpy\\.libs\\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/AtrCheema/ai4water_examples/blob/master/docs/source/_notebooks/postprocessing/pred_analysis_rgr.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "\n",
    "# postprocessing of prediction results\n",
    "This file shows how to post-process prediction results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\sklearn\\experimental\\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from ai4water import Model\n",
    "from ai4water.datasets import busan_beach\n",
    "from ai4water.utils.utils import get_version_info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version: 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:16) [MSC v.1916 64 bit (AMD64)]\n",
      "os version: nt\n",
      "ai4water version: 1.06\n",
      "lightgbm version: 3.3.1\n",
      "tcn version: 3.4.0\n",
      "catboost version: 0.26\n",
      "xgboost version: 1.5.0\n",
      "easy_mpl version: 0.21.2\n",
      "SeqMetrics version: 1.3.3\n",
      "tensorflow version: 2.7.0\n",
      "keras.api._v2.keras version: 2.7.0\n",
      "numpy version: 1.21.0\n",
      "pandas version: 1.3.4\n",
      "matplotlib version: 3.4.3\n",
      "h5py version: 3.5.0\n",
      "sklearn version: 1.0.1\n",
      "shapefile version: 2.3.0\n",
      "xarray version: 0.20.1\n",
      "netCDF4 version: 1.5.7\n",
      "optuna version: 2.10.1\n",
      "skopt version: 0.9.0\n",
      "hyperopt version: 0.2.7\n",
      "plotly version: 5.3.1\n",
      "lime version: NotDefined\n",
      "seaborn version: 0.11.2\n"
     ]
    }
   ],
   "source": [
    "for k,v in get_version_info().items():\n",
    "    print(f\"{k} version: {v}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            building ML model for  \n",
      "            regression problem using XGBRegressor\n"
     ]
    }
   ],
   "source": [
    "model = Model(model=\"XGBRegressor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Training *****\n",
      "input_x shape:  (121, 13)\n",
      "target shape:  (121, 1)\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(data=busan_beach())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Training *****\n",
      "input_x shape:  (121, 13)\n",
      "target shape:  (121, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Validation *****\n",
      "input_x shape:  (31, 13)\n",
      "target shape:  (31, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Test *****\n",
      "input_x shape:  (66, 13)\n",
      "target shape:  (66, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Mean Prediction', ylabel='t'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.prediction_analysis(features=\"tide_cm\", data=busan_beach(), \n",
    "                          show_percentile=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1446, 13), (1446,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = busan_beach()\n",
    "x, y = data.iloc[:, 0:-1].values, data.iloc[:, -1].values\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "Analyzing prediction results for regression"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TBhCGVJuTK/cJ4KfVFVFVWH2iAUMsVI/VtzDS0H6EliKtgC/N7OHoNL8kPMRjuXJtFkm+rmjTNkC/5Oqk6Cb2+cBzwG+STjOA0HIpp96zYuAJPYckkkTSv3cQSqxYqMI4i/Cw0X5AO9bXxZYSEv8ehKc4c1LUOmeNhRvA3yk8eXinpAOj7XUI/Xq3SzqsEVBH0uaJ/y+5JPk9s1DFdBaApF2A/0jqZ2aLCNUyawl9lqPQG+JKwoNj5Nq1VfZZtDCWaWNJ5wJnEPp8uTn6Aksk74bk8HtWyHwIuhxn6+spE0/tDQEWE1pYfBvt1go4mPAHNyeWQNNgP725uFChHfqlkk4F5gPNCU9bovDwyyGEMTO/LX++XGZmL0vqBDwgaSbh19Pfbf3TpT0J1Ul5c11J798WwObAlWb2WFSfnijZl5Kn71kh8HboeUbhyb2VZjY2Wq4PHE74I/qFJbUfzieSjiD0J/KphRHrGxJawVwIDLX1fc/nlSjBtQI+t/VNGX9GaJ30tZn9Lc74NobWPw+RGP0pMSpU7O+Zwri4+wNfmVn3NPY/AriE0Pf/m2Z2VHYjzA5P6HlOYTDgPxK6CPhv3PFUlyoZ6i364hpCeAjp/tqPbONVdG3RvY6bCZ1ljbEcfIAqlUSJvIL1RxJunsb+nkkaRBh6755UCV1hcPcHgSFmtkTl+rjJJ17lkmcSf0zRDbcfCXXqK/IxmUOFPQMi6XTCA0b/NbOnaj+qzKggmXciNO+bA9xQ0bXng0qSeU69Z2Y2WVL75HWSOhLa1TcnNJ89yczeJ1Rf3mRmS6Jj8zKZgyf0vJPUAmGYpFMIzcwuiTeqzJG0DWGMz4tztbVOTUT3A7YCHjOz8XHHk0l59J7dRmhdNVuh/52bCb8CE40LXibc17jEksadzSde5ZLHJPUB+kQtEApG4gZwZT/tC0GhXVsuvmdRCX28mXVX6O/+a+CDpF3qmdn2CkP1rQaOIDQVngz0MLOltRzyRvMSeh4zszcI7bYLStSaJ+ea8mVSoV1bHrxnJcBSM+tdwbYFwFQzWw3Mk/QhYRSuabUYX0Z4O3TnXMGz0C//PEmHw7oHwRL91j9KeIYDSc0IVTBzYwhzo3lCd84VHEn3A68CXSUtkHQCcDRwgqQ3gXcI3R1D6I5hsaR3gUmEYQgXxxH3xvI69DwnaZSZ3RZ3HNlQqNfm1+WyxUvo+W9U6l3yVqFem1+XywpP6M45VyC8yiULmjVrZu3bt487DOcK2uuvv77IzJpX9zg1a2SsWpN6R4DvfpxgZsOq+xpx8WaLWdC+fXumT58edxjOFTRJNXuIafUaGNgh9X4Az7zXrEavERNP6Fk0+bNb4w4howa1Onnd/CfLH4wxksxq1+iIdfNr7eP4AsmwkqQn369/87L4AsmCM3v9aeNOUKDdtHtCd84VnxJP6M45VxgKM597QnfOFRnhJXTnnCsYhZnPPaE754qN/Kaoc84VjMLM557QnXNFxuvQnXOugBRmPveE7pwrQl6H7pxzBUB4Cd055wqGl9Cdc65AlMYdQHZ4QnfOFRfhJXTnnCsYhZnPPaE754qNPynqnHOFozDzuSf0QrJmzVpG7XMFzbduwpX3nMbpB1/LiuUrAViy+Du2792ey+84OcVZcstvT7mF556ewZbNGzPxtb8BcO1l/2XiE9MpKRFbNt+cv916Clu1bIqZccn5dzHpmTdo0KAe1956Cj16bxvzFVTuun+MZvTo/yKJ7j26cscd1/Dyy9P53flXsHbtWho1asgdd15Dp07t4w41bV9+vIi7z18/+MnihUvY55TBdN5pWx68fByrflhF01ZNOOavh1G/Uf34Ai3QEroPEl1Axtz+HNt03nrd8o1jf8voiX9g9MQ/sEPfDuy2T+/4gquhw4/enbvHXrjBul+feQATplzDU69czZ7DduT6Kx8GYNIzM5n30Re8MPN6rrjhJP549ug4Qk7LwoVf8M9/3sVr08bx1qwJrFmzhgceeJzTTv0j9/77Oma88SQjRw7n8stvjDvUatmqfTPOf/BUzn/wVH57/8lsUr8uPYd044E/P8oBZwzld2NOp8eQbjx398vxBZl49D+dKc94Qi8QX322hCnPvs3+I3f5ybbvv1vBjJc/YLdhvWKIbOP037UbTbZotMG6zRpvum7+h+9/RFFpa+IT0zh05CAksePOXVi29Hu+/GJJrcZbHWVla1ix4kfKysr44YcfadWqBZJYtuw7AL799jtatdwq5ihr7sOpc2nWZguatmrC158spmPf9gB0HdCRN599N97glOaU6jRSfUmvSXpT0juS/lzBPsdJ+lrSzGg6MYNXsgGvcikQN178ECf/8WB+iKpYkr349Jv03WU7Gm7WIIbIsuPqPz/AI/dPZrPGDXjgiYsB+OKzJbRqveW6fbZuvSVffvYNW229RVxhVqp1660599yTaL/NLjRoUJ+he+3GXnsN4rZ/Xcn++x1Pgwb1ady4Ea+8+kjcodbYjAmz2HGfngBsvW0LZk16n55DtmfmxLdZ+sW38QaXuaLsSmCImS2XVBd4SdJTZjal3H7/NbPTM/aqlfASegF4ZeIsmjTbjK49t6lw+7OPTWPPg/rVclTZdf7FI5jy/s0cdMSu3H3b03GHU21LlnzLuHET+WjuZBYsnML33//Av/89luuuu4PxT9zBJ5++ynHHHca55/wl7lBrpGx1Ge+88AG9h+4AwMg/H8TLD77GtSNvYeX3qyitG+OTPYl26OlMKViwPFqsG02WxeirlLWELqm9pBWSZqbYbydJZZIOS1r3tKSlksan8TqHSjJJ1cpYko6W9JakWZJekdQradswSR9ImiPpgqT1D0jqXJ3XqQ1vT/+IV555iyP7/4FLTx3NjJc/4C+/uROApd8s5/035jNgzx4xR5kdBx25G089NhWArVttwWcLF6/b9sXCxWzVqmlcoVXpf/97ifbt29K8+ZbUrVuXgw/em1defp233nyP/v37AHDEkfvz6qszYo60Zt57aTZttmvJZluG6rKtOjTnlFuP5bf3n8KO+/SgWZuY35cMVbkASCqN8txXwEQzm1rBbodG+WaMpLYbHX8lsl1C/8jMele2UVIpcBXwTLlN1wDHpDq5pM2AM4GK/gemMg/Y3cx6AJcBtyXFdBOwD9ANGCmpW3TMLcD5NXitrBp14UGMef0K/jv1ci66+QR23KUrf/znrwB4YfwMBv68O/Xq1405ysyZN+fzdfPPPDGNjl1aA/Dzffvx8P2TMTNmvPYhm22+aU5WtwC0a9eKqVPf4IcfVmBmPPfcK3Tr1olvv/2ODz+cC8DEiS+x3fadYo60ZmY8PYsdh60vRHz3TSjErl27lmf+9QI/O3ynuEIL0i+hN5M0PWkaVf5UZrYmynNtgJ0ldS+3y+NAezPrCUwE7s7WZcVdh/4b4GFgg3fXzJ6VtEcax19G+EI4r7ovbGavJC1OIbwZADsDc8xsLoRSOXAg8C7wInCXpDpmVlbd14zDc+Omc9Rpe8cdRo395lfX8+qL77Jk8Xf073oKZ//+cCY98wZzZ39GSUkJrds246/XnwTAkL37MOmZNxjU60waNNiEa285JeboK9e/fx8OPXQf+vXdnzp16tC7TzdOGjWS1m1acvhhp1JSIrbYYnNuH3113KFW28oVq/hgykcc8cfh69bNeGoWL/33NQB67rk9/Q/sE1d4QLVasCwys7R+/ZvZUkmTgGHA20nrFyftdjuQtTc1toQuqTVwMDCYcgk9zeN3BNqa2ROSqp3QyzkBeCqabw18mrRtAdAfwMzWSpoD9AJeLxfPKGAUQLt27TYynJrr87Mu9PlZl3XL1485J7ZYMuGfd575k3Ujjh1S4b6S+MvfT8h2SBlzyZ/P5pI/n73BuoMP3puDD87fL2CAeg024a8vbNjUdPejB7L70QNjiqicDHafK6k5sDpK5g2AoYRCZvI+Lc0s8bNyOPBeZl79p+IsoV8H/C5KktU6UFIJ8HfguI0NQtJgQkLfNc1DvgJaUS6hm9ltRNU2/fr1i+2miHMutXRTThp/yC2Bu6Oq2hLgQTMbL+lSYLqZjQPOkDQcKAO+IQN5qzK1ltAlnQacFC3uC/QDHoiSeTNgX0llZvZoGqfbDOgOPB8dvzUwTtJwM5uezuub2WeSehJ+Au2T9LNoIZB806JNtC6hPrAijRidczkq3UJkqoRuZm8BP6k/MrOLkuYvBC4sv0821FpCN7ObCDcbEzokZiTdBYxPM5ljZt8SvgQSxz8P/NbMpkdVOfeY2Z5Vvb6kdsAjwDFm9mHSrtOAzpI6EBL5COCopO1dSKofc87lnwJ98j/2m6IVkvQisB3QSNIC4AQzm1DuZ0xlWhJ+2qRyEbAlcHP0bV1mZv3MrEzS6cAEQjf4d5jZO1FcWwErzOyLGl+ccy5WEpSmeVN0TZZjybScSOhmdly55d0q2e+iStbvkbQ4gA1/CVT2micCFT6Ca2ZPAk9WsOko4P9Snds5l9sKtICe1YS+Bthc0syq2qJnmpllszejpcC9WTy/c64WVLchRr7IWkI3s0/Z8OZi3jOzO+OOwTm38Qo0n+dGlYtzztWWAh5S1BO6c674eJWLc84VAomSPBy8Ih2e0J1zRadAC+ie0J1zxUUU7kAQntCdc0XH69Cdc65AFGg+94TunCsy6Y0ul5c8oTvniorAW7k451yh8Dp055wrEAWazz2hO+eKT4Hmc0/ozrniInmVi3POFYzSAn2yqEAvyznnKiOk9KaUZ5LqS3pN0puS3pH05wr2qSfpv5LmSJoqqX02rgo8oTvnikyi+9x0pjSsBIaYWS+gNzBM0oBy+5wALDGzTsA/gKsydzUb8oTunCsuUR16JkroFiyPFutGk5Xb7UDg7mh+DLCnslSJ7wndOVd0qlFCbyZpetI06qfnUqmkmcBXwEQzm1pul9bApwBmVgZ8SxigPuP8pmgWDWp1ctwhZE27RkfEHUJWlGSvejNWZ/b6U9wh5JRqFI8XmVm/qnYwszVAb0lNgLGSupvZ2xsVYA15Cd05V1QSj/6nM1WHmS0FJgHDym1aSDS+sqQ6wObA4o2+kAp4QnfOFReBSpTWlPJUUvOoZI6kBsBQ4P1yu40Djo3mDwOeM7Py9ewZ4VUuWbTGPow7hIwqVZd182bzY4wks6RtkpY+iy2OzGu1bk7De8QYR+bZuFkbdXwG70m2BO6WVEooID9oZuMlXQpMN7NxwGjgXklzgG+AEZl68fI8oTvnikx6LVjSYWZvAX0qWH9R0vyPwOEZecEUPKE754pOgT757wndOVdcwoNFhZnRPaE754qLoKRAO3PxhO6cKzpeQnfOuYKQuZuiucYTunOuqEigwqxx8YTunCs+XkJ3zrkCoZLCLKJ7QnfOFZc8GIJO0jJCC8vkLgISQTc0swq/kTyhO+eKTq7XoZtZ4+RlSd0JXQbsC1Tak6MndOdcUVGetHKR1Bo4HhgOfAzcD1xmZisrO8YTunOu6ORDQgcOBs4AzgPuryqRJ+T4Dw/nnMuwDA5Bl01mdiOwB9AJmCLpbknDop4dK+QldOdckVHOP/ovafekxYnAM0B/4K/AvUDzio7zhO6cKyqhc664o0jp3ErWL4imCnlCd84Vlzxotmhmw2tyXG7/7nDVsmbNGvrteBDDD/g1APPmfcrAAYfTtfNQRo44i1WrVsUcYfVdf/0d9OixF927D+W660YD8NBDT9C9+1BKSzswffpbMUdYM8cffw4tWvSke/ch69Z9880Shg4dQefOuzB06AiWLFkKwPvvz2HgwAOoV68D1157a0wRV8PqNTD9E3juQ5g0G775Ad79Iiw/PxumzQ/7xCjX69Al1Zd0uaTpkqZF85umOs4TegG54fp72G77juuWL7zgWs466zg+mD2RLZo05o7RY2KMrvrefvsDbr/9AaZOfYyZM5/iiSeeY86cj+nevSsPP3wrgwbtHHeINXbccUfw9NP3bbDuyitvYs89d2X27JfZc89dufLKmwBo2rQJN9xwGb/97a/jCLX63v4cmjeCIV1g946wWT1o1hD26BymhvVg9texhpipMUWz6DqgMTASqAe8A/wz1UGe0AvEggVf8OSTz3P8CYcBYGZMem4Khx62NwDHHHswjz32bJwhVtt7781h5517s+mmDahTpw6DBvXnkUeeZvvtO9G1a8fUJ8hhgwYNoGnTJhuse+yxCRx7bBip7NhjD+fRR58GoEWLZuy0U2/q1q1b22FW3+o1sPh7aLdFWC4pgbql0GIzSCTILTaFH1fHFqIEJSVKa0p9LrWVNEnSu5LekXRmBfvsIelbSTOj6aKKzlXOz8zsN2Y2G1hjZv8Bdkh1kCf0AnHO2X/lyqvOoyTqo2Lx4iU0adKYOnXCbZI2bbbms4VfxhlitXXv3pWXXprG4sVL+OGHFTz11CQ+/fTzuMPKmi+/XETLllsBsPXWLfjyy0UxR1QDP6yCenVg5kJ4YQ68uRDK1m64z6dLQoKPTXrVLWlWuZQB55pZN2AAcJqkbhXs96KZ9Y6mS9MKMnlB2hxI+Y3uCb0AjB8/iRbNm9K3b/e4Q8mo7bfvxPnnn8zeex/DPvscS69e3SjN8eZmmRJ3HW6NGfDtCmjfFHbvBKUlMCepeuXDr0Kqar15XBECmatyMbPPzWxGNP8d8B7QOgMhzpPUO5pvArwG/DnVQXnz1yGpvaQVkmZWsv1oSW9JmiXpFUm9krYNk/SBpDmSLkjjtepJ+m+0/1RJ7aP1PSTdlaFLyphXXp7B448/R8cOQzh65DlMem4KZ591OUuXLqOsrAwIVTKtWm8Vc6TVd8IJRzJ9+nheeOFBtthic7p02TbukLJmq62a8fnn4VfU559/SYsWW8YcUQ3UrwP164ZqFYCWjUOCh1Ay/+o76NM23naDWXqwKMoTfYCpFWweKOlNSU9JSll1YmbDzWxmtDgM6G1m41IdlzcJPfKRmfWuZNs8YHcz6wFcBtwGED1VdROwD9ANGFnJT6JkJwBLzKwT8A/gKgAzmwW0kdRuYy8kk/56xbnM/3QyH817jvvu/zuDhwzg3n//jT0G9+fhMRMAuPfusQwfPiTFmXLPV1+FaodPPlnI2LFPc9RRNWrNlReGD9+Lu+9+CIC7736IAw/cO+aIaqB+XWhQF5ZHT6kvWg6b1Q+JfM4i2GkbqJMDaUdKb4JmUUuTxDSq4tOpEfAwcJaZLSu3eQawjZn1ItzYfDR1eKov6WxJY4ErgZMl1U91XA78n80MM3vFzJZEi1OANtH8zsAcM5trZquAB4ADU5zuQODuaH4MsKfWf10/Tuj1LOddceV5/OMfd9K181AWf7OU4084PO6Qqu2ww05hhx1+zvDhJ3DjjZfRpMnmjB37NG3bDuDVV99g//2PZ9iwY+IOs9pGjjyVgQOH88EHH9GmTV9Gj76fCy44jYkTJ9O58y78738vcsEFpwHwxRdf0aZNX/7+99v4y1+up02bvixb9l3MV1CF7i1hxqehieK3P0Kn5jDrcyhbA1M+DnXrby2MNcT08zmLzKxf0nTbT8+luoRkfp+ZPVJ+u5ktM7Pl0fyTQF1JzVKEeCehAHp9NO0Qrav6usws1T45Ifo5M97MUlYUS/otsJ2ZnSjpMGCYmZ0YbTsG6G9mp1dx/NvRMQui5Y+iYxZJ2gW4wMwOKHfMKGAUQLt27frOnz+fNfZhja41V5Wqy7p5s/kxRpJZ0jZJS5/FFkfmtVo3p+E9Yowj82zcLCS9bmb9qnts447NbOer90tr32cPu6fK14gKencD35jZWZXsszXwpZmZpJ0JhcRtrIrkK+kdM9sh1bryCu5JUUmDCVUmu2bpJb4i+S8lEn1z3wbQr1+//PiWdK4YCUoyV4e/C3AMMCvp/t7vgXYAZnYrcBhwiqQyYAUwoqpkHnlPUnczexvC/Tvgg1TB5G1Cl3QacFK0uK+ZfSapJ3A7sI+ZLY62LQTaJh3aJlpXlcQxCyTVATYHEuerT3hTnHN5SGQuoZvZS5RrYljBPjcCN1bz1K2BNyS9RWg71AuYLmlSdM7BFR2UtwndzG4i3OwEILpR+QhwjNkGdR3TgM6SOhAS9QjgqOiYK4DXzGxsudOPA44FXiV8uz6X9I3ahSpGDHHO5b4MltCz5bSaHJS3Cb0CFwFbAjdH9y/LopsYZZJOByYApcAdZvZOdEwPQvIubzRwr6Q5wDdseBN0MPBElq7BOVcLcr2Nf6Jte3UVTEKPbnqeWMm2J4EnK9hU18xerWD/H4GfNAmRVA/oB5y1UcE652IjREnVtSSxkzSXDatyjNCIpYOk8Wa2f0XH5VNCXwNsLmlmFW3Rq8XMqtvQtx2hhUtZJl7fORcDQWm8HW+lo6rWO0dVtiFvErqZfcqGNzfjiGE2MDvOGJxzGyeTN0WzaDnhCdFvzeyF5A0VPLi0Tt4kdOecy5Q8SOiPAquBLSQ9R+hO904zO7iqgzyhO+eKSp6U0Lcxsx2i+3ZTzewSSW1SHZRWQpfUBPgl0D75GDM7o2axOudcXIRy/KYo8IGk7czs/aijsPqEZ2CqlG4J/UlC/yizgLUp9nXOuZwlQZ2SnO/GqgnhwaIpwDaE52muT3VQugm9vpmdU/PYnHMud+RBlcslSfM/ArOTOh+sVLoJ/V5JJwHjgZWJlWb2TXUidM65XJDr+dzMJtfkuHQT+irgGuAPhAbuRP8W7mgDzrmClA83RSUtI4RqhKHn6gHfm1mVY/elm9DPBTqZWR4Ocuicc8mU8wndzBonL0vaF/hZquPSvTMwB/ihBnE551zOUZr/5Yqo+5IKH/dPlm4J/XtgZtR1Y3IdujdbdM7llXxo5SLp0KTFUqAvaRSq003oj5LGOHjOOZfr8qEOHUgeUqkM+JjUQ2emndDHAD+a2RpYN/ByvWoG6JxzOUD50H3u8TU5Lt3fHc8CDZKWGwD/q8kLOudc3EqU3pRvqvNg0fLEgpktl7RplmJyzrmsEeR8f+g1lW4J/XtJOyYWJPXFx9V0zuWjaJDodKaUp5LaSpok6V1J70g6s4J9JOkGSXMkvZWcSzMt3RL6WcBDkj4jfMFtDRyZraCccy5bBJRmrpVLGXCumc2QtBnwuqSJZvZu0j77AJ2jqT9wS/Rv5TFKx1a13czurmh9WgndzKZJ2g7oGq36wMxWJ734UDObmM65nHMuXpl7sMjMPgc+j+a/k/Qe0BpITugHAvdEA81PkdREUsvo2Mr0TZofATxQbrnmCT0KdjWVj3Z/FeAJ3TmXF7LRykVSe6APMLXcptbAp0nLC6J1lSb05Gd8JO1afrmy4zI1wEVh3mHYSKXqEncIWSNtE3cIWdIq7gCywsbNijuEnCFVqx16M0nTk5ZvM7PbfnpONQIeBs6qaoi4GlopaVszmyupObBJZTtmKqFb6l2ccy43VKMGfZGZVTVgM5LqEpL5fWb2SAW7LGTD8ZDbROvSdQXwiqR5QEfg95Xt6EPQOeeKSiZviirU3YwG3jOzv1ey2zjgdEkPEG6Gfpui/nwDZjZO0mSgEzDfzL6ubN9MJfSPM3SeglK29q24Q8ioOiU9182vsQ9jjCSzNqwa+yy2ODJvffWR9u4WYxyZZxPeTb1TFTL40NAuwDHALEkzo3W/B9oBmNmthBHf9mV9J4e/SnVSSXdQQVW2mVV5bLpjim5K6EK3nZmdJKkz0NXMxkcvckg653HOufhlridFM3uJFPcQo9Ytp1Xz1OOT5hsCh5NGNU26JfQ7gdeBgdHyQuChci/qnHM5Lx8656qgLv7eaHzRKqVbkdTRzK4GVkcv9gPessU5l4/S7MclF/pykbSZpIbR4ktRx4iVSnsIOkkNiFqzSOpIUr/ozjmXT3Jp8IqKKLQLvg/oAGwh6RVgVKLH28qkW0K/GHgaaCvpPkLvi+dvRLzOORcLAXVKlNYUo1uB68ysNfA+8GvgplQHpfvo/0RJM4ABhP8fZ/r4os65fJXr/aEDLc1sTDQvM/tIUrNUB1WZ0CvoFSzRdrKdpHZmNqMGgTrnXGyE8qH73A1ys6SdycAQdH+L/q0P9APeJJTQewLTWd/qxTnn8kYu3PBM4QFJPc3sLaAu4WnRk1IdVGVCN7PBAJIeAXY0s1nRcnfgko2N2Dnnap1yv8rFzP6SNN893ePSvSnaNZHMoxd4G9g+/fCccy43JEYsSmeKLUapvaRHJX0taZGkx6LeHKuUbkJ/S9LtkvaIpn8BhfVcu3OuaJSWKK0pRv8hPLy5VTQ9GK2rUroJ/VfAO8CZ0fQuafRH4JxzuSbxpGgmhqDLooZmdp+ZrTWzNWZ2H6ELgCql22zxR+Af0eScc3ktYwPQZc9jkk4hPFxkhA7AxqU6KFWzxQfN7AhJs6igz3Mz61nBYc45l8OU8zdFCeM4QxgNbp1oEGqZ2WYVHZSqhJ4Ywfo94Lzk8wJXVz9G55yLVzVHLIqFmTWuyXGpmi0mHiTqZGbzk7dFg0Y751zeKc3xhK5Kxng0s/lVDTBdZVWSpFOi6paukt5KmubhrVxyzpo1a+jX9wgOPOB0APbY/Tj67ngEfXc8gnZtfs6hB58Vb4A1cOLxF9Jyq4H06rH/unVjHnqKnt33o27pdkyfvn6szMWLl7DnkGPYfLM+nHH6pXGEm7bjjz+HFi160r37kA3W//Ofd7DddoPYYYfBnH9+aIr88cef0qBBR3r3Hkrv3kM5+eTfxRFyer5fCa/OXT89+wHM/2b99o8XwzPvwaqy2EIMN0VzvrfFxyuYEt2VX1/ZQamqXP4DPEV4SumCpPXfmdk3FR/i4nLDDfex/XbbsmzZcgCef+GudduOOOwcDhg+OKbIau6Xxx3Cqaf/gl8duz6J7dC9Cw89/E9OOfniDfatX78ef770TN55ezbvvD27tkOtluOOO4LTT/8Vv/zlmevWTZr0Mo89NoE335xIvXr1+Oqr9d0ldey4DTNnTowj1OppWA8GbhvmzeCF2dAiqu79cTUs/h7qxz/yZR5UuVR6f9LMjqhsW5UldDP71sw+NrORZjY/afJknmMWLPiSp558keNPOPgn25YtW86kSa9x4EH5l9AHDdqJpk0332Dd9tt3pGvXbX+yb8OGm7Lrrv2oX79ebYVXY4MGDaBp0yYbrLvllnu44ILTqFcvxN+iRcq+mHLb4u9h002gQd2w/P6X0KUFuTCUgtL8L7b4pFJJv5Y0JppOkZSycU4etN5x6Tj37Ku54sqzKalg8NvHHp3EkCH9ady4UQyRuXR9+OFcXnzxNfr335/ddz+UadNmrts2b94n9OmzF7vvfigvvjg1viCr44tlsHV0b++r70LJfLP68cZE3lS5XA3sCdwSTYMp1+KlIvH/9nEb7YnxL9C8RVP69u3GC89P+8n2/z7wFMef4MO+5rqysjV8881Spkx5nGnTZnLEESczd+6rtGzZgk8+eY0tt2zK66+/xUEHHc8770yiceMKW67lhrUGXy+Hzi1gzVqYuwj6tos7qiCDDw1FgznvD3xVUZ8rkvYAHgPmRaseMbN0bvAMA3omBrSQ9BzhvuV5VR2UlyX0qJ+DFUmjbCNpmKQPJM2RdEEVhyf2P0fSu9FN3meT7ypLOlbS7Gg6Nmn9/yRtkfEL2kivvDKT8Y8/T6dt9+Hoo37HpEnT+OUxFwKwaNESpk17m3332y3eIF1Kbdq05JBD9kESO+/ch5KSEhYt+oZ69eqx5ZZNAejbtycdO7bnww/nxhxtCouWQ+P6UK8O/LAKVqyGV+fB5DmwcjVMmQcr47kxKkIrl3SmNNxFSL5VedHMekdTunfrVyWPThQNNF3laEWQpwk98pGZ9YZQ30QYzWMfoBswUlK3FMe/AfSLbj6MIWpXL6kpYYSm/sDOwMVJSfxe4NQMX8dGu/yvZ/LxJxOZM/cp7vvPVQwevBP33HsFAA+Pmci++w3Ki3rlYnfQQXszadIrAHz44UesWrWKZs2a8vXXi1mzJvwtz507n9mz57HttjlS2q1McnXLZvVhcBcY1ClM9erCgA4h2cckU4/+m9lkIBv3FP+VXHiU1AT4V6qD8jmhJ9sZmGNmc81sFfAAcGBVB5jZpGiwa4ApQJtofm9gopl9Y2ZLgIms/wYeB4zMePRZ9OCDExgxYp+4w6ixo486h11/NoIPPpjHNm0Hccfoh3h07ES2aTuIKa++wfD9f80+w05Yt3/HDkP47blXcvfdY9mm7SDefXdOjNFXbuTIUxk4cDgffPARbdr0ZfTo+zn++BHMnfsJ3bsPYcSIU7n77uuQxOTJU+jZ8+f07j2Uww4bxa23XkHTpjn3Q3G9srXhhmiL3K0SkpTWlCEDJb0p6SlJO6RzgJndHOWfxPJS0sjXCiX5/BJ1Izk+UWcl6TBgmJmdGC0fA/Q3s9PTPN+NwBdm9hdJvwXqJ/ojlvQnYIWZXRstzwYGmNnicucYBYwCaNeuXd/58+dTtrawmurXKVnfkmqNfRhjJJlVqi5JS5/FFkfmtVo3p71T/WDNLzbhXSS9bmb9qntspx5t7ZrHzklr30M6njMfSB5u8zYzuy15n/L5qNy2xsBaM1suaV/gejPrnOp1JZ1OGNAi8cSoAa0JH9DrzKzCtuhFf1NU0i8IozHtnuYhXxH+UjZI6NGbfBtAv3798u9b0rkiUo3S96KafGkkmNmypPknJd0sqVkaYzKfTqgZSBxvwPOE1i6VDkVXKAl9IdA2ablNtK5Kkn4O/AHY3cxWJp1rj3Lnej5puT6wYiNidc7FqRa7xpW0NfClmVk0LmgJ5QqDlVhoZh+XO9eiVM8AFUpCnwZ0ltSBkJBHAEcBSLoCeM3MxiYfIKkP8H+EqpqvkjZNAP6adENiL+DC6BgBWwMfZ+9SnHPZlOgPPSPnku4nFACbSVpAaFBRF8DMbgUOA06RVEYoCI6wNOq5zWzPdNaVVxAJ3czKojqnCUApcIeZvRNt7kHF/QhfAzQCHop+fn1iZsPN7BtJlxG+JAAuTfpW7AtMMbP4OqJwzm20TA0vZ2ZVNpIwsxuBG6t7XklzCd89yclfZtZB0ngz27+i4woioUOonwKerGBTXTN7tYL9f17Fue4A7qhg0zHAzTUO0jmXE3K8KxcI9/Uqc1RlG/I1oa8BNpc0M9EWvTJmtncGX/dtM3s2g+dzztWyTFa5ZEtUU9AHSDwR+JKZzYi2LavsuLxsh25mn5pZ21TJPAuvm7Jhv3Mu9+VB51xnAXcCTaPpTkkp21rmawndOedqRKT9WH+cTgB2isZzRtKVhPt6f6/qIE/ozrniomq1Q4+LERp4JJRSwbjO5XlCd84VndzP5/wLmCIp0dz6ENLoy8UTunOu6GSq2WK2mNk/JU1m/U3Ro83szVTHeUJ3zhUVkftVLlF33ksJY4muW2dm86s6zhO6c67o5EHzvsdZ/2BRPaADMIfQPXilPKE754qMKhyqMZeUHyRaUg/gN6mO84TunCsqIi9K6Bsws1mSfpZqP0/ozrnikgfNFiWdm7RYSuhHakGq4zyhO+eKTpxPgaapYdJ8GWGg6YdTHeQJ3TlXVEJfLnFHUbVqDCa9AU/ozrmiU6p8q0VPjyd051zRyfEq9BrzhO6cKypx96SYTZ7QnXNFJ9f7Q68pT+jOuaJTmOk8/9rXO+fcRpFCCT2dKfW5dIekryS9Xcl2SbpB0hxJb0naMeMXlMQTunOu6GQqoQN3AcOq2L4P0DmaRgG3bHTwVfAqlyyqU9Iz9U55qlRd4g4hS1rFHUBW2IR34w4hh6SdrFMys8mS2lexy4HAPWZmhP7Nm0hqaWafZySAcryE7pwrKqJWxxRtDXyatLwgWpcVXkJ3zhWdahTQm0manrR8m5ndlvmIMsMTehb9uGZS3CFkVP3SwevmV699I8ZIMqtuSZ9182Yp+z/KG1Kb9fN7VdmNdt6xZzauCqkape9FZtZvI15qIdA2ablNtC4rvMrFOVd0arHKZRzwy6i1ywDg22zVn4OX0J1zxSaD3edKuh/Yg1A1swC4GKgLYGa3Ak8C+xJGG/oB+FVGXrgSntCdc0VFKGMJ3cxGpthuwGkZebE0eEJ3zhWdQn1S1BO6c67oeOdczjlXIHJ9CLqa8oTunCsqYZBoT+jOOVcQvITunHMFojDTuSd051zRyVyzxVzjCd05V3S8lYtzzhWARG+LhcgTunOuuMjHFHXOuYLhJXTnnCsQBVpA94TunCs2olAbLnp/6AXgxx9XseuAU9h5xxPZseevuOySuwB4/rkZDNxpFH17Hc+Jv7qSsrI18QZaAyedcAmtt96T3j0PX7duzEMT6dXjMOrV6cvr09cPdLB48VKG7jmKLRrvwpm/uTKOcNN2/PHnstVWvenRY89168477y9sv/0e9Oo1lEMOOZGlS78F4L77xtKnz97rptLSdsyc+U5coaf2zmfw/Ifwytz16z78El7+CF6dCzM/hdVJn8V5i+ClOWH7ouVZD0+EB4vSmfKNJ/QCUK9eXZ7+3995bcbtTH39Xzwz4TVefeVtTjz+Ku6570+8/uYdtGu3Ff++Z0LcoVbbL489gPFP3rjBuh26d+TBMdey26AdN1hfv349LvnzKVx19dm1GWKNHHfc4Tz11L0brBs6dDdmzfofb745kc6dt+WKK24C4OijD+aNNybwxhsTuOee6+jQoS29e+8QR9jpadUEdmy74botG8LAbcPUsB58vDisX74SvlgGP9s2HPP+F2CW9RCV5pRvPKEXAEk0atQAgNWryygrK6O0tJRNNqlD5y7hD2vIz/vy6COT4wyzRnYb1Jctmm6+wbrtt9+Wrl3b/2Tfhg0bsMuufahff5Naiq7mBg0aQNOmTTZYt9deu1OnTqgFHTCgDwsX/nRgm/vvf4wjjxxeGyHW3BabQt3SDddt2QhKohS5eX34cXWY//o72LoxlJRAg01g003g2xVZD7FEJWlN+Sb/InYVWrNmDf37nkS7locwZM9+7LTzdpSVreH16R8AMPaRySxY8HXMUbp03XnngwwbNvgn6x988HFGjjwwhogyaOG30KxRmF9ZBvXrrt9Wr05Yl0Xpls7zsYTuN0ULRGlpKVNf/xdLly7nyEMv4t13Puae+/7E+efezMqVq/j50H6Ulvr3dz64/PIbqFOnlKOPPniD9VOnvsGmmzage/ftYoosA+YuCply68axhlGozRZz+i9cUntJKyTNjJbbSpok6V1J70g6M2nfppImSpod/btFmq/RWNICSTdGy5tKekLS+9FrXJm079mSPknsm4uaNGnE7nv05pkJrzFg4A48+8L1vDTlFnbdrSedOrdJfQIXq7vuepAnnniWf//7nz+5KffAA48xYkQel84/WxpuevZovb7dYL0666tfIJTO62W7nJneDdF0b4pKGibpA0lzJF1QwfbjJH0taWY0nZjxS4rkdEKPfGRmvaP5MuBcM+sGDABOk9Qt2nYB8KyZdQaejZbTcRlQvnL5WjPbDugD7CJpHwAz+wdwUY2vJEu+/nopS5eG1gErVqzk2f+9Tteu7fjqqyUArFy5ir9d8wAnjTogzjBdCk8/PYlrrrmVxx67g003bbDBtrVr1/LQQ+MZMSLH688rs2h5uBHauw0k/1Jsvlm4Kbp2LaxYBT+sgs0bVH6eDFGa/6U8j1QK3ATsA3QDRiblpGT/NbPe0XR7Zq9mvbyqcjGzz4HPo/nvJL0HtAbeBQ4kjL4NcDfwPPC7qs4nqS+wFfA00C867w/ApGh+laQZQE4Xbb/4fDEnHX8Va9asZe3atRx62B7su/9ALjz/Vp56cgpr167lpF8PZ48hO6Y+WY75xVEXMvmF11m0aCkd2g3jootPZoumjTn7zKv5+uslHHjAGfTq1YUnnr4ZgM7b7seyZd+zatVqxj32PE88fTPdum0b81X81FFHncbzz09h0aJvaNt2Jy655FyuvPJGVq5cxV57HQVA//47cuutVwAwefJU2rZtxbbbbhNn2Ol5ayEs+T40TZw8Gzo2D00T1xq8/knYZ/MG0K0lNKoHWzUOTRwl2G7rrD/1o8w++r8zMMfM5oZz6wFCLnq3yqOyRFYLTYRqSlJ7YLyZda9k22Sgu5ktk7TUzJpE2wQsSSxXcu4S4DngF8DPgX5mdnq5fZoAM4CfJ71hx1Wy7yhgFEC7du36zp8/nx/XTKr+Reew+qXrb9KtXvtGjJFkVt2SPuvmzRbEGElmSevLIdqrokJj/rJn3kXS62bWr7rH9u3XxV6eelNa+zaos1eVryHpMGCYmZ0YLR8D9E/OD1HOuAL4GvgQONvMPq1u3OnIhyqXn5DUCHgYOMvMlpXfbuFbKtU31anAk1bJX7CkOsD9wA2JZF4VM7vNzPqZWb/mzZunvAbnXFzCIHTpTTSTND1pGlWDF3wcaG9mPYGJhBqErMirKhcASXUJyfw+M3skadOXklqa2eeSWgJfpTjVQGA3SacCjYBNJC03s0Td+23AbDO7LsOX4JyLWTVauSxK8StgIZD8FFWbaN06ZrY4afF24Op0X7y68qqEHlWljAbeM7O/l9s8Djg2mj8WeCw6ZmdJ95Q/l5kdbWbtzKw98FvgnkQyl/QXYHPgrGxch3MubhlriT4N6Cypg6RNgBGEXLT+lUIBM2E48N7GRl+ZvErowC7AMcCQpCZA+0bbrgSGSppNqBNPNDdsB6T96JlCxeMfCHesZ2S7mZFzLg5pV7lUyczKgNOBCYRE/aCZvSPpUkmJJklnRE2g3wTOAI7L7LWsl1dVLmb2EpV8bUY/a/asYFN/QrOiqs57F3BXNL+gstdwzhUCoQw+1m9mTwJPllt3UdL8hcCFGXvBKuR6CX0NsHniwaKaMLPzzOytTAQj6WzCG/OTG7HOuXySmRJ6rsnpEnrUtKdtyh1rSfRg0T/ijsM5t7EK80d4Tid055zLvESzxcLjCd05V3QKtXMuT+jOuSLkCd055wqAgNKUe+UjT+jOuaIjr0N3zrlC4VUuzjlXAJT1Lnrj4gndOVeEvMrFOefyXuh2yxO6c84VAH+wyDnnCojXoTvnXEHwKhfnnCsIaQ9ekXc8oTvnipAndOecKwiSP/rvnHMFwKtcnHOugPhNUeecKxBeQnfVVL90cNwhZE3dkj5xh5AVUpu4Q8gKe+bduEPIISrYZosys7hjKDiSvgbmxx2HcwVuGzNrXt2DJD0NNEtz90VmNqy6rxEXT+jOOVcgCvN3h3POFSFP6M45VyA8oTvnXIHwhO6ccwXCE7pzzhUIT+jOOVcgPKE751yB8ITunHMFwhO6c84VCE/ormhIukvSYXHH4Vy2eEJ3zrkC4Qnd5TVJDSU9IelNSW9LOlLSRZKmRcu3SUqrr1RJH0u6WtIsSa9J6hStPzw615uSJkfrjpN0Y9Kx4yXtkY1rdC5dntBdvhsGfGZmvcysO/A0cKOZ7RQtNwD2r8b5vjWzHsCNwHXRuouAvc2sFzA8c6E7l1me0F2+mwUMlXSVpN3M7FtgsKSpkmYBQ4AdqnG++5P+HRjNvwzcJekkoDAHo3QFwQe4cHnNzD6UtCOwL/AXSc8CpwH9zOxTSZcA9atzyvLzZnaypP7AfsDrkvoCZWxYIKrOaziXFV5Cd3lNUivgBzP7N3ANsGO0aZGkRkB1W7UcmfTvq9FrdDSzqWZ2EfA10Bb4GOgtqURSW2DnjbsS5zael9BdvusBXCNpLbAaOAU4CHgb+AKYVs3zbSHpLWAlMDJad42kzoSBKJ8F3ozWzwPeBd4DZmzENTiXET5ikXMRSR8TqmoWxR2LczXhVS7OOVcgvMrFFR1JY4EO5Vb/zszaxxCOcxnjVS7OOVcgvMrFOecKhCd055wrEJ7QnXOuQHhCd865AuEJ3TnnCsT/A5ePywMypBAiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = model.prediction_analysis(\n",
    "    features=['tide_cm', 'sal_psu'],\n",
    "    x=x, \n",
    "    y=y,\n",
    "    annotate_kws = {\n",
    "        \"annotate_counts\":True,\n",
    "        \"annotate_colors\":(\"black\", \"black\"),\n",
    "        \"annotate_fontsize\":10\n",
    "    },\n",
    "    custom_grid=[[-41.4, -20.0, 0.0, 20.0, 42.0],\n",
    "                      [33.45, 33.7, 33.9, 34.05, 34.4]],\n",
    "    border=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Edit 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": 1
}