model_note.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ann import lstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>SO2 µg/m3</th>\n",
       "      <th>Nox µg/m3</th>\n",
       "      <th>RSPM µg/m3</th>\n",
       "      <th>SPM</th>\n",
       "      <th>CO2 µg/m3</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01 00:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-02-01 00:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>48</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-03-01 00:00:00</td>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-04-01 00:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>37</td>\n",
       "      <td>135.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-01 00:00:00</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  Date SO2 µg/m3 Nox µg/m3  RSPM µg/m3  SPM  CO2 µg/m3    AQI  \\\n",
       "0  2009-01-01 00:00:00        15        53       179.0  NaN        NaN  153.0   \n",
       "1  2009-02-01 00:00:00        15        48       156.0  NaN        NaN  137.0   \n",
       "2  2009-03-01 00:00:00        13        51       164.0  NaN        NaN  143.0   \n",
       "3  2009-04-01 00:00:00         8        37       135.0  NaN        NaN  123.0   \n",
       "4  2009-07-01 00:00:00        13        36       140.0  NaN        NaN  127.0   \n",
       "\n",
       "  Location  \n",
       "0  MPCB-KR  \n",
       "1  MPCB-KR  \n",
       "2  MPCB-KR  \n",
       "3  MPCB-KR  \n",
       "4  MPCB-KR  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatset = pd.read_csv(\"PNQ_AQI.csv\")\n",
    "datatset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatset[\"AQI\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "datatset['AQI'].fillna(value=datatset['AQI'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatset[\"AQI\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>SO2 µg/m3</th>\n",
       "      <th>Nox µg/m3</th>\n",
       "      <th>RSPM µg/m3</th>\n",
       "      <th>SPM</th>\n",
       "      <th>CO2 µg/m3</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01 00:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-02-01 00:00:00</td>\n",
       "      <td>15</td>\n",
       "      <td>48</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-03-01 00:00:00</td>\n",
       "      <td>13</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>MPCB-KR</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-04-01 00:00:00</td>\n",
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       "      <td>37</td>\n",
       "      <td>135.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-01 00:00:00</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127.0</td>\n",
       "      <td>MPCB-KR</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                  Date SO2 µg/m3 Nox µg/m3  RSPM µg/m3  SPM  CO2 µg/m3    AQI  \\\n",
       "0  2009-01-01 00:00:00        15        53       179.0  NaN        NaN  153.0   \n",
       "1  2009-02-01 00:00:00        15        48       156.0  NaN        NaN  137.0   \n",
       "2  2009-03-01 00:00:00        13        51       164.0  NaN        NaN  143.0   \n",
       "3  2009-04-01 00:00:00         8        37       135.0  NaN        NaN  123.0   \n",
       "4  2009-07-01 00:00:00        13        36       140.0  NaN        NaN  127.0   \n",
       "\n",
       "  Location  \n",
       "0  MPCB-KR  \n",
       "1  MPCB-KR  \n",
       "2  MPCB-KR  \n",
       "3  MPCB-KR  \n",
       "4  MPCB-KR  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = datatset.iloc[:,6:7].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[153.],\n",
       "       [137.],\n",
       "       [143.],\n",
       "       ...,\n",
       "       [173.],\n",
       "       [108.],\n",
       "       [147.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[153.],\n",
       "       [137.],\n",
       "       [143.],\n",
       "       [123.],\n",
       "       [127.],\n",
       "       [123.],\n",
       "       [131.],\n",
       "       [124.],\n",
       "       [110.],\n",
       "       [143.]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7844"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = data[:7844-60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_set = data[7844-60:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testing_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7784"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range= (0,1))\n",
    "training_set_scaled = sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(60, len(training_set_scaled)):\n",
    "    X_train.append(training_set_scaled[i-60:i, 0])\n",
    "    y_train.append(training_set_scaled[i, 0])\n",
    "    \n",
    "X_train, y_train = np.array(X_train), np.array(y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train =  np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Architecture of the Multi Stack LSTM Layer is ready!\n"
     ]
    }
   ],
   "source": [
    "model = lstm(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "242/242 [==============================] - 17s 56ms/step - loss: 0.0026\n",
      "Epoch 2/5\n",
      "242/242 [==============================] - 16s 66ms/step - loss: 0.0020\n",
      "Epoch 3/5\n",
      "242/242 [==============================] - 14s 60ms/step - loss: 0.0018\n",
      "Epoch 4/5\n",
      "242/242 [==============================] - 16s 67ms/step - loss: 0.0017\n",
      "Epoch 5/5\n",
      "242/242 [==============================] - 15s 63ms/step - loss: 0.0016\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x116311799a0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train_it()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_test = pd.read_csv(\"test_pune.csv\")\n",
    "testing_set = dataset_test.iloc[:, 6:7].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 91],\n",
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       "       [147]], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_total = pd.concat((datatset['AQI'], dataset_test['AQI']), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     153.0\n",
       "1     137.0\n",
       "2     143.0\n",
       "3     123.0\n",
       "4     127.0\n",
       "      ...  \n",
       "25    123.0\n",
       "26    152.0\n",
       "27    173.0\n",
       "28    108.0\n",
       "29    147.0\n",
       "Name: AQI, Length: 7874, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ravin\\AppData\\Local\\Temp\\ipykernel_8996\\2846909557.py:1: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  inputs = dataset_total[len(dataset_total)-len(dataset_test)-60 : ].values\n"
     ]
    }
   ],
   "source": [
    "inputs = dataset_total[len(dataset_total)-len(dataset_test)-60 : ].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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       "       [0.09817672],\n",
       "       [0.16830295],\n",
       "       [0.12482468],\n",
       "       [0.18513324],\n",
       "       [0.17110799],\n",
       "       [0.23281907],\n",
       "       [0.18232819],\n",
       "       [0.1542777 ],\n",
       "       [0.16409537],\n",
       "       [0.2454418 ],\n",
       "       [0.20196353],\n",
       "       [0.17952314],\n",
       "       [0.17671809],\n",
       "       [0.17110799],\n",
       "       [0.18232819],\n",
       "       [0.21178121],\n",
       "       [0.23562412],\n",
       "       [0.26227209],\n",
       "       [0.21458626],\n",
       "       [0.19915849],\n",
       "       [0.21458626],\n",
       "       [0.17251052],\n",
       "       [0.21318373],\n",
       "       [0.24263675],\n",
       "       [0.15147265],\n",
       "       [0.20617111]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = []\n",
    "\n",
    "for i in range(60, len(inputs)):\n",
    "    X_test.append(inputs[i-60:i, 0])\n",
    "X_test = np.array(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.18373072],\n",
       "        [0.20336606],\n",
       "        [0.18934081],\n",
       "        ...,\n",
       "        [0.24263675],\n",
       "        [0.15147265],\n",
       "        [0.20617111]],\n",
       "\n",
       "       [[0.20336606],\n",
       "        [0.18934081],\n",
       "        [0.18793829],\n",
       "        ...,\n",
       "        [0.15147265],\n",
       "        [0.20617111],\n",
       "        [0.12762973]],\n",
       "\n",
       "       [[0.18934081],\n",
       "        [0.18793829],\n",
       "        [0.16269285],\n",
       "        ...,\n",
       "        [0.20617111],\n",
       "        [0.12762973],\n",
       "        [0.11220196]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.07713885],\n",
       "        [0.07854137],\n",
       "        [0.12622721],\n",
       "        ...,\n",
       "        [0.21458626],\n",
       "        [0.17251052],\n",
       "        [0.21318373]],\n",
       "\n",
       "       [[0.07854137],\n",
       "        [0.12622721],\n",
       "        [0.12762973],\n",
       "        ...,\n",
       "        [0.17251052],\n",
       "        [0.21318373],\n",
       "        [0.24263675]],\n",
       "\n",
       "       [[0.12622721],\n",
       "        [0.12762973],\n",
       "        [0.11220196],\n",
       "        ...,\n",
       "        [0.21318373],\n",
       "        [0.24263675],\n",
       "        [0.15147265]]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n"
     ]
    }
   ],
   "source": [
    "y_predicted = model.predict_it(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_actual = sc.inverse_transform(y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[143.25667 ],\n",
       "       [136.79018 ],\n",
       "       [126.1556  ],\n",
       "       [112.056404],\n",
       "       [101.13794 ],\n",
       "       [ 93.96923 ],\n",
       "       [ 95.08132 ],\n",
       "       [ 98.68847 ],\n",
       "       [105.649414],\n",
       "       [112.66414 ],\n",
       "       [122.35995 ],\n",
       "       [128.59967 ],\n",
       "       [127.77393 ],\n",
       "       [123.29291 ],\n",
       "       [125.234924],\n",
       "       [129.80836 ],\n",
       "       [132.06825 ],\n",
       "       [131.50403 ],\n",
       "       [129.30304 ],\n",
       "       [127.97361 ],\n",
       "       [130.50902 ],\n",
       "       [137.58774 ],\n",
       "       [148.32727 ],\n",
       "       [154.9424  ],\n",
       "       [155.0086  ],\n",
       "       [152.53279 ],\n",
       "       [146.87964 ],\n",
       "       [143.51752 ],\n",
       "       [145.88751 ],\n",
       "       [144.36043 ]], dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(testing_set, color = 'red', label = \"Real AQI\")\n",
    "plt.plot(predicted_actual, color = 'Blue', label = \"Predicted AQI\")\n",
    "plt.title(\"AQI on testset\")\n",
    "plt.xlabel(\"Days\")\n",
    "plt.ylabel(\"AQI Levels\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.8 (tags/v3.9.8:bb3fdcf, Nov  5 2021, 20:48:33) [MSC v.1929 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9f21f1895792ccd6804a693f800a8ad892523c966fb0e2796cb78cf8e3c2fca3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}