notebooks/2 - analysis/b - Sunlight Duration EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4945f6fa-9237-4ba7-a6dc-7a370b6d445c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing dependencies\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d8d39f3-0753-4b33-8538-6b9eac49e047",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading data into memory\n",
    "\n",
    "df = pd.read_csv('../../data/interim/combined-sunlight-duration-data.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "332f7bfc-3930-4e1c-9c8f-9df6f411e518",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating UDF for converting daylength in HMS\n",
    "# to decimal hours\n",
    "\n",
    "def decimal_hr_calc(hrs, mins, secs):\n",
    "    decimal_hrs = hrs + (mins / 60) + (secs / 3600)\n",
    "    return round(decimal_hrs, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d91259b-1f5c-43d6-b4f7-d4b86acee34d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.31"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example\n",
    "\n",
    "decimal_hr_calc(10, 18, 52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38ca06c5-db17-4599-9cb3-62248c587bcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's parse the individual HMS chunks from each row to feed through our function\n",
    "# and create a new column out of em\n",
    "\n",
    "df['Daylength in decimal hours'] = [decimal_hr_calc(int(row.split(' ')[0][0:-1]),\n",
    "                                                    int(row.split(' ')[1][:-1]),\n",
    "                                                    int(row.split(' ')[2][:-1])) for row in df['Daylength']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cad012bf-c2e8-44bb-86d2-caaf5c43007e",
   "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>Date</th>\n",
       "      <th>Sunrise</th>\n",
       "      <th>Sunset</th>\n",
       "      <th>Daylength</th>\n",
       "      <th>Daylength in decimal hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/17/2021</td>\n",
       "      <td>06:21:29 AM</td>\n",
       "      <td>04:41:21 PM</td>\n",
       "      <td>10h 19m 52s</td>\n",
       "      <td>10.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/18/2021</td>\n",
       "      <td>06:22:25 AM</td>\n",
       "      <td>04:40:52 PM</td>\n",
       "      <td>10h 18m 27s</td>\n",
       "      <td>10.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/19/2021</td>\n",
       "      <td>06:23:21 AM</td>\n",
       "      <td>04:40:25 PM</td>\n",
       "      <td>10h 17m 4s</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/20/2021</td>\n",
       "      <td>06:24:17 AM</td>\n",
       "      <td>04:39:59 PM</td>\n",
       "      <td>10h 15m 42s</td>\n",
       "      <td>10.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11/21/2021</td>\n",
       "      <td>06:25:12 AM</td>\n",
       "      <td>04:39:35 PM</td>\n",
       "      <td>10h 14m 23s</td>\n",
       "      <td>10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>12/27/2022</td>\n",
       "      <td>06:50:05 AM</td>\n",
       "      <td>04:45:28 PM</td>\n",
       "      <td>9h 55m 23s</td>\n",
       "      <td>9.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>12/28/2022</td>\n",
       "      <td>06:50:23 AM</td>\n",
       "      <td>04:46:09 PM</td>\n",
       "      <td>9h 55m 46s</td>\n",
       "      <td>9.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>12/29/2022</td>\n",
       "      <td>06:50:39 AM</td>\n",
       "      <td>04:46:50 PM</td>\n",
       "      <td>9h 56m 11s</td>\n",
       "      <td>9.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>12/30/2022</td>\n",
       "      <td>06:50:54 AM</td>\n",
       "      <td>04:47:33 PM</td>\n",
       "      <td>9h 56m 39s</td>\n",
       "      <td>9.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>12/31/2022</td>\n",
       "      <td>06:51:06 AM</td>\n",
       "      <td>04:48:17 PM</td>\n",
       "      <td>9h 57m 11s</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date      Sunrise       Sunset    Daylength  \\\n",
       "0    11/17/2021  06:21:29 AM  04:41:21 PM  10h 19m 52s   \n",
       "1    11/18/2021  06:22:25 AM  04:40:52 PM  10h 18m 27s   \n",
       "2    11/19/2021  06:23:21 AM  04:40:25 PM   10h 17m 4s   \n",
       "3    11/20/2021  06:24:17 AM  04:39:59 PM  10h 15m 42s   \n",
       "4    11/21/2021  06:25:12 AM  04:39:35 PM  10h 14m 23s   \n",
       "..          ...          ...          ...          ...   \n",
       "405  12/27/2022  06:50:05 AM  04:45:28 PM   9h 55m 23s   \n",
       "406  12/28/2022  06:50:23 AM  04:46:09 PM   9h 55m 46s   \n",
       "407  12/29/2022  06:50:39 AM  04:46:50 PM   9h 56m 11s   \n",
       "408  12/30/2022  06:50:54 AM  04:47:33 PM   9h 56m 39s   \n",
       "409  12/31/2022  06:51:06 AM  04:48:17 PM   9h 57m 11s   \n",
       "\n",
       "     Daylength in decimal hours  \n",
       "0                         10.33  \n",
       "1                         10.31  \n",
       "2                         10.28  \n",
       "3                         10.26  \n",
       "4                         10.24  \n",
       "..                          ...  \n",
       "405                        9.92  \n",
       "406                        9.93  \n",
       "407                        9.94  \n",
       "408                        9.94  \n",
       "409                        9.95  \n",
       "\n",
       "[410 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b101ff9b-7641-4403-9d2b-2b24906a3045",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can't even remember why this was important...\n",
    "# ...but I wind up dropping them soon anyway\n",
    "\n",
    "df['Sunrise'] = pd.to_datetime(df['Date'] + ' ' + df['Sunrise'])\n",
    "df['Sunset'] = pd.to_datetime(df['Date'] + ' ' + df['Sunset'])\n",
    "\n",
    "# Casting `Date` column as `datetime` object to set as index...\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.set_index('Date', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a4b231b-d68c-4f54-a5eb-0c683d7756a8",
   "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>Date</th>\n",
       "      <th>Daylength in decimal hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-17</td>\n",
       "      <td>10.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>10.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-19</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-20</td>\n",
       "      <td>10.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-21</td>\n",
       "      <td>10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>9.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>9.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>9.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>9.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Daylength in decimal hours\n",
       "0   2021-11-17                       10.33\n",
       "1   2021-11-18                       10.31\n",
       "2   2021-11-19                       10.28\n",
       "3   2021-11-20                       10.26\n",
       "4   2021-11-21                       10.24\n",
       "..         ...                         ...\n",
       "405 2022-12-27                        9.92\n",
       "406 2022-12-28                        9.93\n",
       "407 2022-12-29                        9.94\n",
       "408 2022-12-30                        9.94\n",
       "409 2022-12-31                        9.95\n",
       "\n",
       "[410 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ...but wind up resetting the index here -- I'm a mess ;P\n",
    "\n",
    "sunlight_df = df.reset_index()[['Date', 'Daylength in decimal hours']]\n",
    "\n",
    "# Taking a peek\n",
    "\n",
    "sunlight_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f017e2f7-db73-4141-a3c4-d18c6e48bb4a",
   "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": [
    "# ...clearly I had to keep tinkering with the index\n",
    "# with an eye toward creating a viz, but this code smells\n",
    "\n",
    "sunlight_df.set_index('Date', inplace = True)\n",
    "plt.plot(sunlight_df)\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d705ca6c-0268-46ff-a32e-530c2532e6d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daylength in decimal hours    10.69\n",
       "Name: 2022-11-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a peek\n",
    "\n",
    "sunlight_df.loc['2022-11-4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87df501c-d9fd-43de-b22d-f862133195c7",
   "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>Date time</th>\n",
       "      <th>Solar (kW)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-11-04T00:00:00-08:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-11-04T00:15:00-08:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-11-04T00:30:00-08:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-11-04T00:45:00-08:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-11-04T01:00:00-08:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Date time  Solar (kW)\n",
       "0  2022-11-04T00:00:00-08:00         0.0\n",
       "1  2022-11-04T00:15:00-08:00         0.0\n",
       "2  2022-11-04T00:30:00-08:00         0.0\n",
       "3  2022-11-04T00:45:00-08:00         0.0\n",
       "4  2022-11-04T01:00:00-08:00         0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pulling in a few days of solar data to use as a way to evaluate the difference between\n",
    "# the recorded daylength and the total number of hours per day that the solar panels are\n",
    "# collecting/generating energy\n",
    "\n",
    "solar_data_11_4_2022 = pd.read_csv('https://gist.githubusercontent.com/nickmccarty/06d8a3bffa6c5511261ea0213faa06b3/raw/934c91f1469a4c7d0d1ce744a0c1840b1c792967/gistfile1.txt', sep='\\t')\n",
    "solar_data_5_11_2022 = pd.read_csv('https://gist.githubusercontent.com/nickmccarty/f41939563ec8402b685a1b6738e1bb0b/raw/127bbfba99ceaa73a5f489f1eeacfbc59b55564f/gistfile1.txt', sep='\\t')\n",
    "solar_data_11_17_2021 = pd.read_csv('https://gist.githubusercontent.com/nickmccarty/1bff533269b24d51f97d46f9e09f816e/raw/c841a38502937d5cfd72e65d4ca4f55677483116/gistfile1.txt', sep='\\t')\n",
    "\n",
    "solar_spot_check = solar_data_11_4_2022\n",
    "\n",
    "solar_spot_check.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fbed41f4-a26c-4cf5-a98f-de726f8e86fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'08:15'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First time corresponding to non-zero output for `Solar (kW)` --\n",
    "# solar energy starts being collected/generated at this time\n",
    "\n",
    "start_time = solar_spot_check[solar_spot_check['Solar (kW)'] != 0].iloc[0][0].split('T')[1][:5]\n",
    "start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5929958d-b8cc-4942-b05c-e98bc76e2b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'17:00'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final time corresponding to non-zero output for `Solar (kW)` --\n",
    "# solar energy stops being collected/generated at this time\n",
    "\n",
    "end_time = solar_spot_check[solar_spot_check['Solar (kW)'] != 0].iloc[-1][0].split('T')[1][:5]\n",
    "end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b2548d1-3ffa-4aef-acf3-609c1eca8a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 08:45:00')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(end_time) - pd.to_datetime(start_time)\n",
    "\n",
    "# 8.75 hours on 11-17-2021\n",
    "# 12.5 hours on 5-11-2022\n",
    "# 8.75 hours on 11-4-2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00c45953-3340-4bf9-951d-56ffda103633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average solar collection/generation duration per day,\n",
    "# based on the average of the three spot checks\n",
    "\n",
    "(8.75 + 12.5 + 8.75) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b21b6abe-48af-4dc7-b092-436110df0a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.75"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tesla says the panels started collecting energy at 6:30 AM\n",
    "# and stopped at 7 PM on 11-4-2022 (8H and 45M)\n",
    "\n",
    "decimal_hr_calc(8, 45, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d7c480a4-c03b-47d3-9cb8-02261c9d9d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.69"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sunlight_df.loc['2022-11-4'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a5c6951-21ee-4e97-a707-4c9dbd99603a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.94"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_daylight = sunlight_df.loc['2022-11-4'][0]\n",
    "collected_daylight = decimal_hr_calc(8, 45, 0)\n",
    "\n",
    "round(total_daylight - collected_daylight, 2)\n",
    "\n",
    "# That's almost two hours per day of sunlight that wasn't\n",
    "# collected that day...I blame the surrounding mountains\n",
    "\n",
    "# 1.59 fewer hours of collection/generation than there was daylight on 11-17-2021\n",
    "# 1.35 fewer hours of collection/generation than there was daylight on 5-11-2022\n",
    "# 1.94 fewer hours of collection/generation than there was daylight on 11-4-2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "31106863-606e-4fd7-a86d-44f897bcb657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.626666666666667"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average solar collection/generation duration per day,\n",
    "# based on the average of the three spot checks\n",
    "\n",
    "(1.59 + 1.35 + 1.94) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "97f592a1-e094-4936-b53d-7992e1f4025b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the above as rationale for subtracting the average\n",
    "# from daylength for downstream processes\n",
    "\n",
    "df = sunlight_df.loc['2021-11-17' : '2022-11-17'] - 1.63\n",
    "df.to_csv('../../data/processed/sunlight_duration_in_decimal_hrs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ddc69f0-f50e-4e1e-ab73-6e0e6abaeb63",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}