IoB_07_OSM_mergeclean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85f37628",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f352f4c0",
   "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>osmid</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>building</th>\n",
       "      <th>addr_house</th>\n",
       "      <th>addr_stree</th>\n",
       "      <th>addr_hou_1</th>\n",
       "      <th>addr_postc</th>\n",
       "      <th>building_l</th>\n",
       "      <th>start_date</th>\n",
       "      <th>...</th>\n",
       "      <th>building_m</th>\n",
       "      <th>height</th>\n",
       "      <th>roof_colou</th>\n",
       "      <th>roof_mater</th>\n",
       "      <th>roof_shape</th>\n",
       "      <th>residentia</th>\n",
       "      <th>roof_level</th>\n",
       "      <th>addr_neigh</th>\n",
       "      <th>geometry</th>\n",
       "      <th>build_prog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30527021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sim Lim Square</td>\n",
       "      <td>retail</td>\n",
       "      <td>1</td>\n",
       "      <td>Rochor Canal Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188504</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.85308 1.30254, 103.85302 1.30259...</td>\n",
       "      <td>deco_box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32342169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Museum of Singapore</td>\n",
       "      <td>public</td>\n",
       "      <td>93</td>\n",
       "      <td>Stamford Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1887</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.84797 1.29710, 103.84808 1.29718...</td>\n",
       "      <td>civic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32507125</td>\n",
       "      <td>police</td>\n",
       "      <td>Police Cantonment Complex</td>\n",
       "      <td>yes</td>\n",
       "      <td>391</td>\n",
       "      <td>New Bridge Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>088762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.83885 1.27855, 103.83912 1.27869...</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32507157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>office</td>\n",
       "      <td>11</td>\n",
       "      <td>Outram Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.83794 1.28024, 103.83794 1.28044...</td>\n",
       "      <td>glass_box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32507166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>public</td>\n",
       "      <td>3</td>\n",
       "      <td>Second Hospital Avenue</td>\n",
       "      <td>Health Promotion Board Building</td>\n",
       "      <td>168937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.83840 1.27965, 103.83823 1.27965...</td>\n",
       "      <td>civic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32507173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Dental Centre Singapore</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.83795 1.27950, 103.83806 1.27950...</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32750183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sentosa Golf Club</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.82893 1.24424, 103.82895 1.24428...</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33913082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.83843 1.27266, 103.83776 1.27264...</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33913487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St. James Power Station</td>\n",
       "      <td>industrial</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.82456 1.26470, 103.82456 1.26469...</td>\n",
       "      <td>industrial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37354400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>office</td>\n",
       "      <td>456</td>\n",
       "      <td>Alexandra Road</td>\n",
       "      <td>Fragrance Empire Building</td>\n",
       "      <td>119962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((103.80163 1.27530, 103.80161 1.27526...</td>\n",
       "      <td>glass_box</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      osmid amenity                              name    building addr_house   \n",
       "0  30527021     NaN                    Sim Lim Square      retail          1  \\\n",
       "1  32342169     NaN      National Museum of Singapore      public         93   \n",
       "2  32507125  police         Police Cantonment Complex         yes        391   \n",
       "3  32507157     NaN                               NaN      office         11   \n",
       "4  32507166     NaN                               NaN      public          3   \n",
       "5  32507173     NaN  National Dental Centre Singapore         yes        NaN   \n",
       "6  32750183     NaN                 Sentosa Golf Club         yes        NaN   \n",
       "7  33913082     NaN                               NaN         yes        NaN   \n",
       "8  33913487     NaN           St. James Power Station  industrial        NaN   \n",
       "9  37354400     NaN                               NaN      office        456   \n",
       "\n",
       "               addr_stree                       addr_hou_1 addr_postc   \n",
       "0       Rochor Canal Road                              NaN     188504  \\\n",
       "1           Stamford Road                              NaN     178897   \n",
       "2         New Bridge Road                              NaN     088762   \n",
       "3             Outram Road                              NaN     169078   \n",
       "4  Second Hospital Avenue  Health Promotion Board Building     168937   \n",
       "5                     NaN                              NaN        NaN   \n",
       "6                     NaN                              NaN        NaN   \n",
       "7                     NaN                              NaN        NaN   \n",
       "8                     NaN                              NaN        NaN   \n",
       "9          Alexandra Road        Fragrance Empire Building     119962   \n",
       "\n",
       "  building_l start_date  ... building_m height roof_colou roof_mater   \n",
       "0          6        NaN  ...        NaN    NaN        NaN        NaN  \\\n",
       "1        NaN       1887  ...        NaN    NaN        NaN        NaN   \n",
       "2        NaN       2001  ...        NaN      0        NaN        NaN   \n",
       "3        NaN       1981  ...        NaN      0        NaN        NaN   \n",
       "4        NaN       1981  ...        NaN      0        NaN        NaN   \n",
       "5        NaN        NaN  ...        NaN      0        NaN        NaN   \n",
       "6          2        NaN  ...        NaN    NaN        NaN        NaN   \n",
       "7          1        NaN  ...        NaN      5        NaN        NaN   \n",
       "8        NaN        NaN  ...        NaN    NaN        NaN        NaN   \n",
       "9        NaN        NaN  ...        NaN      0        NaN        NaN   \n",
       "\n",
       "  roof_shape residentia roof_level addr_neigh   \n",
       "0        NaN        NaN        NaN        NaN  \\\n",
       "1        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN   \n",
       "5        NaN        NaN        NaN        NaN   \n",
       "6        NaN        NaN        NaN        NaN   \n",
       "7        NaN        NaN        NaN        NaN   \n",
       "8        NaN        NaN        NaN        NaN   \n",
       "9        NaN        NaN        NaN        NaN   \n",
       "\n",
       "                                            geometry  build_prog  \n",
       "0  POLYGON ((103.85308 1.30254, 103.85302 1.30259...    deco_box  \n",
       "1  POLYGON ((103.84797 1.29710, 103.84808 1.29718...       civic  \n",
       "2  POLYGON ((103.83885 1.27855, 103.83912 1.27869...     unknown  \n",
       "3  POLYGON ((103.83794 1.28024, 103.83794 1.28044...   glass_box  \n",
       "4  POLYGON ((103.83840 1.27965, 103.83823 1.27965...       civic  \n",
       "5  POLYGON ((103.83795 1.27950, 103.83806 1.27950...     unknown  \n",
       "6  POLYGON ((103.82893 1.24424, 103.82895 1.24428...     unknown  \n",
       "7  POLYGON ((103.83843 1.27266, 103.83776 1.27264...     unknown  \n",
       "8  POLYGON ((103.82456 1.26470, 103.82456 1.26469...  industrial  \n",
       "9  POLYGON ((103.80163 1.27530, 103.80161 1.27526...   glass_box  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf1 = gpd.read_file(r'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_test.shp', encoding='latin-1')\n",
    "gdf2 = gpd.read_file(r'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_FIN.shp', encoding='latin-1')\n",
    "\n",
    "gdf21 = gdf2[['osmid', 'build_prog']]\n",
    "merged_gdf = gdf1.merge(gdf21, on='osmid')\n",
    "merged_gdf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d82bf95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_gdf['build_prog'] = merged_gdf['build_prog'].replace('unknown', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fcacf120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the count of non-NaN values for each column\n",
    "non_nan_count = merged_gdf.count()\n",
    "\n",
    "# create a bar plot of non-NaN counts with red color\n",
    "ax = non_nan_count.plot(kind='bar', color='red')\n",
    "\n",
    "# add the count percentage above each bar\n",
    "for i, v in enumerate(counts):\n",
    "    ax.text(i, v+5, str(round((v/merged_gdf.shape[0])*100, 2)) + '%', ha='center', rotation=90, size=8)\n",
    "    \n",
    "# set the x-label and y-label\n",
    "ax.set_xlabel('Columns')\n",
    "ax.set_ylabel('Number of non-NaN values')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ff1c6381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['osmid', 'amenity', 'name', 'building', 'addr_house', 'addr_stree',\n",
       "       'addr_hou_1', 'addr_postc', 'building_l', 'start_date', 'addr_count',\n",
       "       'building_c', 'building_m', 'height', 'roof_colou', 'roof_mater',\n",
       "       'roof_shape', 'residentia', 'roof_level', 'addr_neigh', 'geometry',\n",
       "       'build_prog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx = merged_gdf\n",
    "gdfx.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "eadeee0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan, 1887., 2001., 1981., 1986., 2009., 1985., 1988., 1974.,\n",
       "       1980., 1995., 2005., 1976., 1975., 1973., 2016., 2002., 2000.,\n",
       "       2020., 1880., 1968., 1972., 1983., 1939., 1993., 1949., 2006.,\n",
       "       1998., 2013., 1997., 1984., 1979., 1996., 1884., 1930., 2015.,\n",
       "       1978., 1982., 1924., 1977., 1999., 1960., 1990., 1971., 1830.,\n",
       "       1969., 1987., 2007., 1928., 2004., 1958., 1992., 1954., 2008.,\n",
       "       1927., 2011., 1965., 1970., 1945., 1994., 1991., 1989., 1964.,\n",
       "       1841., 1904., 2010., 1929., 2003., 1820., 1860., 1926., 1908.,\n",
       "       1912., 1878., 1892., 1862., 1847., 1870., 1843., 1932., 1920.,\n",
       "       1879., 1855., 1899., 1951., 2012., 2017., 1957., 1914., 2014.,\n",
       "       2019., 1961., 1937., 1898., 1891., 2018., 1839., 1906., 1861.,\n",
       "       1835., 1905., 1885., 1909., 1867., 1883., 1963., 1910., 1913.,\n",
       "       2021., 1844., 2022., 1874., 1947., 1950., 2023., 1925., 1827.,\n",
       "       1907., 1952., 1923., 1903., 1882., 1894., 1967., 1966., 1942.])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx['start_date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c19553eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count total number of no-value rows in building column\n",
    "gdfx['start_date'].value_counts().get('10/2017', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0fc1e96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace disticnctive format of year\n",
    "gdfx['start_date'] = gdfx['start_date'].replace('10/2017', '2017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c82645de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get only years\n",
    "gdfx['start_date'] = gdfx['start_date'].str.split('-').str[0].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ca630f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to integer\n",
    "gdfx['start_date'] = pd.to_numeric(gdfx['start_date'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e6ab9710",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add column with age ranges\n",
    "def generate_ranges(start_year, end_year, step):\n",
    "    ranges = []\n",
    "    for i in range(start_year, end_year+1, step):\n",
    "        end = min(i+step-1, end_year)\n",
    "        ranges.append(f\"{i}-{end}\")\n",
    "    return ranges\n",
    "\n",
    "gdfx['build_agerange'] = gdfx['start_date'].apply(lambda x: generate_ranges(1800, 2025, 25)[int(x-1800)//25] if not np.isnan(x) else np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "24618ee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111889, 23)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "64512b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdfx.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "30b7e23c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the modified shapefile\n",
    "merged_gdf.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_FIN.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75151d9a",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}