{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely import wkt\n",
    "from shapely.geometry import shape\n",
    "\n",
    "from google.oauth2 import service_account\n",
    "from google.cloud import bigquery\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with BigQuery GIS #\n",
    "Remake of the example https://cloud.google.com/bigquery/docs/gis-getting-started with lets_plot library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NYC_DISTRICT_IDS = [9691750, 8398124, 9691819, 9691948, 9691916]\n",
    "NYC_XMIN, NYC_XMAX, NYC_YMIN, NYC_YMAX = -74.05, -73.9, 40.65, 40.85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def osm_boundaries(osm_id):\n",
    "    response = requests.get('http://polygons.openstreetmap.fr/get_geojson.py?id={0}&params=0'.format(osm_id))\n",
    "    return gpd.GeoDataFrame(geometry=[ shape(response.json()['geometries'][0]) ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "credentials = service_account.Credentials.from_service_account_file(\n",
    "    'bigquery_account_credentials.json',\n",
    "    scopes=['https://www.googleapis.com/auth/cloud-platform'],\n",
    ")\n",
    "client = bigquery.Client(credentials=credentials, project=credentials.project_id)\n",
    "query_job = client.query('''\n",
    "    SELECT ST_GeogPoint(longitude, latitude) as WKT, num_bikes_available\n",
    "    FROM `bigquery-public-data.new_york.citibike_stations`\n",
    "    WHERE num_bikes_available > 30\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = query_job.result().to_dataframe()\n",
    "df['WKT'] = df['WKT'].apply(wkt.loads)\n",
    "gdf = gpd.GeoDataFrame(df, geometry='WKT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_gdf = pd.concat([osm_boundaries(osm_id) for osm_id in NYC_DISTRICT_IDS])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot() + \\\n",
    "    geom_rect(xmin=NYC_XMIN, xmax=NYC_XMAX, ymin=NYC_YMIN, ymax=NYC_YMAX, fill='#deebf7') + \\\n",
    "    geom_polygon(data=nyc_gdf, color='#3182bd', fill='#f1f1f1') + \\\n",
    "    geom_point(aes(size='num_bikes_available'), data=gdf, color='#0000FF', alpha=.5) + \\\n",
    "    scale_size(range=[4, 8], name='Available bikes') + \\\n",
    "    coord_cartesian(xlim=[NYC_XMIN, NYC_XMAX], ylim=[NYC_YMIN, NYC_YMAX]) + \\\n",
    "    theme(axis_title='blank', axis_text='blank', axis_ticks='blank', axis_line='blank') + \\\n",
    "    ggsize(800, 600)"
   ]
  }
 ],
 "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": "3.7.10"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}