Sergio Lucero sergiolucero

Currently, only data within the last 90 days is available via the OpenAQ API. However, there is much more data available on OpenAQ and a variety of different access mechanisms. Note also that there is work under way to bring back to the API a mechanism to access the data older than 90 days, details here.

If you're looking to query across all the data or even easily export the data (or a subset of it), the easiest way to do that currently is using a service like Amazon Athena. I'll provide some directions on how to do that below, but at a high level, this will let you make any query of the entire dataset that you'd like (written in SQL). I'll also provide some sample queries so you can see what's possible.

On to the directions!

You will need to create an AWS account if you don't currently have one, you can start this process at htt

	from pptx import Presentation
	from pptx.util import Inches
	import glob

	SLIDE_LAYOUT_TITLE_AND_CONTENT = 1
	FILTER = 'fotos/*.jpg' # what to include

	prs = Presentation()
	slide_layout = prs.slide_layouts[SLIDE_LAYOUT_TITLE_AND_CONTENT]

	from creds import ID,CODE # isolines: driving from my house

	import folium
	import requests

	home =[-33.406654,-70.572701] # 085 LOS MILITARES / ALONSO DE CORDOVA
	head = 'https://isoline.route.cit.api.here.com/routing/7.2/calculateisoline.json?'
	URL_BASE = '{}app_id={}&app_code={}&mode=shortest;car;traffic:disabled&start=geo!{},{}&range={}&rangetype={}'

	def isodata(home, range, type):

	import aiohttp
	import asyncio
	import time, pandas as pd

	def async_http_get(urls, extractor=None, json_response=True):
	tasks = []
	sem = asyncio.Semaphore(32)

	async def fetch(session, url):
	async with session.get(url) as response:

	import sqlite3
	import pandas as pd
	db = sqlite3.connect('database.db')
	tables = pd.read_sql_query("SELECT * FROM sqlite_master WHERE type='table'", db)
	print(tables)

	import pandas as pd
	from geopandas import GeoDataFrame
	from shapely.geometry import Point
	import fiona

	df = pd.read_csv('data.csv')

	geometry = [Point(xy) for xy in zip(df.x, df.y)]
	crs = {'init': 'epsg:2263'} #http://www.spatialreference.org/ref/epsg/2263/
	geo_df = GeoDataFrame(df, crs=crs, geometry=geometry)

	import glob, os, pptx, scipy.misc
	from PIL import Image

	size = 800,600
	prs = pptx.Presentation()

	for fn in glob.glob('*.jpg'):
	img = Image.open(fn)
	img.thumbnail(size, Image.ANTIALIAS)
	img.save(fn)

	# -- coding: utf-8 --
	"""
	❯ termbike.py "36 Maple St, NY"
	396 - Lefferts Pl & Franklin Ave
	🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 🚲 ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮
	365 - Fulton St & Grand Ave
	🚲 🚲 🚲 🚲 🚲 ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮
	436 - Hancock St & Bedford Ave
	🚲 🚲 🚲 🚲 🚲 ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮ ☮
	437 - Macon St & Nostrand Ave

	import urllib2
	import json
	import re

	class SoccerwayTeamMatches:

	def __init__(self, teamId):
	self.teamId = str(teamId)
	self.data = {'all': [], 'home': [], 'away': []}