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        "\n# Airnow Data\n\nThis example shows the different ways to pull\nair quality information from EPA's AirNow API for\na station near to SGP\n"
      ]
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      "source": [
        "import os\n\nimport matplotlib.pyplot as plt\n\nimport act\n\n# You need an account and token from https://docs.airnowapi.org/ first\ntoken = os.getenv('AIRNOW_API')\n\nif token is not None and len(token) > 0:\n    # This first example will get the forcasted values for the date passed\n    # at stations within 100 miles of the Zipcode. Can also use latlon instead as\n    # results = act.discovery.get_airnow_forecast(token, '2022-05-01', distance=100,\n    #                                             latlon=[41.958, -88.12])\n    # If the username and token are not set, use the existing sample file\n    results = act.discovery.get_airnow_forecast(token, '2022-05-01', zipcode=74630, distance=100)\n\n    # The results show a dataset with air quality information from Oklahoma City\n    # The data is not indexed by time and just a rudimentary xarray object from\n    # converted from a pandas DataFrame.  Note that the AirNow API labels the data\n    # returned as AQI.\n    print(results)\n\n    # This call gives the daily average for Ozone, PM2.5 and PM10\n    results = act.discovery.get_airnow_obs(token, date='2022-05-01', zipcode=74630, distance=100)\n    print(results)\n\n    # This call will get all the station data for a time period within\n    # the bounding box provided.  This will return the object with time\n    # as a coordinate and can be used with ACT Plotting to plot after\n    # squeezing the dimensions.  It can be a 2D time series\n    lat_lon = '-98.172,35.879,-96.76,37.069'\n    results = act.discovery.get_airnow_bounded_obs(\n        token, '2022-05-01T00', '2022-05-01T12', lat_lon, 'OZONE,PM25', data_type='B'\n    )\n    # Reduce to 1D timeseries\n    results = results.squeeze(dim='sites', drop=False)\n    print(results)\n\n    # Plot out data but note that Ozone was not return in the results\n    display = act.plotting.TimeSeriesDisplay(results)\n    display.plot('PM2.5', label='PM2.5')\n    display.plot('AQI', label='AQI')\n    plt.legend()\n    plt.show()"
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