.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/discovery/plot_airnow.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_discovery_plot_airnow.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_discovery_plot_airnow.py:


Airnow Data
-----------

This example shows the different ways to pull
air quality information from EPA's AirNow API for
a station near to SGP

.. GENERATED FROM PYTHON SOURCE LINES 10-56



.. image-sg:: /auto_examples/discovery/images/sphx_glr_plot_airnow_001.png
   :alt: act_datastream AQI on 20220501
   :srcset: /auto_examples/discovery/images/sphx_glr_plot_airnow_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    <xarray.Dataset> Size: 0B
    Dimensions:         (index: 0)
    Coordinates:
      * index           (index) int64 0B 
    Data variables:
        DateIssue       (index) object 0B 
        DateForecast    (index) object 0B 
        ReportingArea   (index) object 0B 
        StateCode       (index) object 0B 
        Latitude        (index) object 0B 
        Longitude       (index) object 0B 
        ParameterName   (index) object 0B 
        AQI             (index) object 0B 
        CategoryNumber  (index) object 0B 
        CategoryName    (index) object 0B 
        ActionDay       (index) object 0B 
        Discussion      (index) object 0B 
    <xarray.Dataset> Size: 0B
    Dimensions:         (index: 0)
    Coordinates:
      * index           (index) int64 0B 
    Data variables:
        DateObserved    (index) object 0B 
        HourObserved    (index) object 0B 
        LocalTimeZone   (index) object 0B 
        ReportingArea   (index) object 0B 
        StateCode       (index) object 0B 
        Latitude        (index) object 0B 
        Longitude       (index) object 0B 
        ParameterName   (index) object 0B 
        AQI             (index) object 0B 
        CategoryNumber  (index) object 0B 
        CategoryName    (index) object 0B 
    <xarray.Dataset> Size: 584B
    Dimensions:            (time: 13)
    Coordinates:
      * time               (time) datetime64[us] 104B 2022-05-01 ... 2022-05-01T1...
        sites              <U10 40B 'Ponca City'
    Data variables:
        latitude           float64 8B 36.7
        longitude          float64 8B -97.08
        aqs_id             int64 8B 400710604
        PM2.5              (time) float64 104B 4.3 5.3 5.9 6.9 ... 7.7 7.4 7.6 7.8
        AQI                (time) float64 104B 24.0 29.0 33.0 ... 41.0 42.0 43.0
        category           (time) float64 104B 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
        raw_concentration  (time) float64 104B 5.1 6.3 6.5 7.9 ... 7.1 7.1 7.8 8.1
    /home/runner/work/ACT/ACT/act/plotting/plot.py:81: UserWarning: Could not discern datastreamname and dict or tuple were not provided. Using defaultname of act_datastream!
      warnings.warn(






|

.. code-block:: Python


    import os

    import matplotlib.pyplot as plt

    import act

    # You need an account and token from https://docs.airnowapi.org/ first
    token = os.getenv('AIRNOW_API')

    if token is not None and len(token) > 0:
        # This first example will get the forcasted values for the date passed
        # at stations within 100 miles of the Zipcode. Can also use latlon instead as
        # results = act.discovery.get_airnow_forecast(token, '2022-05-01', distance=100,
        #                                             latlon=[41.958, -88.12])
        # If the username and token are not set, use the existing sample file
        results = act.discovery.get_airnow_forecast(token, '2022-05-01', zipcode=74630, distance=100)

        # The results show a dataset with air quality information from Oklahoma City
        # The data is not indexed by time and just a rudimentary xarray object from
        # converted from a pandas DataFrame.  Note that the AirNow API labels the data
        # returned as AQI.
        print(results)

        # This call gives the daily average for Ozone, PM2.5 and PM10
        results = act.discovery.get_airnow_obs(token, date='2022-05-01', zipcode=74630, distance=100)
        print(results)

        # This call will get all the station data for a time period within
        # the bounding box provided.  This will return the object with time
        # as a coordinate and can be used with ACT Plotting to plot after
        # squeezing the dimensions.  It can be a 2D time series
        lat_lon = '-98.172,35.879,-96.76,37.069'
        results = act.discovery.get_airnow_bounded_obs(
            token, '2022-05-01T00', '2022-05-01T12', lat_lon, 'OZONE,PM25', data_type='B'
        )
        # Reduce to 1D timeseries
        results = results.squeeze(dim='sites', drop=False)
        print(results)

        # Plot out data but note that Ozone was not return in the results
        display = act.plotting.TimeSeriesDisplay(results)
        display.plot('PM2.5', label='PM2.5')
        display.plot('AQI', label='AQI')
        plt.legend()
        plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.594 seconds)


.. _sphx_glr_download_auto_examples_discovery_plot_airnow.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_airnow.ipynb <plot_airnow.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_airnow.py <plot_airnow.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_airnow.zip <plot_airnow.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_