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

.. only:: html

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

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

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

.. _sphx_glr_gallery_scalar_data_aurora_forecast.py:


Plotting the Aurora Forecast from NOAA on Orthographic Polar Projection
-----------------------------------------------------------------------

The National Oceanic and Atmospheric Administration (NOAA) monitors the
solar wind conditions using the ACE spacecraft orbiting close to the L1
Lagrangian point of the Sun-Earth system. This data is fed into the
OVATION-Prime model to forecast the probability of visible aurora at
various locations on Earth. Every five minutes a new forecast is
published for the coming 30 minutes. The data is provided as a
360 by 181 grid of probabilities in percent of visible aurora. The
data spaced equally in degrees from 0 to 359 and -90 to 90.

.. GENERATED FROM PYTHON SOURCE LINES 15-118



.. image-sg:: /gallery/scalar_data/images/sphx_glr_aurora_forecast_001.png
   :alt: aurora forecast
   :srcset: /gallery/scalar_data/images/sphx_glr_aurora_forecast_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    from datetime import datetime
    import json
    from urllib.request import urlopen

    from matplotlib.colors import LinearSegmentedColormap
    import matplotlib.pyplot as plt
    import numpy as np

    import cartopy.crs as ccrs
    from cartopy.feature.nightshade import Nightshade


    def aurora_forecast():
        """
        Get the latest Aurora Forecast from https://www.swpc.noaa.gov.

        Returns
        -------
        img : numpy array
            The pixels of the image in a numpy array.
        img_proj : cartopy CRS
            The rectangular coordinate system of the image.
        img_extent : tuple of floats
            The extent of the image ``(x0, y0, x1, y1)`` referenced in
            the ``img_proj`` coordinate system.
        origin : str
            The origin of the image to be passed through to matplotlib's imshow.
        dt : datetime
            Time of forecast validity.

        """

        # GitHub gist to download the example data from
        url = ('https://gist.githubusercontent.com/lgolston/594c030876c0614d3'
               '6d13d03e4f115b6/raw/342ff751419204594180e88d69b3986dbd4fea4a/'
               'ovation_aurora_latest.json')
        # To plot the current forecast instead, uncomment the following line
        # url = 'https://services.swpc.noaa.gov/json/ovation_aurora_latest.json'

        # load data (JSON format)
        response = urlopen(url)
        aurora = json.loads(response.read().decode('utf-8'))
        # parse timestamp
        dt = datetime.strptime(aurora['Forecast Time'], '%Y-%m-%dT%H:%M:%SZ')
        # convert lists of [lon, lat, value] to 2D array of probability values
        aurora_data = np.array(aurora['coordinates'])
        img = np.reshape(aurora_data[:, 2], (181, 360), order='F')

        img_proj = ccrs.PlateCarree()
        img_extent = (0, 359, -90, 90)
        return img, img_proj, img_extent, 'lower', dt


    def aurora_cmap():
        """Return a colormap with aurora like colors"""
        stops = {'red': [(0.00, 0.1725, 0.1725),
                         (0.50, 0.1725, 0.1725),
                         (1.00, 0.8353, 0.8353)],

                 'green': [(0.00, 0.9294, 0.9294),
                           (0.50, 0.9294, 0.9294),
                           (1.00, 0.8235, 0.8235)],

                 'blue': [(0.00, 0.3843, 0.3843),
                          (0.50, 0.3843, 0.3843),
                          (1.00, 0.6549, 0.6549)],

                 'alpha': [(0.00, 0.0, 0.0),
                           (0.50, 1.0, 1.0),
                           (1.00, 1.0, 1.0)]}

        return LinearSegmentedColormap('aurora', stops)


    def main():
        fig = plt.figure(figsize=[10, 5])

        # We choose to plot in an Orthographic projection as it looks natural
        # and the distortion is relatively small around the poles where
        # the aurora is most likely.

        # ax1 for Northern Hemisphere
        ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.Orthographic(0, 90))

        # ax2 for Southern Hemisphere
        ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.Orthographic(180, -90))

        img, crs, extent, origin, dt = aurora_forecast()

        for ax in [ax1, ax2]:
            ax.coastlines(zorder=3)
            ax.stock_img()
            ax.gridlines()
            ax.add_feature(Nightshade(dt))
            ax.imshow(img, vmin=0, vmax=100, transform=crs,
                      extent=extent, origin=origin, zorder=2,
                      cmap=aurora_cmap())

        plt.show()


    if __name__ == '__main__':
        main()


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

   **Total running time of the script:** (0 minutes 4.710 seconds)


.. _sphx_glr_download_gallery_scalar_data_aurora_forecast.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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