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

    Click :ref:`here <sphx_glr_download_gallery_aurora_forecast.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_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
1024 by 512 grid of probabilities in percent of visible aurora. The
data spaced equally in degrees from -180 to 180 and -90 to 90.





.. image:: /gallery/images/sphx_glr_aurora_forecast_001.png
    :class: sphx-glr-single-img





.. code-block:: python

    try:
        from urllib2 import urlopen
    except ImportError:
        from urllib.request import urlopen

    from io import StringIO

    import numpy as np
    from datetime import datetime
    import cartopy.crs as ccrs
    from cartopy.feature.nightshade import Nightshade
    import matplotlib.pyplot as plt
    from matplotlib.colors import LinearSegmentedColormap


    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/belteshassar/'
               'c7ea9e02a3e3934a9ddc/raw/aurora-nowcast-map.txt')
        # To plot the current forecast instead, uncomment the following line
        # url = 'https://services.swpc.noaa.gov/text/aurora-nowcast-map.txt'

        response_text = StringIO(urlopen(url).read().decode('utf-8'))
        img = np.loadtxt(response_text)
        # Read forecast date and time
        response_text.seek(0)
        for line in response_text:
            if line.startswith('Product Valid At:', 2):
                dt = datetime.strptime(line[-17:-1], '%Y-%m-%d %H:%M')

        img_proj = ccrs.PlateCarree()
        img_extent = (-180, 180, -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()

**Total running time of the script:** ( 0 minutes  3.649 seconds)


.. _sphx_glr_download_gallery_aurora_forecast.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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