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

.. only:: html

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

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

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

.. _sphx_glr_source_auto_examples_plotting_plot_satellite.py:


Using ACT for Satellite data
----------------------------

Simple example for working with satellite data.
It is recommended that users explore other
satellite specific libraries suchas SatPy.

Author: Adam Theisen

.. GENERATED FROM PYTHON SOURCE LINES 11-54



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /source/auto_examples/plotting/images/sphx_glr_plot_satellite_001.png
         :alt: Cloud Percentage
         :srcset: /source/auto_examples/plotting/images/sphx_glr_plot_satellite_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /source/auto_examples/plotting/images/sphx_glr_plot_satellite_002.png
         :alt: ARM percent_opaque on 20230307
         :srcset: /source/auto_examples/plotting/images/sphx_glr_plot_satellite_002.png
         :class: sphx-glr-multi-img





.. code-block:: Python


    import act
    import matplotlib.pyplot as plt
    import numpy as np
    from arm_test_data import DATASETS

    # Read in VISST Data
    files = DATASETS.fetch('enavisstgridm11minnisX1.c1.20230307.000000.cdf')
    ds = act.io.read_arm_netcdf(files)

    # Plot up the VISST cloud percentage using XSectionDisplay
    display = act.plotting.XSectionDisplay(ds, figsize=(10, 8))
    display.plot_xsection_map(
        'cloud_percentage', x='longitude', y='latitude', isel_kwargs={'time': 0, 'cld_type': 0}
    )
    plt.show()

    # Download ARM TSI Data
    files = DATASETS.fetch('enatsiskycoverC1.b1.20230307.082100.cdf')
    ds_tsi = act.io.read_arm_netcdf(files)
    ds_tsi = ds_tsi.where(ds_tsi.percent_opaque > 0)
    ds_tsi = ds_tsi.resample(time='30min').mean()

    # Set coordinates to extra data for ENA
    ena_lat = 39.091600
    ena_lon = 28.025700

    lat = ds['lat'].values
    lon = ds['lon'].values

    # Find the nearest pixel for the satellite data and extract it
    lat_ind = np.argmin(np.abs(lat - ena_lat))
    lon_ind = np.argmin(np.abs(lon - ena_lon))

    ds_new = ds.isel(lat=lat_ind, lon=lon_ind, cld_type=0)

    # Plot the comparison using TimeSeriesDisplay
    display = act.plotting.TimeSeriesDisplay({'Satellite': ds_new, 'ARM': ds_tsi}, figsize=(15, 8))
    display.plot('cloud_percentage', dsname='Satellite', label='VISST Cloud Percentage')
    display.plot('percent_opaque', dsname='ARM', label='ARM TSI Percent Opaque')
    display.day_night_background(dsname='ARM')
    plt.legend()
    plt.show()


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

   **Total running time of the script:** (0 minutes 3.760 seconds)


.. _sphx_glr_download_source_auto_examples_plotting_plot_satellite.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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