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

.. only:: html

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

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

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

.. _sphx_glr_examples_correct_plot_cloud_mask.py:


=====================================
Calculating and Plotting a Cloud Mask
=====================================

This example shows how to correct and plot reflectivity from an ARM
KAZR using a noise floor cloud mask.

.. GENERATED FROM PYTHON SOURCE LINES 10-21

.. code-block:: Python

    print(__doc__)

    # Author: Adam Theisen and Zach Sherman
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np
    from open_radar_data import DATASETS

    import pyart








.. GENERATED FROM PYTHON SOURCE LINES 22-25

**Read and plot raw data**

First let's read and plot our dataset without any mask.

.. GENERATED FROM PYTHON SOURCE LINES 25-36

.. code-block:: Python


    # Fetch and read in the ARM KAZR file.
    filename = DATASETS.fetch("sgpkazrgeC1.a1.20190529.000002.cdf")
    radar = pyart.aux_io.read_kazr(filename)

    # Let's now take a look at reflectivity data prior to any corrections.
    display = pyart.graph.RadarDisplay(radar)
    display.plot("reflectivity_copol")
    display.set_limits(xlim=(0, 55))
    plt.show()




.. image-sg:: /examples/correct/images/sphx_glr_plot_cloud_mask_001.png
   :alt:  2019-05-29T15:00:00Z  Reflectivity, copolar
   :srcset: /examples/correct/images/sphx_glr_plot_cloud_mask_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 37-42

**Calculate cloud mask and plot corrected data**

Now lets apply a mask by using the calc_cloud_mask function
that will use a noise floor calculation from range and more
to calculate the mask.

.. GENERATED FROM PYTHON SOURCE LINES 42-67

.. code-block:: Python


    # First lets correct the data by calculating the mask.
    cloud_mask_radar = pyart.correct.calc_cloud_mask(radar, "reflectivity_copol", "range")

    # In this new radar object we should now have a new cloud mask field.
    print(cloud_mask_radar.fields["cloud_mask_2"])

    # Next we'll create a copy of the reflectivity field so we are not
    # overwriting the original data.
    cloud_mask_radar.add_field_like(
        "reflectivity_copol",
        "reflectivity_cloud_mask",
        cloud_mask_radar.fields["reflectivity_copol"]["data"].copy(),
    )

    # Now let's apply the mask to the copied reflectivity data.
    cloud_mask_radar.fields["reflectivity_cloud_mask"]["data"][
        cloud_mask_radar.fields["cloud_mask_2"]["data"] == 0
    ] = np.nan

    # And now we can plot the masked reflectivity field.
    display = pyart.graph.RadarDisplay(cloud_mask_radar)
    display.plot("reflectivity_cloud_mask")
    display.set_limits(xlim=(0, 55))
    plt.show()



.. image-sg:: /examples/correct/images/sphx_glr_plot_cloud_mask_002.png
   :alt:  2019-05-29T15:00:00Z  Reflectivity, copolar
   :srcset: /examples/correct/images/sphx_glr_plot_cloud_mask_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    {'long_name': 'Cloud mask 2 (2D box)', 'units': '1', 'comment': 'The mask uses a 2D box to filter out noise.', 'flag_values': [0, 1], 'flag_meanings': ['no_cloud', 'cloud'], 'data': masked_array(
      data=[[0, 0, 0, ..., 0, 0, 0],
            [0, 0, 1, ..., 0, 0, 0],
            [0, 1, 1, ..., 0, 0, 0],
            ...,
            [0, 1, 1, ..., 0, 0, 0],
            [0, 0, 1, ..., 0, 0, 0],
            [0, 0, 0, ..., 0, 0, 0]],
      mask=[[False, False, False, ..., False, False, False],
            [False, False, False, ..., False, False, False],
            [False, False, False, ..., False, False, False],
            ...,
            [False, False, False, ..., False, False, False],
            [False, False, False, ..., False, False, False],
            [False, False, False, ..., False, False, False]],
      fill_value=np.int64(999999),
      dtype=int16)}





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

   **Total running time of the script:** (0 minutes 0.685 seconds)


.. _sphx_glr_download_examples_correct_plot_cloud_mask.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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