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

.. only:: html

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

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

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

.. _sphx_glr_examples_xradar_plot_dealias_xradar.py:


=================================================
Dealias Radial Velocities Using Xradar and Py-ART
=================================================

An example which uses xradar and Py-ART to dealias radial velocities.

.. GENERATED FROM PYTHON SOURCE LINES 9-38

.. code-block:: Python


    # Author: Max Grover (mgrover@anl.gov)
    # License: BSD 3 clause


    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt
    import xradar as xd

    import pyart
    from pyart.testing import get_test_data

    # Locate the test data and read in using xradar
    filename = get_test_data("swx_20120520_0641.nc")
    tree = xd.io.open_cfradial1_datatree(filename)

    # Give the tree Py-ART radar methods
    radar = pyart.xradar.Xradar(tree)

    # Determine the nyquist velocity using the maximum radial velocity from the first sweep
    nyq = radar["sweep_0"]["mean_doppler_velocity"].max().values

    # Set the nyquist to what we captured above
    # Calculate the velocity texture
    vel_texture = pyart.retrieve.calculate_velocity_texture(
        radar, vel_field="mean_doppler_velocity", nyq=nyq
    )
    radar.add_field("velocity_texture", vel_texture, replace_existing=True)








.. GENERATED FROM PYTHON SOURCE LINES 39-41

**Visualize our velocity texture field**
Let's use the RadarMapDisplay to visualize the texture field

.. GENERATED FROM PYTHON SOURCE LINES 41-55

.. code-block:: Python


    fig = plt.figure(figsize=[8, 8])
    display = pyart.graph.RadarMapDisplay(radar)
    display.plot_ppi_map(
        "velocity_texture",
        sweep=2,
        resolution="50m",
        vmin=0,
        vmax=10,
        projection=ccrs.PlateCarree(),
        cmap="pyart_balance",
    )
    plt.show()




.. image-sg:: /examples/xradar/images/sphx_glr_plot_dealias_xradar_001.png
   :alt: xsapr-sg 0.5 Deg. 2011-05-20T06:42:42Z  Texture of radial velocity of scatters away from instrument
   :srcset: /examples/xradar/images/sphx_glr_plot_dealias_xradar_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 56-59

**Determine a Velocity Texture Threshold Value**

We can use the xradar/xarray plotting functionality here

.. GENERATED FROM PYTHON SOURCE LINES 59-62

.. code-block:: Python

    radar["sweep_0"]["velocity_texture"].plot.hist()
    plt.show()




.. image-sg:: /examples/xradar/images/sphx_glr_plot_dealias_xradar_002.png
   :alt: latitude = 36.49 [degrees_north], longitude = -...
   :srcset: /examples/xradar/images/sphx_glr_plot_dealias_xradar_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 63-67

**Apply a Gatefilter Mask**

We now apply this threshold, along with a reflectivity threshold,
and make use of the region-based dealiasing algorithm

.. GENERATED FROM PYTHON SOURCE LINES 67-84

.. code-block:: Python


    # Configure the gatefilter
    gatefilter = pyart.filters.GateFilter(radar)
    gatefilter.exclude_above("velocity_texture", 4)
    gatefilter.exclude_below("corrected_reflectivity_horizontal", 0)

    # At this point, we can simply used dealias_region_based to dealias the velocities
    # and then add the new field to the radar.
    velocity_dealiased = pyart.correct.dealias_region_based(
        radar,
        vel_field="mean_doppler_velocity",
        nyquist_vel=nyq,
        centered=True,
        gatefilter=gatefilter,
    )
    radar.add_field("corrected_velocity", velocity_dealiased, replace_existing=True)








.. GENERATED FROM PYTHON SOURCE LINES 85-88

**Visualize the Cleaned Radial Velocities**

We can visualize the uncorrected and corrected radial velocity fields

.. GENERATED FROM PYTHON SOURCE LINES 88-109

.. code-block:: Python

    fig = plt.figure(figsize=(14, 5))
    display = pyart.graph.RadarMapDisplay(radar)
    ax1 = plt.subplot(121)
    display.plot_ppi(
        "mean_doppler_velocity",
        cmap="twilight_shifted",
        vmin=-40,
        vmax=40,
        colorbar_label="Uncorrected Radial Velocity (m/s)",
        ax=ax1,
    )
    ax2 = plt.subplot(122)
    display.plot_ppi(
        "corrected_velocity",
        cmap="twilight_shifted",
        vmin=-40,
        vmax=40,
        colorbar_label="Corrected Radial Velocity (m/s)",
        ax=ax2,
    )
    plt.show()



.. image-sg:: /examples/xradar/images/sphx_glr_plot_dealias_xradar_003.png
   :alt: xsapr-sg 0.5 Deg. 2011-05-20T06:42:11Z  Radial velocity of scatterers away from instrument, xsapr-sg 0.5 Deg. 2011-05-20T06:42:11Z  Corrected radial velocity of scatterers away from instrument
   :srcset: /examples/xradar/images/sphx_glr_plot_dealias_xradar_003.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 10.379 seconds)


.. _sphx_glr_download_examples_xradar_plot_dealias_xradar.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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