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

    Click :ref:`here <sphx_glr_download_examples_Nexrad_S3_Demo.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_Nexrad_S3_Demo.py:


Plotting AWS-hosted NEXRAD Level 2 Data
=======================================================

Access NEXRAD radar data via Amazon Web Services and plot with MetPy

Accessing data remotely is a powerful tool for big data, such as NEXRAD radar data.
By accessing it in the cloud, you can save time and space from downloading the data locally.


.. code-block:: default


    import boto3
    import botocore
    from botocore.client import Config
    import matplotlib.pyplot as plt
    from metpy.io import Level2File
    from metpy.plots import add_timestamp, ctables
    from mpl_toolkits.axes_grid1 import make_axes_locatable
    import numpy as np








Access the data in the AWS cloud. In this example, we're plotting data
from the Evansville, IN radar, which had convection within its
domain on 06/26/2019.



.. code-block:: default


    s3 = boto3.resource('s3', config=Config(signature_version=botocore.UNSIGNED,
                                            user_agent_extra='Resource'))
    bucket = s3.Bucket('noaa-nexrad-level2')
    for obj in bucket.objects.filter(Prefix='2019/06/26/KVWX/KVWX20190626_221105_V06'):
        print(obj.key)

        # Use MetPy to read the file
        f = Level2File(obj.get()['Body'])






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

 Out:

 .. code-block:: none

    2019/06/26/KVWX/KVWX20190626_221105_V06




Subset Data
-----------

With the file comes a lot of data, including multiple elevations and products.
In the next block, we'll pull out the specific data we want to plot.



.. code-block:: default


    sweep = 0
    # First item in ray is header, which has azimuth angle
    az = np.array([ray[0].az_angle for ray in f.sweeps[sweep]])

    ref_hdr = f.sweeps[sweep][0][4][b'REF'][0]
    ref_range = np.arange(ref_hdr.num_gates) * ref_hdr.gate_width + ref_hdr.first_gate
    ref = np.array([ray[4][b'REF'][1] for ray in f.sweeps[sweep]])

    rho_hdr = f.sweeps[sweep][0][4][b'RHO'][0]
    rho_range = (np.arange(rho_hdr.num_gates + 1) - 0.5) * rho_hdr.gate_width + rho_hdr.first_gate
    rho = np.array([ray[4][b'RHO'][1] for ray in f.sweeps[sweep]])

    phi_hdr = f.sweeps[sweep][0][4][b'PHI'][0]
    phi_range = (np.arange(phi_hdr.num_gates + 1) - 0.5) * phi_hdr.gate_width + phi_hdr.first_gate
    phi = np.array([ray[4][b'PHI'][1] for ray in f.sweeps[sweep]])

    zdr_hdr = f.sweeps[sweep][0][4][b'ZDR'][0]
    zdr_range = (np.arange(zdr_hdr.num_gates + 1) - 0.5) * zdr_hdr.gate_width + zdr_hdr.first_gate
    zdr = np.array([ray[4][b'ZDR'][1] for ray in f.sweeps[sweep]])









Plot the data
-------------

Use MetPy and Matplotlib to plot the data



.. code-block:: default


    # Get the NWS reflectivity colortable from MetPy
    ref_norm, ref_cmap = ctables.registry.get_with_steps('NWSReflectivity', 5, 5)

    # Plot the data!
    fig, axes = plt.subplots(2, 2, figsize=(15, 15))
    for var_data, var_range, colors, lbl, ax in zip((ref, rho, zdr, phi),
                                                    (ref_range, rho_range, zdr_range, phi_range),
                                                    (ref_cmap, 'plasma', 'viridis', 'viridis'),
                                                    ('REF (dBZ)', 'RHO', 'ZDR (dBZ)', 'PHI'),
                                                    axes.flatten()):
        # Turn into an array, then mask
        data = np.ma.array(var_data)
        data[np.isnan(data)] = np.ma.masked

        # Convert az,range to x,y
        xlocs = var_range * np.sin(np.deg2rad(az[:, np.newaxis]))
        ylocs = var_range * np.cos(np.deg2rad(az[:, np.newaxis]))

        # Define norm for reflectivity
        norm = ref_norm if colors == ref_cmap else None

        # Plot the data
        a = ax.pcolormesh(xlocs, ylocs, data, cmap=colors, norm=norm)

        divider = make_axes_locatable(ax)
        cax = divider.append_axes('right', size='5%', pad=0.05)
        fig.colorbar(a, cax=cax, orientation='vertical', label=lbl)

        ax.set_aspect('equal', 'datalim')
        ax.set_xlim(-100, 100)
        ax.set_ylim(-100, 100)
        add_timestamp(ax, f.dt, y=0.02, high_contrast=False)
    plt.suptitle('KVWX Level 2 Data', fontsize=20)
    plt.tight_layout()
    plt.show()



.. image:: /examples/images/sphx_glr_Nexrad_S3_Demo_001.png
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  5.965 seconds)


.. _sphx_glr_download_examples_Nexrad_S3_Demo.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: Nexrad_S3_Demo.py <Nexrad_S3_Demo.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: Nexrad_S3_Demo.ipynb <Nexrad_S3_Demo.ipynb>`


.. only:: html

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

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