Visualizations with Py-ART

First we'll import needed packages



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import pyart
from matplotlib import pyplot as plt
import numpy as np
import os
import s3fs
from datetime import datetime as dt

%matplotlib inline
print(pyart.__version__)

import warnings
warnings.simplefilter("ignore", category=DeprecationWarning)
#warnings.simplefilter('ignore')



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def get_latest_file(radar_id, bucket='noaa-nexrad-level2', engine='s3fs'):
    """Return latest NEXRAD data file name."""
    s3conn = s3fs.S3FileSystem(anon=True)
    latest_year = os.path.join(
        bucket, os.path.basename(s3conn.ls(bucket)[-1]))
    latest_month = os.path.join(
        latest_year, os.path.basename(s3conn.ls(latest_year)[-1]))
    latest_day = os.path.join(
        latest_month, os.path.basename(s3conn.ls(latest_month)[-1]))
    return s3conn.ls(os.path.join(latest_day, radar_id))[-1]

Py-ART Colormaps

Retrieve the names of colormaps and the colormap list dictionary.

The colormaps are registered with matplotlib and can be accessed by inserting 'pyart_' in front of any name.



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cm_names = pyart.graph.cm._cmapnames
cms = pyart.graph.cm.cmap_d



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nrows = len(cm_names)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))

# Create a figure and axes instance
fig, axes = plt.subplots(nrows=nrows, figsize=(5,10))
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
axes[0].set_title('Py-Art Colormaps', fontsize=14)

# Loop through the possibilities
for nn, pymap in enumerate(cm_names):
    axes[nn].imshow(gradient, aspect='auto', cmap=cms[pymap])
    pos = list(axes[nn].get_position().bounds)
    x_text = pos[0] - 0.01
    y_text = pos[1] + pos[3]/2.
    fig.text(x_text, y_text, pymap, va='center', ha='right', fontsize=8)

# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axes:
    ax.set_axis_off()

The RadarDisplay

This is the most commonly used class designed for surface-based scanning radar



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### Plot a NEXRAD file



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nexf = "data/KILN20140429_231254_V06"
nexr = pyart.io.read(nexf)
nexd = pyart.graph.RadarDisplay(nexr)



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nexr.fields.keys()



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fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(16, 12))
nexd.plot('reflectivity', sweep=1, cmap='pyart_NWSRef', vmin=0., vmax=55., mask_outside=False, ax=ax[0, 0])
nexd.plot_range_rings([50, 100], ax=ax[0, 0])
nexd.set_limits((-150., 150.), (-150., 150.), ax=ax[0, 0])

nexd.plot('velocity', sweep=1, cmap='pyart_NWSVel', vmin=-30, vmax=30., mask_outside=False, ax=ax[0, 1])
nexd.plot_range_rings([50, 100], ax=ax[0, 1])
nexd.set_limits((-150., 150.), (-150., 150.), ax=ax[0, 1])

nexd.plot('cross_correlation_ratio', sweep=0, cmap='pyart_BrBu12', vmin=0.85, vmax=1., mask_outside=False, ax=ax[1, 0])
nexd.plot_range_rings([50, 100], ax=ax[0, 1])
nexd.set_limits((-150., 150.), (-150., 150.), ax=ax[0, 1])

nexd.plot('differential_reflectivity', sweep=0, cmap='pyart_BuDOr12', vmin=-2, vmax=2., mask_outside=False, ax=ax[1, 1])
nexd.plot_range_rings([50, 100], ax=ax[1, 1])
nexd.set_limits((-150., 150.), (-150., 150.), ax=ax[1, 1])

There are many keyword values we can employe to refine the plot

Keywords exist for title, labels, colorbar, along with others. In addition, there are many methods that can be employed. For example, pull out a constructed RHI at a given azimuth.



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nexd.plot_azimuth_to_rhi('reflectivity', 305., cmap='pyart_NWSRef', vmin=0., vmax=55.)
nexd.set_limits((0., 150.), (0., 15.))

Py-ART RHI

Not only can we construct an RHI from a PPI volume, but RHI scans may be plotted as well.



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rhif = "data/noxp_rhi_140610232635.RAWHJFH"
rhir = pyart.io.read(rhif)
rhid = pyart.graph.RadarDisplay(rhir)



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rhid.plot_rhi('reflectivity', 0, vmin=-5.0, vmax=70,)

Py-ART RadarMapDisplay or RadarMapDisplayCartopy

This converts the x-y coordinates to latitude and longitude overplotting on a map



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nexmap = pyart.graph.RadarMapDisplayCartopy(nexr)



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fig, ax = plt.subplots(1, 1, figsize=(7, 7))
nexmap.plot_ppi_map('reflectivity', sweep=1, vmin=0., vmax=55., ax=ax)
nexmap.ax.set_extent([-87., -82., 37., 42.])

Use what you have learned!

Using all that you have learned, make a two panel plot of reflectivity and doppler velocity using the data file from an RHI of NOXP data/noxp_rhi_140610232635.RAWHJFH. Use Cartopy to overlay the plots on a map of Austrailia and play around with differing colormaps and axes limits!



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Solution

The following cell loads a solution to the above problem.



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# %load solution.py



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