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ENH: Add column example to docs #1263

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6 changes: 6 additions & 0 deletions examples/retrieve/README.txt
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.. _retrieve_examples:

Retrieval Examples
------------------

Retrievals from various radars, such as additional fields or subsets of the data.
67 changes: 67 additions & 0 deletions examples/retrieve/plot_column_subset.py
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"""
====================================
Extract a radar column above a point
====================================

Given a radar and a point, extract the column of radar data values above
a point
"""

# Author: Maxwell Grover ([email protected])
# License: BSD 3 clause

import pyart
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from pyart.testing import get_test_data

# Read in some test data
filename = get_test_data('swx_20120520_0641.nc')
radar = pyart.io.read(filename)

######################################
# **Plot the first sweep and our desired point**
#
# Let's visualize our radar data from a single sweep, and plot
# the location of our desired point on a map.
# This will provide some context as to where we are extracting our
# column of values.

site_lon = -97.73 # longitude in degrees
site_lat = 36.41 # latitdue in degrees

# Setup the RadarMapDisplay and add our projection
display = pyart.graph.RadarMapDisplay(radar)
ax = plt.subplot(111, projection=ccrs.PlateCarree())

# Visualize the reflectivity field, using the lowest sweep with
# latitude and longitude lines
display.plot_ppi_map('reflectivity_horizontal',
0,
ax=ax,
vmin=-32,
vmax=64.,
lon_lines=np.arange(-98, -97, .2),
lat_lines=np.arange(36, 37, .2))

# Plot our site location on top of the radar image
ax.scatter(site_lon,
site_lat,
color='black');

######################################
# Now that we have our point defined, and our radar object, we can use the following
# utility function in Py-ART to subset a column
ds = pyart.util.columnsect.get_field_location(radar, site_lat, site_lon)

######################################
# This function returns an xarray dataset, with all of our data fields!
print(ds)

######################################
# **Visualize the Reflectivity Values in the Column**
#
# Let's visualize the reflectivity values in the column
# above our point, which is stored in our new dataset
ds.corrected_reflectivity_horizontal.plot(y='height');