==================================================== An example which creates a plot containing multiple moments taken from a NEXRAD Archive file. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data filename = get_test_data('KATX20130717_195021_V06') radar = pyart.io.read_nexrad_archive(filename) display = pyart.graph.RadarDisplay(radar) fig = plt.figure(figsize=(10, 10)) ax = fig.add_subplot(221) display.plot('velocity', 1, ax=ax, title='Doppler Velocity', colorbar_label='', axislabels=('', 'North South distance from radar (km)')) display.set_limits((-300, 300), (-300, 300), ax=ax) ax = fig.add_subplot(222) display.plot('differential_reflectivity',
""" ================================= Create a RHI plot from a MDV file ================================= An example which creates a RHI plot of a MDV file using a RadarDisplay object. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data filename = get_test_data('110041.mdv') # create the plot using RadarDisplay radar = pyart.io.read_mdv(filename) display = pyart.graph.RadarDisplay(radar) fig = plt.figure(figsize=[5, 5]) ax = fig.add_subplot(111) display.plot('reflectivity', 0, vmin=-16, vmax=64.0) plt.show()
Map the reflectivity field of a single radar from Antenna coordinates to a Cartesian grid. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data # read in the data file = get_test_data('110635.mdv') radar = pyart.io.read_mdv(file) # mask out last 10 gates of each ray, this removes the "ring" around the radar. radar.fields['reflectivity']['data'][:, -10:] = np.ma.masked # exclude masked gates from the gridding gatefilter = pyart.filters.GateFilter(radar) gatefilter.exclude_transition() gatefilter.exclude_masked('reflectivity') # perform Cartesian mapping, limit to the reflectivity field. grid = pyart.map.grid_from_radars( (radar,), gatefilters=(gatefilter, ), grid_shape=(1, 241, 241), grid_limits=((2000, 2000), (-123000.0, 123000.0), (-123000.0, 123000.0)),
An example which creates an RHI plot of reflectivity using a RadarDisplay object and adding differnential Reflectivity contours from the same MDV file. """ print(__doc__) # Author: Cory Weber ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data import numpy as np import scipy.ndimage as ndimage filename = get_test_data('220629.mdv') # create the plot using RadarDisplay sweep = 0 # read file radar = pyart.io.read_mdv(filename) display = pyart.graph.RadarDisplay(radar) fig = plt.figure(figsize=[20, 5]) ax = fig.add_subplot(111) # plot reflectivity # alpha=0.25 sets the transparency of the pcolormesh to 75% transparent against # the default white. matplolib overlaps the edges of the pcolormesh and creates # a visable border around the edge, even with the default of edgecolor set to # 'none' the transparancy is effected. the flowing paramters are designed to # compensate for that:
In this example doppler velocities are dealiased using the Univ. of Washington FourDD algorithm implemented in Py-ART. Sounding data is used for the initial condition of the dealiasing. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import netCDF4 import pyart from pyart.testing import get_test_data radar_file = get_test_data('095636.mdv') sonde_file = get_test_data('sgpinterpolatedsondeC1.c1.20110510.000000.cdf') # read in the data radar = pyart.io.read_mdv(radar_file) # read in sonde data dt, profile = pyart.io.read_arm_sonde_vap(sonde_file, radar=radar) # create a gate filter which specifies gates to exclude from dealiasing gatefilter = pyart.filters.GateFilter(radar) gatefilter.exclude_transition() gatefilter.exclude_invalid('velocity') gatefilter.exclude_invalid('reflectivity') gatefilter.exclude_outside('reflectivity', 0, 80)
"""
print(__doc__)
#Author: Jason Hemedinger
#License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import pyart
from pyart.testing import get_test_data
# Read in the file, create a RadarMapDisplay object
filename = get_test_data('nsaxsaprppiC1.a1.20140201.184802.nc')
radar = pyart.io.read(filename)
display = pyart.graph.RadarMapDisplay(radar)
# Setting projection and ploting the second tilt
projection = ccrs.LambertConformal(
central_latitude=radar.latitude['data'][0],
central_longitude=radar.longitude['data'][0])
fig = plt.figure(figsize=(6, 6))
display.plot_ppi_map('reflectivity_horizontal',
1,
vmin=-20,
vmax=20,
min_lon=-157.1,
max_lon=-156,
An example which creates a two panel RHI plot of a Sigmet file. The fields included in the two panels are reflectivity and doppler velocity. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data import netCDF4 # read the data and create the display object filename = get_test_data('XSW110520113537.RAW7HHL') radar = pyart.io.read_rsl(filename) display = pyart.graph.RadarDisplay(radar) # fields to plot and ranges fields_to_plot = ['reflectivity', 'velocity'] ranges = [(-32, 64), (-17.0, 17.0)] # plot the data nplots = len(fields_to_plot) plt.figure(figsize=[5 * nplots, 4]) # plot each field for plot_num in range(nplots): field = fields_to_plot[plot_num] vmin, vmax = ranges[plot_num]
An example which creates an RHI plot of velocity using a RadarDisplay object and adding Reflectivity contours from the same MDV file. """ print(__doc__) # Author: Cory Weber ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data import numpy as np import scipy.ndimage as spyi filename = get_test_data('034142.mdv') # create the plot using RadarDisplay sweep = 2 # read file radar = pyart.io.read_mdv(filename) display = pyart.graph.RadarDisplay(radar) fig = plt.figure(figsize=[20, 5]) ax = fig.add_subplot(111) # plot velocity # cmap is the color ramp being used in this case blue to red no 18 # https://github.com/ARM-DOE/pyart/blob/master/pyart/graph/cm.py # for more information display.plot('velocity',
"""
print(__doc__)
# Author: Jason Hemedinger
# License: BSD 3 clause
import cartopy.crs as ccrs
import numpy as np
import matplotlib.pyplot as plt
import pyart
from pyart.testing import get_test_data
# Read in the gridded file, create GridMapDisplay object
filename = get_test_data('20110520100000_nexrad_grid.nc')
radar = pyart.io.read_grid(filename)
display = pyart.graph.GridMapDisplay(radar)
# Setting projection, figure size, and panel sizes.
projection = ccrs.PlateCarree()
fig = plt.figure(figsize=[15, 7])
map_panel_axes = [0.05, 0.05, .4, .80]
x_cut_panel_axes = [0.55, 0.10, .4, .25]
y_cut_panel_axes = [0.55, 0.50, .4, .25]
# Set parameters.
level = 1
vmin = -8
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import warnings
import pyart
from pyart.testing import get_test_data
warnings.filterwarnings("ignore")
######################################
# **Read in the Data**
#
# For this example, we use two XSAPR radars from our test data.
# read in the data from both XSAPR radars
xsapr_sw_file = get_test_data('swx_20120520_0641.nc')
xsapr_se_file = get_test_data('sex_20120520_0641.nc')
radar_sw = pyart.io.read_cfradial(xsapr_sw_file)
radar_se = pyart.io.read_cfradial(xsapr_se_file)
######################################
# **Filter and Configure the GateMapper**
#
# We are interested in mapping the southwestern radar to the
# southeastern radar. Before running our gatemapper, we add a
# filter for only positive reflectivity values.
# We also need to set a distance (meters) and time (seconds)
# between the source and destination gate allowed for an
# adequate match), using the distance_tolerance/time_tolerance variables.
gatefilter = pyart.filters.GateFilter(radar_sw)
""" ==================================== Create a PPI plot from a Sigmet file ==================================== An example which creates a PPI plot of a Sigmet file. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data filename = get_test_data('XSW110520105408.RAW7HHF') # create the plot using RadarDisplay (recommended method) radar = pyart.io.read_rsl(filename) display = pyart.graph.RadarDisplay(radar) fig = plt.figure() ax = fig.add_subplot(111) display.plot('reflectivity', 0, vmin=-32, vmax=64.) display.plot_range_rings([10, 20, 30, 40]) display.plot_cross_hair(5.) plt.show()
An example which creates a plot containing the first collected scan from a NEXRAD file. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import pyart from pyart.testing import get_test_data # open the file, create the displays and figure filename = get_test_data('Level2_KATX_20130717_1950.ar2v') radar = pyart.io.read_nexrad_archive(filename) display = pyart.graph.RadarDisplay(radar) fig = plt.figure(figsize=(6, 5)) # plot super resolution reflectivity ax = fig.add_subplot(111) display.plot('reflectivity', 0, title='NEXRAD Reflectivity', vmin=-32, vmax=64, colorbar_label='', ax=ax) display.plot_range_ring(radar.range['data'][-1] / 1000., ax=ax) display.set_limits(xlim=(-500, 500), ylim=(-500, 500), ax=ax)
An example which creates a multiple panel RHI plot of a CF/Radial file using a RadarDisplay object. """ print(__doc__) # Author: Jonathan J. Helmus ([email protected]) # License: BSD 3 clause import matplotlib.pyplot as plt import netCDF4 import pyart from pyart.testing import get_test_data filename = get_test_data('sgpxsaprrhicmacI5.c0.20110524.015604_NC4.nc') # create the plot using RadarDisplay radar = pyart.io.read_cfradial(filename) radar.metadata['instrument_name'] = 'XSARP' display = pyart.graph.RadarDisplay(radar) fig = plt.figure(figsize=[12, 17]) fig.subplots_adjust(hspace=0.4) xlabel = 'Distance from radar (km)' ylabel = 'Height agl (km)' colorbar_label = 'Hz. Eq. Refl. Fac. (dBZ)' nplots = radar.nsweeps for snum in radar.sweep_number['data']: