def multipanel_V03(inpdir, outdir, stamp, dpi=100, verbose=False):
"""
"""
files = [os.path.join(inpdir, f) for f in sorted(os.listdir(inpdir))
if stamp in f]
if verbose:
print 'Number of files to plot = %i' % len(files)
for f in files:
# Parse input file
radar = read_radx(f)
# Check the number of sweeps are consistent with defined VCP
if radar.nsweeps != VCP_SWEEPS:
continue
if verbose:
print 'Currently plotting file %s' % os.path.basename(f)
# Create figure instance
subs = {'xlim': (-MAX_RANGE, MAX_RANGE),
'ylim': (-MAX_RANGE, MAX_RANGE)}
figs = {'figsize': (66, 24)}
fig, ax = plt.subplots(
nrows=3, ncols=len(SWEEPS), subplot_kw=subs, **figs)
# Iterate over each sweep
for j, sweep in enumerate(SWEEPS):
# (a) Reflectivity
qma = pcolormesh(
radar, 'REF', sweep=sweep, cmap=cmap_refl, norm=norm_refl,
ax=ax[0,j])
# (b) Doppler velocity
qmb = pcolormesh(
radar, 'VEL', sweep=sweep, cmap=cmap_vdop, norm=norm_vdop,
ax=ax[1,j])
# (c) Spectrum width
qmc = pcolormesh(
radar, 'SW', sweep=sweep, cmap=cmap_sw, norm=norm_sw,
ax=ax[2,j])
# Format plot axes
for i, j in np.ndindex(ax.shape):
ax[i,j].xaxis.set_major_locator(MultipleLocator(50))
ax[i,j].xaxis.set_minor_locator(MultipleLocator(10))
ax[i,j].yaxis.set_major_locator(MultipleLocator(50))
ax[i,j].yaxis.set_minor_locator(MultipleLocator(10))
ax[i,j].set_xlabel('Eastward Range from Radar (km)')
ax[i,j].set_ylabel('Northward Range from Radar (km)')
ax[i,j].grid(which='major')
# Color bars
plt.colorbar(mappable=qma, cax=fig.add_axes([0.91, 0.68, 0.008, 0.2]),
ticks=ticks_refl)
plt.colorbar(mappable=qmb, cax=fig.add_axes([0.91, 0.40, 0.008, 0.2]),
ticks=ticks_vdop)
plt.colorbar(mappable=qmc, cax=fig.add_axes([0.91, 0.12, 0.008, 0.2]),
ticks=ticks_sw)
# Save figure
date_stamp = num2date(radar.time['data'][:].min(), radar.time['units'])
filename = '{}.png'.format(date_stamp.strftime('%Y%m%d.%H%M%S'))
fig.savefig(os.path.join(outdir, filename), format='png', dpi=dpi,
bbox_inches='tight')
plt.close(fig)
return
def process_file(filename, outdir, debug=False, verbose=False):
"""
"""
# Read radar data
if USE_RADX:
radar = read_radx(filename)
else:
radar = read(filename, exclude_fields=None)
# Radar VCP check
if CHECK_VCP:
if NSWEEPS is not None and radar.nsweeps != NSWEEPS:
return
if verbose:
print 'Processing file: {}'.format(os.path.basename(filename))
if debug:
print 'Number of sweeps: {}'.format(radar.nsweeps)
if USE_RADX:
# Create file metadata object
meta = FileMetadata(
'nexrad_archive', field_names=None, additional_metadata=None,
file_field_names=False, exclude_fields=None)
# Remove unnecessary fields
for field in REMOVE_FIELDS:
radar.fields.pop(field, None)
# Rename fields to default Py-ART names
for field in radar.fields.keys():
default_field = meta.get_field_name(field)
radar.fields[default_field] = radar.fields.pop(field, None)
# Step 1: Determine radar significant detection
# Since NEXRAD WSR-88D Level II data is already processed to some degree,
# this amounts to essentially removing salt and pepper noise
gf = noise._significant_features(
radar, REFL_FIELD, gatefilter=None, size_bins=SIZE_BINS,
size_limits=SIZE_LIMITS, structure=STRUCTURE, remove_size_field=False,
fill_value=None, size_field=None, debug=debug)
gf = noise.significant_detection(
radar, gatefilter=gf, remove_small_features=False, size_bins=SIZE_BINS,
size_limits=SIZE_LIMITS, fill_holes=FILL_HOLES, dilate=DILATE,
structure=STRUCTURE, iterations=1, rays_wrap_around=False,
min_ncp=None, detect_field=None, debug=debug, verbose=verbose)
# Step 2: Doppler velocity correction
if DEALIAS == 'phase':
vdop_corr = dealias_unwrap_phase(
radar, gatefilter=gf, unwrap_unit='sweep', nyquist_vel=None,
rays_wrap_around=True, keep_original=False, vel_field=None,
corr_vel_field=VDOP_CORR_FIELD)
elif DEALIAS == 'region':
vdop_corr = dealias_region_based(
radar, gatefilter=gf, interval_splits=INTERVAL_SPLITS,
interval_limits=None, skip_between_rays=2, skip_along_ray=2,
centered=True, nyquist_vel=None, rays_wrap_around=True,
keep_original=False, vel_field=None,
corr_vel_field=VDOP_CORR_FIELD)
else:
raise ValueError('Unsupported velocity correction routine')
radar.add_field(VDOP_CORR_FIELD, vdop_corr, replace_existing=True)
# Step 3: Reflectivity correction
# Currently no correction procedures are applied to the reflectivity field
# due to minimal attenuation at S-band
refl_corr = radar.fields[REFL_FIELD].copy()
radar.add_field(REFL_CORR_FIELD, refl_corr, replace_existing=True)
# Step 4: Interpolate missing gates
basic_fixes.interpolate_missing(
radar, fields=FILL_FIELDS, interp_window=FILL_WINDOW,
interp_sample=FILL_SAMPLE, kind='mean', rays_wrap_around=False,
fill_value=None, debug=debug, verbose=verbose)
# Add metadata
_add_metadata(radar, filename)
# ARM file name protocols
date_stamp = datetimes_from_radar(radar).min().strftime('%Y%m%d.%H%M%S')
fname = 'nexradwsr88d{}cmac{}.{}.{}.cdf'.format(QF, FN, DL, date_stamp)
# Write CMAC NetCDF file
write_cfradial(os.path.join(outdir, fname), radar, format=FORMAT,
arm_time_variables=True)
return