def _loop_over_dict(
json_file, pickle_file, inpdir=None, outdir=None, verbose=False,
debug=False):
"""
"""
# Parse files from JSON
with open(json_file, 'r') as fid:
files = json.load(fid)
if inpdir is not None:
files = [os.path.join(inpdir, f) for f in files]
# Loop over all files
for f in files:
# Read radar data
radar = read(f, exclude_fields=EXCLUDE_FIELDS)
if VCP_SWEEPS is not None and radar.nsweeps != VCP_SWEEPS:
continue
if VCP_RAYS is not None and radar.nrays != VCP_RAYS:
continue
if verbose:
print 'Processing file %s' % os.path.basename(f)
# Determine significant detection of the radar
gatefilter = noise.velocity_coherency(
radar, gatefilter=None, num_bins=BINS_VDOP_COHER,
limits=LIMITS_VDOP_COHER, texture_window=(3, 3),
texture_sample=5, min_sigma=None, max_sigma=None, nyquist=None,
rays_wrap_around=False, remove_salt=REMOVE_SALT,
salt_window=SALT_WINDOW, salt_sample=SALT_SAMPLE, fill_value=None,
verbose=verbose)
gatefilter = noise.spectrum_width_coherency(
radar, gatefilter=gatefilter, num_bins=BINS_SW_COHER,
limits=LIMITS_SW_COHER, texture_window=(3, 3), texture_sample=5,
min_sigma=None, max_sigma=None, rays_wrap_around=False,
remove_salt=REMOVE_SALT, salt_window=SALT_WINDOW,
salt_sample=SALT_SAMPLE, fill_value=None, verbose=True)
gatefilter = noise.significant_detection(
radar, gatefilter=gatefilter, remove_salt=REMOVE_SALT,
salt_window=SALT_WINDOW, salt_sample=SALT_SAMPLE, min_ncp=MIN_NCP,
detect_field=None, verbose=verbose)
# Compute histogram counts for each field
geo.height_histogram_from_radar(
radar, HIST_DICT, gatefilter=gatefilter, min_ncp=MIN_NCP,
min_sweep=MIN_SWEEP, max_sweep=MAX_SWEEP, min_range=MIN_RANGE,
max_range=MAX_RANGE, fill_value=None, ncp_field=NCP_FIELD,
verbose=verbose, debug=debug)
# Parse bin edges and histogram counts
bin_edges = HIST_DICT['bin edges']
counts = HIST_DICT['histogram counts']
# Compute normalized histogram and probability density
# Add these to the histogram dictionary
counts_norm = counts.astype(np.float64) / counts.max()
pdf = counts_norm / np.sum(counts_norm * np.diff(bin_edges))
HIST_DICT['normalized histogram'] = counts_norm
HIST_DICT['probability density'] = pdf
# Include other parameters in the histogram dictionary
HIST_DICT['radar files'] = files
HIST_DICT['min sweep'] = MIN_SWEEP
HIST_DICT['max sweep'] = MAX_SWEEP
HIST_DICT['min range'] = MIN_RANGE
HIST_DICT['max range'] = MAX_RANGE
HIST_DICT['sweeps in VCP'] = VCP_SWEEPS
HIST_DICT['rays in VCP'] = VCP_RAYS
HIST_DICT['min NCP'] = MIN_NCP
# Pickle histogram data
geo._pickle_histograms(
HIST_DICT, pickle_file, outdir=outdir)
return
def _loop_over_dict(json_file, pickle_file, inpdir=None, outdir=None, verbose=False, debug=False):
"""
"""
# Parse files from JSON
with open(json_file, "r") as fid:
files = json.load(fid)
if inpdir is not None:
files = [os.path.join(inpdir, f) for f in files]
# Loop over all files
for f in files:
# Read radar data
radar = read(f, exclude_fields=EXCLUDE_FIELDS)
if VCP_SWEEPS is not None and radar.nsweeps != VCP_SWEEPS:
continue
if VCP_RAYS is not None and radar.nrays != VCP_RAYS:
continue
if verbose:
print "Processing file %s" % os.path.basename(f)
# Determine significant detection of the radar
gatefilter = noise.velocity_coherency(
radar,
gatefilter=None,
num_bins=BINS_VDOP_COHER,
limits=LIMITS_VDOP_COHER,
texture_window=(3, 3),
texture_sample=5,
min_sigma=None,
max_sigma=None,
nyquist=None,
rays_wrap_around=False,
remove_salt=REMOVE_SALT,
salt_window=SALT_WINDOW,
salt_sample=SALT_SAMPLE,
fill_value=None,
verbose=verbose,
)
gatefilter = noise.spectrum_width_coherency(
radar,
gatefilter=gatefilter,
num_bins=BINS_SW_COHER,
limits=LIMITS_SW_COHER,
texture_window=(3, 3),
texture_sample=5,
min_sigma=None,
max_sigma=None,
rays_wrap_around=False,
remove_salt=REMOVE_SALT,
salt_window=SALT_WINDOW,
salt_sample=SALT_SAMPLE,
fill_value=None,
verbose=True,
)
gatefilter = noise.significant_detection(
radar,
gatefilter=gatefilter,
remove_salt=REMOVE_SALT,
salt_window=SALT_WINDOW,
salt_sample=SALT_SAMPLE,
min_ncp=MIN_NCP,
detect_field=None,
verbose=verbose,
)
# Compute histogram counts for each field
geo.height_histogram_from_radar(
radar,
HIST_DICT,
gatefilter=gatefilter,
min_ncp=MIN_NCP,
min_sweep=MIN_SWEEP,
max_sweep=MAX_SWEEP,
min_range=MIN_RANGE,
max_range=MAX_RANGE,
fill_value=None,
ncp_field=NCP_FIELD,
verbose=verbose,
debug=debug,
)
# Parse bin edges and histogram counts
bin_edges = HIST_DICT["bin edges"]
counts = HIST_DICT["histogram counts"]
# Compute normalized histogram and probability density
# Add these to the histogram dictionary
counts_norm = counts.astype(np.float64) / counts.max()
pdf = counts_norm / np.sum(counts_norm * np.diff(bin_edges))
HIST_DICT["normalized histogram"] = counts_norm
HIST_DICT["probability density"] = pdf
# Include other parameters in the histogram dictionary
HIST_DICT["radar files"] = files
HIST_DICT["min sweep"] = MIN_SWEEP
HIST_DICT["max sweep"] = MAX_SWEEP
HIST_DICT["min range"] = MIN_RANGE
HIST_DICT["max range"] = MAX_RANGE
HIST_DICT["sweeps in VCP"] = VCP_SWEEPS
HIST_DICT["rays in VCP"] = VCP_RAYS
HIST_DICT["min NCP"] = MIN_NCP
# Pickle histogram data
geo._pickle_histograms(HIST_DICT, pickle_file, outdir=outdir)
return
def process_file(filename, outdir, verbose=False):
"""
"""
if verbose:
print 'Processing file: {}'.format(os.path.basename(filename))
# Read radar data
radar = read_sigmet(filename, exclude_fields=EXLUDE_FIELDS)
# Radar significant detection
# Includes Doppler velocity coherency, spectrum width coherency, and
# minimum normalized coherent power
gf = noise.velocity_coherency(
radar, gatefilter=None, num_bins=VDOP_COHER_BINS,
limits=VDOP_COHER_LIMITS, texture_window=TEXTURE_WINDOW,
texture_sample=TEXTURE_SAMPLE, min_sigma=None, max_sigma=None,
rays_wrap_around=False, remove_salt=False, fill_value=None,
vdop_field=VDOP_FIELD, vdop_text_field=None, cohere_field=None,
verbose=verbose)
gf = noise.velocity_phasor_coherency(
radar, gatefilter=gf, num_bins=PHASE_COHER_BINS,
limits=PHASE_COHER_LIMITS, texture_window=TEXTURE_WINDOW,
texture_sample=TEXTURE_SAMPLE, min_sigma=None, max_sigma=None,
rays_wrap_around=False, remove_salt=False, fill_value=None,
vdop_field=VDOP_FIELD, vdop_phase_field=None, phase_text_field=None,
cohere_field=None, verbose=verbose)
gf = noise.spectrum_width_coherency(
radar, gatefilter=gf, num_bins=SW_COHER_BINS,
limits=SW_COHER_LIMITS, texture_window=TEXTURE_WINDOW,
texture_sample=TEXTURE_SAMPLE, min_sigma=None, max_sigma=None,
rays_wrap_around=False, remove_salt=False, fill_value=None,
width_field=SW_FIELD, width_text_field=None, cohere_field=None,
verbose=verbose)
gf = noise.significant_detection(
radar, gatefilter=gf, remove_salt=True, salt_window=SALT_WINDOW,
salt_sample=SALT_SAMPLE, fill_holes=False, dilate=DILATE,
structure=None, min_ncp=MIN_NCP, ncp_field=NCP_FIELD,
detect_field=None, verbose=verbose)
# Compute radar texture fields
texture_fields.add_textures(
radar, fields=TEXTURE_FIELDS, gatefilter=None,
texture_window=TEXTURE_WINDOW, texture_sample=TEXTURE_SAMPLE,
min_sweep=None, max_sweep=None, min_range=None, max_range=None,
min_ncp=None, rays_wrap_around=False, fill_value=None,
ncp_field=NCP_FIELD)
# Echo classification
bayes.classify(
radar, textures=TEXTURES, moments=MOMENTS, heights=HEIGHTS,
nonprecip_map=None, gatefilter=gf, weights=1.0, class_prob='equal',
min_inputs=3, zero=ZERO, ignore_inputs=IGNORE_INPUTS, use_insects=True,
fill_value=None, cloud_field=CLOUD_FIELD, ground_field=GROUND_FIELD,
insect_field=INSECT_FIELD, ncp_field=NCP_FIELD, verbose=verbose)
# Filter ground clutter gates
gf.exclude_equal(
'radar_echo_classification', 1, exclude_masked=True, op='or')
# Doppler velocity correction
vdop_corr = dealias_region_based(
radar, gatefilter=gf, interval_splits=3, 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=VDOP_FIELD,
corr_vel_field=CORR_VDOP_FIELD)
radar.add_field(CORR_VDOP_FIELD, vdop_corr, replace_existing=False)
# TODO: reflectivity correction
# Parse metadata
radar.metadata = _create_metadata(radar, filename)
# ARM file name protocols
date = datetime_from_radar(radar).strftime('%Y%m%d.%H%M%S')
filename = 'sgpxsaprppicmac{}.{}.{}.cdf'.format(FN, DL, date)
# Write CMAC NetCDF file
write_cfradial(os.path.join(outdir, filename), radar, format=FORMAT,
arm_time_variables=True)
return
