def sigmet2cfrad(filepath,
outdir='cf',
dirlock=None,
verbose=False,
filter_raw=False):
"""Convert Sigmet raw radar data to cfradial format"""
radar = io.read_sigmet(filepath)
if is_bad(radar):
if filter_raw:
if verbose:
eprint('rm {}'.format(filepath))
remove(filepath)
return
time = radar_start_datetime(radar)
if dirlock:
dirlock.acquire()
subdir = ensure_dir(path.join(outdir, time.strftime('%Y%m%d')))
if dirlock:
dirlock.release()
out_filepath = path.join(subdir, cfrad_filename(radar))
if verbose:
print(out_filepath)
io.write_cfradial(out_filepath, radar)
# Search for the file(s)
flist = glob(args.searchstring)
# Create an output file if requested
if args.make_file:
OutTx = open(args.outFile,'w')
for file in flist:
fLName, fileExt = os.path.splitext(file)
fName = os.path.basename(file)
# Get the file size
fSize = os.path.getsize(file)
# read in the file
radar = fio.read_sigmet(file,file_field_names=True)
# Create a latitude string
if radar.latitude['units'] == 'degrees_north':
rlat = str(radar.latitude['data'])[1:-1]+'N'
if radar.latitude['units'] == 'degrees_south':
rlat = str(radar.latitude['data'])[1:-1]+'S'
# Create a longitude string
if radar.longitude['units'] == 'degrees_east':
rlon = str(radar.longitude['data'])[1:-1]+'E'
if radar.longitude['units'] == 'degrees_west':
rlon = str(radar.longitude['data'])[1:-1]+'W'
# Create field names, check for extended header
if radar.metadata['sigmet_extended_header'] == 'true':
# Search for the file(s)
flist = glob(args.searchstring)
# Create an output file if requested
if args.make_file:
OutTx = open(args.outFile, 'w')
for file in flist:
fLName, fileExt = os.path.splitext(file)
fName = os.path.basename(file)
# Get the file size
fSize = os.path.getsize(file)
# read in the file
radar = fio.read_sigmet(file, file_field_names=True)
# Create a latitude string
if radar.latitude['units'] == 'degrees_north':
rlat = str(radar.latitude['data'])[1:-1] + 'N'
if radar.latitude['units'] == 'degrees_south':
rlat = str(radar.latitude['data'])[1:-1] + 'S'
# Create a longitude string
if radar.longitude['units'] == 'degrees_east':
rlon = str(radar.longitude['data'])[1:-1] + 'E'
if radar.longitude['units'] == 'degrees_west':
rlon = str(radar.longitude['data'])[1:-1] + 'W'
# Create field names, check for extended header
if radar.metadata['sigmet_extended_header'] == 'true':
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