def _loop_over_fields(json, pickle, inpdir=None, outdir=None, verbose=False):
"""
"""
# Reflectivity
if verbose:
print 'Processing reflectivity'
refl = moment_fields.histogram_from_json(
json, REFL_FIELD, inpdir=inpdir, bins=BINS_REFL,
limits=LIMITS_REFL, min_ncp=MIN_NCP, vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS, min_sweep=MIN_SWEEP, max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS, fill_value=None, ncp_field=NCP_FIELD,
verbose=verbose)
# Doppler velocity
if verbose:
print 'Processing Doppler velocity'
vdop = moment_fields.histogram_from_json(
json, VDOP_FIELD, inpdir=inpdir, bins=BINS_VDOP,
limits=LIMITS_VDOP, min_ncp=MIN_NCP, vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS, min_sweep=MIN_SWEEP, max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS, fill_value=None, ncp_field=NCP_FIELD,
verbose=verbose)
# Spectrum width
if verbose:
print 'Processing spectrum width'
sw = moment_fields.histogram_from_json(
json, SW_FIELD, inpdir=inpdir, bins=BINS_SW,
limits=LIMITS_SW, min_ncp=MIN_NCP, vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS, min_sweep=MIN_SWEEP, max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS, fill_value=None, ncp_field=NCP_FIELD,
verbose=verbose)
# Copolar correlation
if verbose:
print 'Processing copolar correlation'
rhohv = moment_fields.histogram_from_json(
json, RHOHV_FIELD, inpdir=inpdir, bins=BINS_RHOHV,
limits=LIMITS_RHOHV, min_ncp=MIN_NCP, vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS, min_sweep=MIN_SWEEP, max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS, fill_value=None, ncp_field=NCP_FIELD,
verbose=verbose)
# Differential reflectivity
if verbose:
print 'Processing differential reflectivity'
zdr = moment_fields.histogram_from_json(
json, ZDR_FIELD, inpdir=inpdir, bins=BINS_ZDR,
limits=LIMITS_ZDR, min_ncp=MIN_NCP, vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS, min_sweep=MIN_SWEEP, max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS, fill_value=None, ncp_field=NCP_FIELD,
verbose=verbose)
# Coherent power
if verbose:
print 'Processing normalized coherent power'
ncp = moment_fields.histogram_from_json(
json, NCP_FIELD, inpdir=inpdir, bins=BINS_NCP,
limits=LIMITS_NCP, min_ncp=MIN_NCP, vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS, min_sweep=MIN_SWEEP, max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS, fill_value=None, ncp_field=NCP_FIELD,
verbose=verbose)
# Pack histograms together
histograms = [refl, vdop, sw, rhohv, zdr, ncp]
# Pickle moment histograms
moment_fields._pickle_histograms(
histograms, pickle, outdir=outdir)
return
def _loop_over_fields(json, pickle, inpdir=None, outdir=None, verbose=False):
"""
"""
# Reflectivity
if verbose:
print 'Processing reflectivity'
refl = moment_fields.histogram_from_json(json,
REFL_FIELD,
inpdir=inpdir,
bins=BINS_REFL,
limits=LIMITS_REFL,
min_ncp=MIN_NCP,
vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS,
min_sweep=MIN_SWEEP,
max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS,
fill_value=None,
ncp_field=NCP_FIELD,
verbose=verbose)
# Doppler velocity
if verbose:
print 'Processing Doppler velocity'
vdop = moment_fields.histogram_from_json(json,
VDOP_FIELD,
inpdir=inpdir,
bins=BINS_VDOP,
limits=LIMITS_VDOP,
min_ncp=MIN_NCP,
vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS,
min_sweep=MIN_SWEEP,
max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS,
fill_value=None,
ncp_field=NCP_FIELD,
verbose=verbose)
# Spectrum width
if verbose:
print 'Processing spectrum width'
sw = moment_fields.histogram_from_json(json,
SW_FIELD,
inpdir=inpdir,
bins=BINS_SW,
limits=LIMITS_SW,
min_ncp=MIN_NCP,
vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS,
min_sweep=MIN_SWEEP,
max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS,
fill_value=None,
ncp_field=NCP_FIELD,
verbose=verbose)
# Copolar correlation
if verbose:
print 'Processing copolar correlation'
rhohv = moment_fields.histogram_from_json(json,
RHOHV_FIELD,
inpdir=inpdir,
bins=BINS_RHOHV,
limits=LIMITS_RHOHV,
min_ncp=MIN_NCP,
vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS,
min_sweep=MIN_SWEEP,
max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS,
fill_value=None,
ncp_field=NCP_FIELD,
verbose=verbose)
# Differential reflectivity
if verbose:
print 'Processing differential reflectivity'
zdr = moment_fields.histogram_from_json(json,
ZDR_FIELD,
inpdir=inpdir,
bins=BINS_ZDR,
limits=LIMITS_ZDR,
min_ncp=MIN_NCP,
vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS,
min_sweep=MIN_SWEEP,
max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS,
fill_value=None,
ncp_field=NCP_FIELD,
verbose=verbose)
# Coherent power
if verbose:
print 'Processing normalized coherent power'
ncp = moment_fields.histogram_from_json(json,
NCP_FIELD,
inpdir=inpdir,
bins=BINS_NCP,
limits=LIMITS_NCP,
min_ncp=MIN_NCP,
vcp_sweeps=VCP_SWEEPS,
vcp_rays=VCP_RAYS,
min_sweep=MIN_SWEEP,
max_sweep=MAX_SWEEP,
exclude_fields=EXCLUDE_FIELDS,
fill_value=None,
ncp_field=NCP_FIELD,
verbose=verbose)
# Pack histograms together
histograms = [refl, vdop, sw, rhohv, zdr, ncp]
# Pickle moment histograms
moment_fields._pickle_histograms(histograms, pickle, outdir=outdir)
return
def _loop_over_dict(json_file, pickle_file, inpdir=None, outdir=None,
verbose=False):
"""
"""
# Parse files from JSON file
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=True)
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=True)
# Compute histogram counts for each field
moment_fields.histograms_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)
# Normalize histograms for each field and compute probability densities
for field in HIST_DICT:
# Parse bin edges and histogram counts
bin_edges = HIST_DICT[field]['bin edges']
counts = HIST_DICT[field]['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[field]['normalized histogram'] = counts_norm
HIST_DICT[field]['probability density'] = pdf
# Include other parameters in the histogram dictionary
HIST_DICT[field]['radar files'] = files
HIST_DICT[field]['min sweep'] = MIN_SWEEP
HIST_DICT[field]['max sweep'] = MAX_SWEEP
HIST_DICT[field]['min range'] = MIN_RANGE
HIST_DICT[field]['max range'] = MAX_RANGE
HIST_DICT[field]['sweeps in VCP'] = VCP_SWEEPS
HIST_DICT[field]['rays in VCP'] = VCP_RAYS
HIST_DICT[field]['min NCP'] = MIN_NCP
# Pickle histogram data
moment_fields._pickle_histograms(
HIST_DICT, pickle_file, outdir=outdir)
return
def _loop_over_dict(json_file,
pickle_file,
inpdir=None,
outdir=None,
verbose=False):
"""
"""
# Parse files from JSON file
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=True)
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=True)
# Compute histogram counts for each field
moment_fields.histograms_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)
# Normalize histograms for each field and compute probability densities
for field in HIST_DICT:
# Parse bin edges and histogram counts
bin_edges = HIST_DICT[field]['bin edges']
counts = HIST_DICT[field]['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[field]['normalized histogram'] = counts_norm
HIST_DICT[field]['probability density'] = pdf
# Include other parameters in the histogram dictionary
HIST_DICT[field]['radar files'] = files
HIST_DICT[field]['min sweep'] = MIN_SWEEP
HIST_DICT[field]['max sweep'] = MAX_SWEEP
HIST_DICT[field]['min range'] = MIN_RANGE
HIST_DICT[field]['max range'] = MAX_RANGE
HIST_DICT[field]['sweeps in VCP'] = VCP_SWEEPS
HIST_DICT[field]['rays in VCP'] = VCP_RAYS
HIST_DICT[field]['min NCP'] = MIN_NCP
# Pickle histogram data
moment_fields._pickle_histograms(HIST_DICT, pickle_file, outdir=outdir)
return
