def process_file(filename, outdir, debug=False, verbose=False): """ """ if verbose: print 'Processing file: {}'.format(os.path.basename(filename)) # Read radar data radar = read_mdv(filename, exclude_fields=EXLUDE_FIELDS) if debug: print 'Number of sweeps: {}'.format(radar.nsweeps) # Step 1: Remove last 7 gates from each ray # These gates are reserved for signal testing only for field in radar.fields.keys(): if verbose: print 'Removing signal testing gates: {}'.format(field) radar.fields[field]['data'][:,-7:] = np.ma.masked # Step 2: Radar significant detection # Includes Doppler velocity coherency, Doppler velocity phasor coherency, # and significant echo boundary detection gf = noise.velocity_coherency( radar, gatefilter=None, text_bins=VDOP_TEXT_BINS, text_limits=VDOP_TEXT_LIMITS, texture_window=TEXTURE_WINDOW, texture_sample=TEXTURE_SAMPLE, max_texture=None, nyquist=None, rays_wrap_around=False, remove_small_features=False, fill_value=None, vdop_field=VDOP_FIELD, text_field=None, coherent_field=None, debug=debug, verbose=verbose) gf = noise.velocity_phasor_coherency( radar, gatefilter=gf, text_bins=PHASOR_TEXT_BINS, text_limits=PHASOR_TEXT_LIMITS, texture_window=TEXTURE_WINDOW, texture_sample=TEXTURE_SAMPLE, max_texture=None, rays_wrap_around=False, remove_small_features=False, fill_value=None, vdop_field=VDOP_FIELD, phasor_field=None, text_field=None, coherent_field=None, debug=debug, verbose=verbose) gf = noise.echo_boundaries( radar, gatefilter=gf, texture_window=TEXTURE_WINDOW, texture_sample=TEXTURE_SAMPLE, min_texture=None, bounds_percentile=BOUNDS_PERCENTILE, remove_small_features=False, rays_wrap_around=False, fill_value=None, sqi_field=NCP_FIELD, text_field=None, bounds_field=None, debug=debug, verbose=verbose) gf = noise.significant_detection( radar, gatefilter=gf, remove_small_features=True, size_bins=SIZE_BINS, size_limits=SIZE_LIMITS, fill_holes=FILL_HOLES, dilate=DILATE, structure=STRUCTURE, iterations=ITERATIONS, min_ncp=MIN_NCP, ncp_field=NCP_FIELD, detect_field=None, debug=debug, verbose=verbose) # Step 3: 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) # Step 4: Doppler velocity correction if DEALIAS.upper() == '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=VDOP_FIELD, corr_vel_field=CORR_VDOP_FIELD) else: raise ValueError('Unsupported velocity correction routine') radar.add_field(CORR_VDOP_FIELD, vdop_corr, replace_existing=False) # TODO # Step 5: Reflectivity correction refl_corr = radar.fields[REFL_FIELD].copy() radar.add_field(CORR_REFL_FIELD, refl_corr, replace_existing=False) # Step 6: 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) # Step 7: Remove unwanted fields before writing for field in REMOVE_FIELDS: if verbose: print 'Removing radar field before writing: {}'.format(field) radar.fields.pop(field, None) # Parse metadata _add_metadata(radar, filename) # ARM file name protocols date = datetimes_from_radar(radar).min().strftime('%Y%m%d.%H%M%S') fname = 'sgpcsaprsurcmac{}.{}.{}.cdf'.format(FN, DL, date) # Write CMAC NetCDF file write_cfradial(os.path.join(outdir, fname), radar, format=FORMAT, arm_time_variables=True) 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