def main(opts):
"""
crossmul
"""
# prepare input rasters
referenceSlc = SLC(hdf5file=opts.reference)
referenceSlcDataset = referenceSlc.getSlcDataset(opts.frequency,
opts.polarization)
referenceSlcRaster = isce3.io.raster(filename='', h5=referenceSlcDataset)
secondarySlc = SLC(hdf5file=opts.secondary)
if opts.secondaryRaster:
secondarySlcRaster = isce3.io.raster(filename=opts.secondaryRaster)
else:
secondarySlcDataset = secondarySlc.getSlcDataset(
opts.frequency, opts.polarization)
secondarySlcRaster = isce3.io.raster(filename='',
h5=secondarySlcDataset)
# prepare mulitlooked interferogram dimensions
referenceGrid = referenceSlc.getRadarGrid(opts.frequency)
length = int(referenceGrid.length / opts.alks)
width = int(referenceGrid.width / opts.rlks)
# init output directory(s)
getDir = lambda filepath: os.path.split(filepath)[0]
os.makedirs(getDir(opts.intFilePath), exist_ok=True)
os.makedirs(getDir(opts.cohFilePath), exist_ok=True)
# prepare output rasters
driver = gdal.GetDriverByName('ISCE')
igramDataset = driver.Create(opts.intFilePath, width, length, 1,
gdal.GDT_CFloat32)
igramRaster = isce3.io.raster(filename='', dataset=igramDataset)
# coherence only generated when multilooked enabled
if (opts.alks > 1 or opts.rlks > 1):
cohDataset = driver.Create(opts.cohFilePath, width, length, 1,
gdal.GDT_Float32)
cohRaster = isce3.io.raster(filename='', dataset=cohDataset)
else:
cohRaster = None
# prepare optional rasters
if opts.rgoff:
rgOffRaster = isce3.io.raster(filename=opts.rgoff)
else:
rgOffRaster = None
if opts.azband:
dopReference = referenceSlc.getDopplerCentroid()
dopSecondary = secondarySlc.getDopplerCentroid()
prf = referenceSlc.getSwathMetadata(
opts.frequency).nominalAcquisitionPRF
azimuthBandwidth = opts.azband
else:
dopReference = dopSecondary = None
prf = azimuthBandwidth = 0.0
# instantiate crossmul object based on user input
if 'cuda' not in dir(isce3) and opts.gpu:
warnings.warn('CUDA crossmul not available. Switching to CPU crossmul')
opts.gpu = False
if opts.gpu:
crossmul = isce3.cuda.signal.crossmul()
else:
crossmul = isce3.signal.crossmul()
crossmul.crossmul(referenceSlcRaster,
secondarySlcRaster,
igramRaster,
cohRaster,
rngOffset=rgOffRaster,
refDoppler=dopReference,
secDoppler=dopSecondary,
rangeLooks=opts.rlks,
azimuthLooks=opts.alks,
prf=prf,
azimuthBandwidth=azimuthBandwidth)
def run(cfg: dict, output_hdf5: str = None, resample_type='coarse'):
'''
run crossmul
'''
# pull parameters from cfg
ref_hdf5 = cfg['input_file_group']['input_file_path']
sec_hdf5 = cfg['input_file_group']['secondary_file_path']
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
flatten = cfg['processing']['crossmul']['flatten']
if flatten is not None:
flatten_path = cfg['processing']['crossmul']['flatten']
if output_hdf5 is None:
output_hdf5 = cfg['product_path_group']['sas_output_file']
# init parameters shared by frequency A and B
ref_slc = SLC(hdf5file=ref_hdf5)
sec_slc = SLC(hdf5file=sec_hdf5)
error_channel = journal.error('crossmul.run')
info_channel = journal.info("crossmul.run")
info_channel.log("starting crossmultipy")
# check if gpu ok to use
use_gpu = isce3.core.gpu_check.use_gpu(cfg['worker']['gpu_enabled'],
cfg['worker']['gpu_id'])
if use_gpu:
# Set the current CUDA device.
device = isce3.cuda.core.Device(cfg['worker']['gpu_id'])
isce3.cuda.core.set_device(device)
crossmul = isce3.cuda.signal.Crossmul()
else:
crossmul = isce3.signal.Crossmul()
crossmul.range_looks = cfg['processing']['crossmul']['range_looks']
crossmul.az_looks = cfg['processing']['crossmul']['azimuth_looks']
crossmul.oversample = cfg['processing']['crossmul']['oversample']
crossmul.rows_per_block = cfg['processing']['crossmul']['rows_per_block']
# check if user provided path to raster(s) is a file or directory
coregistered_slc_path = pathlib.Path(
cfg['processing']['crossmul']['coregistered_slc_path'])
coregistered_is_file = coregistered_slc_path.is_file()
if not coregistered_is_file and not coregistered_slc_path.is_dir():
err_str = f"{coregistered_slc_path} is invalid; needs to be a file or directory."
error_channel.log(err_str)
raise ValueError(err_str)
t_all = time.time()
with h5py.File(output_hdf5, 'a', libver='latest', swmr=True) as dst_h5:
for freq, pol_list in freq_pols.items():
# get 2d doppler, discard azimuth dependency, and set crossmul dopplers
ref_dopp = isce3.core.avg_lut2d_to_lut1d(
ref_slc.getDopplerCentroid(frequency=freq))
sec_dopp = isce3.core.avg_lut2d_to_lut1d(
sec_slc.getDopplerCentroid(frequency=freq))
crossmul.set_dopplers(ref_dopp, sec_dopp)
freq_group_path = f'/science/LSAR/RIFG/swaths/frequency{freq}'
if flatten is not None:
# set frequency dependent range offset raster
flatten_raster = isce3.io.Raster(
f'{flatten_path}/geo2rdr/freq{freq}/range.off')
# prepare range filter parameters
rdr_grid = ref_slc.getRadarGrid(freq)
rg_pxl_spacing = rdr_grid.range_pixel_spacing
wavelength = rdr_grid.wavelength
rg_sample_freq = isce3.core.speed_of_light / 2.0 / rg_pxl_spacing
rg_bandwidth = ref_slc.getSwathMetadata(
freq).processed_range_bandwidth
# set crossmul range filter
crossmul.set_rg_filter(rg_sample_freq, rg_bandwidth,
rg_pxl_spacing, wavelength)
for pol in pol_list:
pol_group_path = f'{freq_group_path}/interferogram/{pol}'
# prepare reference input raster
ref_raster_str = f'HDF5:{ref_hdf5}:/{ref_slc.slcPath(freq, pol)}'
ref_slc_raster = isce3.io.Raster(ref_raster_str)
# prepare secondary input raster
if coregistered_is_file:
raster_str = f'HDF5:{sec_hdf5}:/{sec_slc.slcPath(freq, pol)}'
else:
raster_str = str(
coregistered_slc_path /
f'{resample_type}_resample_slc/'
f'freq{freq}/{pol}/coregistered_secondary.slc')
sec_slc_raster = isce3.io.Raster(raster_str)
# access the HDF5 dataset for a given frequency and polarization
dataset_path = f'{pol_group_path}/wrappedInterferogram'
igram_dataset = dst_h5[dataset_path]
# Construct the output ratster directly from HDF5 dataset
igram_raster = isce3.io.Raster(
f"IH5:::ID={igram_dataset.id.id}".encode("utf-8"),
update=True)
# call crossmul with coherence if multilooked
if crossmul.range_looks > 1 or crossmul.az_looks > 1:
# access the HDF5 dataset for a given frequency and polarization
dataset_path = f'{pol_group_path}/coherenceMagnitude'
coherence_dataset = dst_h5[dataset_path]
# Construct the output ratster directly from HDF5 dataset
coherence_raster = isce3.io.Raster(
f"IH5:::ID={coherence_dataset.id.id}".encode("utf-8"),
update=True)
if flatten is not None:
crossmul.crossmul(ref_slc_raster, sec_slc_raster,
flatten_raster, igram_raster,
coherence_raster)
else:
crossmul.crossmul(ref_slc_raster, sec_slc_raster,
igram_raster, coherence_raster)
# Allocate raster statistics for coherence
compute_stats_real_data(coherence_raster,
coherence_dataset)
del coherence_raster
else:
# no coherence without multilook
crossmul.crossmul(ref_slc_raster, sec_slc_raster,
igram_raster)
del igram_raster
# Allocate stats for rubbersheet offsets
stats_offsets(dst_h5, freq, pol)
t_all_elapsed = time.time() - t_all
info_channel.log(
f"successfully ran crossmul in {t_all_elapsed:.3f} seconds")