def test_point():
# Subset of tests/cxx/isce3/geometry/geometry/geometry.cpp
fn = os.path.join(iscetest.data, "envisat.h5")
slc = SLC(hdf5file=fn)
orbit = slc.getOrbit()
subband = "A"
doplut = slc.getDopplerCentroid(frequency=subband)
grid = slc.getRadarGrid(frequency=subband)
# First row of input_data.txt
dt = isce.core.DateTime("2003-02-26T17:55:22.976222")
r = 826988.6900674499
h = 1777.
dem = isce.geometry.DEMInterpolator(h)
t = (dt - orbit.reference_epoch).total_seconds()
dop = doplut.eval(t, r)
wvl = grid.wavelength
# native doppler, expect first row of output_data.txt
llh = isce.geometry.rdr2geo(t, r, orbit, grid.lookside, dop, wvl, dem)
assert np.isclose(np.degrees(llh[0]), -115.44101120961082)
assert np.isclose(np.degrees(llh[1]), 35.28794014757191)
assert np.isclose(llh[2], 1777.)
# zero doppler, expect first row of output_data_zerodop.txt
llh = isce.geometry.rdr2geo(t, r, orbit, grid.lookside, 0.0, dem=dem)
assert np.isclose(np.degrees(llh[0]), -115.43883834023249)
assert np.isclose(np.degrees(llh[1]), 35.29610867314526)
assert np.isclose(llh[2], 1776.9999999993)
def run(cfg):
'''
run rdr2geo
'''
# pull parameters from cfg
input_hdf5 = cfg['InputFileGroup']['InputFilePath']
dem_file = cfg['DynamicAncillaryFileGroup']['DEMFile']
scratch_path = pathlib.Path(cfg['ProductPathGroup']['ScratchPath'])
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
# get params from SLC
slc = SLC(hdf5file=input_hdf5)
orbit = slc.getOrbit()
# set defaults shared by both frequencies
dem_raster = isce3.io.Raster(dem_file)
epsg = dem_raster.get_epsg()
proj = isce3.core.make_projection(epsg)
ellipsoid = proj.ellipsoid
# NISAR RSLC products are always zero doppler
grid_doppler = isce3.core.LUT2d()
info_channel = journal.info("rdr2geo.run")
info_channel.log("starting geocode SLC")
# check if gpu ok to use
use_gpu = 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)
t_all = time.time()
for freq in freq_pols.keys():
# get frequency specific parameters
radargrid = slc.getRadarGrid(freq)
# create seperate directory within scratch dir for rdr2geo run
rdr2geo_scratch_path = scratch_path / 'rdr2geo' / f'freq{freq}'
rdr2geo_scratch_path.mkdir(parents=True, exist_ok=True)
# init CPU or CUDA object accordingly
if use_gpu:
Rdr2Geo = isce3.cuda.geometry.Rdr2Geo
else:
Rdr2Geo = isce3.geometry.Rdr2Geo
rdr2geo_obj = Rdr2Geo(radargrid, orbit, ellipsoid, grid_doppler)
# run
rdr2geo_obj.topo(dem_raster, str(rdr2geo_scratch_path))
t_all_elapsed = time.time() - t_all
info_channel.log(f"successfully ran rdr2geo in {t_all_elapsed:.3f} seconds")
def common_crossmul_obj():
'''
instantiate and return common crossmul object for both run tests
'''
# make SLC object and extract parameters
slc_obj = SLC(hdf5file=os.path.join(iscetest.data, 'envisat.h5'))
dopp = isce.core.LUT1d(slc_obj.getDopplerCentroid())
prf = slc_obj.getRadarGrid().prf
crossmul = isce.signal.Crossmul()
crossmul.set_dopplers(dopp, dopp)
crossmul.set_az_filter(prf, 2000.0, 0.25)
return crossmul
def run(cfg):
'''
run geo2rdr
'''
# Pull parameters from cfg dict
sec_hdf5 = cfg['InputFileGroup']['SecondaryFilePath']
dem_file = cfg['DynamicAncillaryFileGroup']['DEMFile']
scratch_path = pathlib.Path(cfg['ProductPathGroup']['ScratchPath'])
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
# Get geo2rdr params
geo2rdr_in = cfg['processing']['geo2rdr']
# Get parameters from SLC
slc = SLC(hdf5file=sec_hdf5)
orbit = slc.getOrbit()
# Set ellipsoid based on DEM epsg
dem_raster = isce3.io.Raster(dem_file)
epsg = dem_raster.get_epsg()
proj = isce3.core.make_projection(epsg)
ellipsoid = proj.ellipsoid
# NISAR RSLC products are always zero doppler
doppler_grid = isce3.core.LUT2d()
info_channel = journal.info('geo2rdr.run')
info_channel.log("starting geo2rdr")
# check if gpu use if required
use_gpu = gpu_check.use_gpu(cfg['worker']['gpu_enabled'],
cfg['worker']['gpu_id'])
if use_gpu:
# set CUDA device
device = isce3.cuda.core.Device(cfg['worker']['gpu_id'])
isce3.cuda.core.set_device(device)
t_all = time.time()
for freq in freq_pols.keys():
# Get parameters specific for that frequency
radar_grid = slc.getRadarGrid(frequency=freq)
# Create geo2rdr directory
geo2rdr_scratch_path = scratch_path / 'geo2rdr' / f'freq{freq}'
geo2rdr_scratch_path.mkdir(parents=True, exist_ok=True)
# Initialize CPU or GPU geo2rdr object accordingly
if use_gpu:
Geo2Rdr = isce3.cuda.geometry.Geo2Rdr
else:
Geo2Rdr = isce3.geometry.Geo2Rdr
geo2rdr_obj = Geo2Rdr(radar_grid,
orbit,
ellipsoid,
doppler_grid,
threshold=geo2rdr_in['threshold'],
numiter=geo2rdr_in['maxiter'])
# Opem Topo Raster
topo_path = pathlib.Path(cfg['processing']['geo2rdr']['topo_path'])
rdr2geo_topo_path = topo_path / 'rdr2geo' / f'freq{freq}' / 'topo.vrt'
topo_raster = isce3.io.Raster(str(rdr2geo_topo_path))
# Run geo2rdr
geo2rdr_obj.geo2rdr(topo_raster, str(geo2rdr_scratch_path))
t_all_elapsed = time.time() - t_all
info_channel.log(
f"Successfully ran geo2rdr in {t_all_elapsed:.3f} seconds")
def create(cfg, frequency):
'''
For production we only fix epsgcode and snap value and will
rely on the rslc product metadta to compute the bounding box of the geocoded products
there is a place holder in SLC product for compute Bounding box
when that method is populated we should be able to simply say
bbox = self.slc_obj.computeBoundingBox(epsg=state.epsg)
for now let's rely on the run config input
'''
error_channel = journal.error('geogrid.create')
# unpack and init
geocode_dict = cfg['processing']['geocode']
input_hdf5 = cfg['InputFileGroup']['InputFilePath']
dem_file = cfg['DynamicAncillaryFileGroup']['DEMFile']
slc = SLC(hdf5file=input_hdf5)
# unpack and check cfg dict values. default values set to trigger inside fix(...)
epsg = geocode_dict['outputEPSG']
start_x = geocode_dict['top_left']['x_abs']
start_y = geocode_dict['top_left']['y_abs']
spacing_x = geocode_dict['output_posting'][frequency]['x_posting']
spacing_y = geocode_dict['output_posting'][frequency]['y_posting']
end_x = geocode_dict['bottom_right']['x_abs']
end_y = geocode_dict['bottom_right']['y_abs']
assert epsg is not None
assert 1024 <= epsg <= 32767
if spacing_y is not None:
# spacing_y from runconfig should be positive valued
assert spacing_y > 0.0
spacing_y = -1.0 * spacing_y
# init geogrid
if None in [start_x, start_y, spacing_x, spacing_y, epsg, end_x, end_y]:
dem_raster = isce.io.Raster(dem_file)
# copy X spacing from DEM
if spacing_x is None:
spacing_x = dem_raster.dx
# DEM X spacing should be positive
if spacing_x <= 0:
err_str = f'Expected positive pixel spacing in the X/longitude direction'
err_str += f' for DEM {dem_file}. Actual value: {spacing_x}.'
error_channel.log(err_str)
raise ValueError(err_str)
# copy Y spacing from DEM
if spacing_y is None:
spacing_y = dem_raster.dy
# DEM Y spacing should be negative
if spacing_y >= 0:
err_str = f'Expected negative pixel spacing in the Y/latitude direction'
err_str += f' for DEM {dem_file}. Actual value: {spacing_y}.'
error_channel.log(err_str)
raise ValueError(err_str)
# extract other geogrid params from radar grid and orbit constructed bounding box
geogrid = isce.product.bbox_to_geogrid(
slc.getRadarGrid(frequency), slc.getOrbit(),
slc.getDopplerCentroid(frequency=frequency), spacing_x, spacing_y,
epsg)
# restore runconfig start_x (if provided)
if start_x is not None:
end_x_from_function = geogrid.start_x + geogrid.spacing_x * geogrid.width
geogrid.start_x = start_x
geogrid.width = int(
np.ceil((end_x_from_function - start_x) / geogrid.spacing_x))
# restore runconfig end_x (if provided)
if end_x is not None:
geogrid.width = int(
np.ceil((end_x - geogrid.start_x) / geogrid.spacing_x))
# restore runconfig start_y (if provided)
if start_y is not None:
end_y_from_function = geogrid.start_y + geogrid.spacing_y * geogrid.length
geogrid.start_y = start_y
geogrid.length = int(
np.ceil((end_y_from_function - start_y) / geogrid.spacing_y))
# restore runconfig end_y (if provided)
if end_y is not None:
geogrid.length = int(
np.ceil((end_y - geogrid.start_y) / geogrid.spacing_y))
else:
if spacing_x == 0.0 or spacing_y == 0.0:
err_str = 'spacing_x or spacing_y cannot be 0.0'
error_channel.log(err_str)
raise ValueError(err_str)
width = _grid_size(end_x, start_x, spacing_x)
length = _grid_size(end_y, start_y, -1.0 * spacing_y)
# build from probably good user values
geogrid = isce.product.GeoGridParameters(start_x, start_y, spacing_x,
spacing_y, width, length,
epsg)
# recheck x+y end points before snap and length+width calculation
end_pt = lambda start, sz, spacing: start + spacing * sz
if end_x is None:
end_x = end_pt(geogrid.start_x, geogrid.spacing_x, geogrid.width)
if end_y is None:
end_y = end_pt(geogrid.start_y, geogrid.spacing_y, geogrid.length)
# snap all the things
x_snap = geocode_dict['x_snap']
y_snap = geocode_dict['y_snap']
if x_snap is not None or y_snap is not None:
# check snap values before proceeding
if x_snap <= 0 or y_snap <= 0:
err_str = 'Snap values must be > 0.'
error_channel.log(err_str)
raise ValueError(err_str)
if x_snap % spacing_x != 0.0 or y_snap % spacing_y != 0:
err_str = 'Snap values must be exact multiples of spacings. i.e. snap % spacing == 0.0'
error_channel.log(err_str)
raise ValueError(err_str)
snap_coord = lambda val, snap, round_func: round_func(
float(val) / snap) * snap
geogrid.start_x = snap_coord(geogrid.start_x, x_snap, np.floor)
geogrid.start_y = snap_coord(geogrid.start_y, y_snap, np.ceil)
end_x = snap_coord(end_x, x_snap, np.ceil)
end_y = snap_coord(end_y, y_snap, np.floor)
geogrid.length = _grid_size(end_y, geogrid.start_y, geogrid.spacing_y)
geogrid.width = _grid_size(end_x, geogrid.start_x, geogrid.spacing_x)
return geogrid
def test_rtc():
# Open HDF5 file and create radar grid parameter
print('iscetest.data:', iscetest.data)
h5_path = os.path.join(iscetest.data, 'envisat.h5')
slc_obj = SLC(hdf5file=h5_path)
frequency = 'A'
radar_grid_sl = slc_obj.getRadarGrid(frequency)
# Open DEM raster
dem_file = os.path.join(iscetest.data, 'srtm_cropped.tif')
dem_obj = isce3.io.Raster(dem_file)
# Crop original radar grid parameter
radar_grid_cropped = \
radar_grid_sl.offset_and_resize(30, 135, 128, 128)
# Multi-look original radar grid parameter
nlooks_az = 5
nlooks_rg = 5
radar_grid_ml = \
radar_grid_sl.multilook(nlooks_az, nlooks_rg)
# Create orbit and Doppler LUT
orbit = slc_obj.getOrbit()
doppler = slc_obj.getDopplerCentroid()
doppler.bounds_error = False
# doppler = isce3.core.LUT2d()
# list input parameters
inputRadiometry = isce3.geometry.RtcInputRadiometry.BETA_NAUGHT
rtc_area_mode = isce3.geometry.RtcAreaMode.AREA_FACTOR
for radar_grid_str in radar_grid_str_list:
# Open DEM raster
if (radar_grid_str == 'cropped'):
radar_grid = radar_grid_cropped
else:
radar_grid = radar_grid_ml
for rtc_algorithm in rtc_algorithm_list:
geogrid_upsampling = 1
# test removed because it requires high geogrid upsampling (too
# slow)
if (rtc_algorithm == isce3.geometry.RtcAlgorithm.RTC_DAVID_SMALL
and radar_grid_str == 'cropped'):
continue
elif (rtc_algorithm == isce3.geometry.RtcAlgorithm.RTC_DAVID_SMALL
):
filename = './rtc_david_small_' + radar_grid_str + '.bin'
else:
filename = './rtc_area_proj_' + radar_grid_str + '.bin'
print('generating file:', filename)
# Create output raster
out_raster = isce3.io.Raster(filename, radar_grid.width,
radar_grid.length, 1,
gdal.GDT_Float32, 'ENVI')
# Call RTC
isce3.geometry.facet_rtc(radar_grid, orbit, doppler, dem_obj,
out_raster, inputRadiometry,
rtc_area_mode, rtc_algorithm,
geogrid_upsampling)
del out_raster
# check results
for radar_grid_str in radar_grid_str_list:
for rtc_algorithm in rtc_algorithm_list:
# test removed because it requires high geogrid upsampling (too
# slow)
if (rtc_algorithm == isce3.geometry.RtcAlgorithm.RTC_DAVID_SMALL
and radar_grid_str == 'cropped'):
continue
elif (rtc_algorithm == isce3.geometry.RtcAlgorithm.RTC_DAVID_SMALL
):
max_rmse = 0.7
filename = './rtc_david_small_' + radar_grid_str + '.bin'
else:
max_rmse = 0.1
filename = './rtc_area_proj_' + radar_grid_str + '.bin'
print('evaluating file:', os.path.abspath(filename))
# Open computed integrated-area raster
test_gdal_dataset = gdal.Open(filename)
# Open reference raster
ref_filename = os.path.join(iscetest.data,
'rtc/rtc_' + radar_grid_str + '.bin')
ref_gdal_dataset = gdal.Open(ref_filename)
print('reference file:', ref_filename)
assert (
test_gdal_dataset.RasterXSize == ref_gdal_dataset.RasterXSize)
assert (
test_gdal_dataset.RasterYSize == ref_gdal_dataset.RasterYSize)
square_sum = 0.0 # sum of square difference
n_nan = 0 # number of NaN pixels
n_npos = 0 # number of non-positive pixels
# read test and ref arrays
test_array = test_gdal_dataset.GetRasterBand(1).ReadAsArray()
ref_array = ref_gdal_dataset.GetRasterBand(1).ReadAsArray()
n_valid = 0
# iterates over rows (i) and columns (j)
for i in range(ref_gdal_dataset.RasterYSize):
for j in range(ref_gdal_dataset.RasterXSize):
# if nan, increment n_nan
if (np.isnan(test_array[i, j])
or np.isnan(ref_array[i, j])):
n_nan = n_nan + 1
continue
# if n_npos, incremennt n_npos
if (ref_array[i, j] <= 0 or test_array[i, j] <= 0):
n_npos = n_npos + 1
continue
# otherwise, increment n_valid
n_valid = n_valid + 1
square_sum += (test_array[i, j] - ref_array[i, j])**2
print(' ----------------')
print(' # total:', n_valid + n_nan + n_npos)
print(' ----------------')
print(' # valid:', n_valid)
print(' # NaNs:', n_nan)
print(' # non-positive:', n_npos)
print(' ----------------')
assert (n_valid != 0)
# Compute average over entire image
rmse = np.sqrt(square_sum / n_valid)
print(' RMSE =', rmse)
print(' ----------------')
# Enforce bound on average pixel-error
assert (rmse < max_rmse)
# Enforce bound on number of ignored pixels
assert (n_nan < 1e-4 * ref_gdal_dataset.RasterXSize *
ref_gdal_dataset.RasterYSize)
assert (n_npos < 1e-4 * ref_gdal_dataset.RasterXSize *
ref_gdal_dataset.RasterYSize)
def run(cfg):
'''
run geocodeSlc according to parameters in cfg dict
'''
# pull parameters from cfg
input_hdf5 = cfg['InputFileGroup']['InputFilePath']
output_hdf5 = cfg['ProductPathGroup']['SASOutputFile']
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
geogrids = cfg['processing']['geocode']['geogrids']
dem_file = cfg['DynamicAncillaryFileGroup']['DEMFile']
threshold_geo2rdr = cfg['processing']['geo2rdr']['threshold']
iteration_geo2rdr = cfg['processing']['geo2rdr']['maxiter']
lines_per_block = cfg['processing']['blocksize']['y']
dem_block_margin = cfg['processing']['dem_margin']
flatten = cfg['processing']['flatten']
# init parameters shared by frequency A and B
slc = SLC(hdf5file=input_hdf5)
orbit = slc.getOrbit()
dem_raster = isce3.io.Raster(dem_file)
epsg = dem_raster.get_epsg()
proj = isce3.core.make_projection(epsg)
ellipsoid = proj.ellipsoid
# Doppler of the image grid (Zero for NISAR)
image_grid_doppler = isce3.core.LUT2d()
info_channel = journal.info("gslc.run")
info_channel.log("starting geocode SLC")
t_all = time.time()
with h5py.File(output_hdf5, 'a') as dst_h5:
for freq in freq_pols.keys():
frequency = f"frequency{freq}"
pol_list = freq_pols[freq]
radar_grid = slc.getRadarGrid(freq)
geo_grid = geogrids[freq]
# get doppler centroid
native_doppler = slc.getDopplerCentroid(frequency=freq)
for polarization in pol_list:
t_pol = time.time()
output_dir = os.path.dirname(os.path.abspath(output_hdf5))
os.makedirs(output_dir, exist_ok=True)
raster_ref = f'HDF5:{input_hdf5}:/{slc.slcPath(freq, polarization)}'
slc_raster = isce3.io.Raster(raster_ref)
# access the HDF5 dataset for a given frequency and polarization
dataset_path = f'/science/LSAR/GSLC/grids/{frequency}/{polarization}'
gslc_dataset = dst_h5[dataset_path]
# Construct the output ratster directly from HDF5 dataset
gslc_raster = isce3.io.Raster(
f"IH5:::ID={gslc_dataset.id.id}".encode("utf-8"),
update=True)
# run geocodeSlc
isce3.geocode.geocode_slc(gslc_raster, slc_raster, dem_raster,
radar_grid, geo_grid, orbit,
native_doppler, image_grid_doppler,
ellipsoid, threshold_geo2rdr,
iteration_geo2rdr, lines_per_block,
dem_block_margin, flatten)
# the rasters need to be deleted
del gslc_raster
del slc_raster
t_pol_elapsed = time.time() - t_pol
info_channel.log(
f'polarization {polarization} ran in {t_pol_elapsed:.3f} seconds'
)
t_all_elapsed = time.time() - t_all
info_channel.log(
f"successfully ran geocode SLC in {t_all_elapsed:.3f} seconds")
def run(cfg):
'''
run resample_slc
'''
input_hdf5 = cfg['InputFileGroup']['SecondaryFilePath']
scratch_path = pathlib.Path(cfg['ProductPathGroup']['ScratchPath'])
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
resamp_args = cfg['processing']['resample']
# Get SLC parameters
slc = SLC(hdf5file=input_hdf5)
info_channel = journal.info('resample_slc.run')
info_channel.log('starting resampling SLC')
# Check if use GPU or CPU resampling
use_gpu = gpu_check.use_gpu(cfg['worker']['gpu_enabled'],
cfg['worker']['gpu_id'])
if use_gpu:
# Set current CUDA device
device = isce3.cuda.core.Device(cfg['worker']['gpu_id'])
isce3.cuda.core.set_device(device)
t_all = time.time()
for freq in freq_pols.keys():
# Get frequency specific parameters
radar_grid = slc.getRadarGrid(frequency=freq)
native_doppler = slc.getDopplerCentroid(frequency=freq)
# create separate directory within scratch for resample_slc
resample_slc_scratch_path = scratch_path / 'resample_slc' / f'freq{freq}'
resample_slc_scratch_path.mkdir(parents=True, exist_ok=True)
# Initialize CPU or GPU resample object accordingly
if use_gpu:
Resamp = isce3.cuda.image.ResampSlc
else:
Resamp = isce3.image.ResampSlc
resamp_obj = Resamp(radar_grid, native_doppler, radar_grid.wavelength)
# If lines per tile is > 0, assign it to resamp_obj
if resamp_args['lines_per_tile']:
resamp_obj.lines_per_tile = resamp_args['lines_per_tile']
# Open offsets
offset_dir = pathlib.Path(cfg['processing']['resample']['offset_dir'])
geo2rdr_off_path = offset_dir / 'geo2rdr' / f'freq{freq}'
# Open offsets
rg_off = isce3.io.Raster(str(geo2rdr_off_path / 'range.off'))
az_off = isce3.io.Raster(str(geo2rdr_off_path / 'azimuth.off'))
# Get polarization list to process
pol_list = freq_pols[freq]
for pol in pol_list:
# Create directory for each polarization
out_dir = resample_slc_scratch_path / pol
out_dir.mkdir(parents=True, exist_ok=True)
out_path = out_dir / 'coregistered_secondary.slc'
# Extract and create raster of SLC to resample
h5_ds = f'//science/LSAR/SLC/swaths/frequency{freq}/{pol}'
raster_path = f'HDF5:{input_hdf5}:{h5_ds}'
raster = isce3.io.Raster(raster_path)
# Create output raster
resamp_slc = isce3.io.Raster(str(out_path), rg_off.width,
rg_off.length, rg_off.num_bands,
gdal.GDT_CFloat32, 'ENVI')
resamp_obj.resamp(raster, resamp_slc, rg_off, az_off)
t_all_elapsed = time.time() - t_all
info_channel.log(
f"successfully ran resample in {t_all_elapsed:.3f} seconds")
