def main(opts):
"""
crossmul
"""
# prepare input rasters
masterSlc = SLC(hdf5file=opts.master)
masterSlcDataset = masterSlc.getSlcDataset(opts.frequency,
opts.polarization)
masterSlcRaster = Raster('', h5=masterSlcDataset)
slaveSlc = SLC(hdf5file=opts.slave)
slaveSlcDataset = slaveSlc.getSlcDataset(opts.frequency, opts.polarization)
slaveSlcRaster = Raster('', h5=slaveSlcDataset)
# prepare mulitlooked interferogram dimensions
masterGrid = masterSlc.getRadarGrid(opts.frequency)
length = int(masterGrid.length / opts.alks)
width = int(masterGrid.width / opts.rlks)
# init output directory(s)
initDir(opts.intPathAndPrefix)
initDir(opts.cohPathAndPrefix)
# prepare output rasters
driver = gdal.GetDriverByName('ISCE')
igramDataset = driver.Create(opts.intPathAndPrefix, width, length, 1,
gdal.GDT_CFloat32)
igramRaster = Raster('', dataset=igramDataset)
cohDataset = driver.Create(opts.cohPathAndPrefix, width, length, 1,
gdal.GDT_Float32)
cohRaster = Raster('', dataset=cohDataset)
# prepare optional rasters
if opts.rgoff:
rgOffRaster = Raster(opts.rgoff)
else:
rgOffRaster = None
if opts.azband:
dopMaster = masterSlc.getDopplerCentroid()
dopSlave = slaveSlc.getDopplerCentroid()
prf = dopMaster.getRadarGrid(opts.frequency).prf
azimuthBandwidth = opts.azband
else:
dopMaster = dopSlave = None
prf = azimuthBandwidth = 0.0
crossmul = Crossmul()
crossmul.crossmul(masterSlcRaster,
slaveSlcRaster,
igramRaster,
cohRaster,
rngOffset=rgOffRaster,
refDoppler=dopMaster,
secDoppler=dopSlave,
rangeLooks=opts.rlks,
azimuthLooks=opts.alks,
prf=prf,
azimuthBandwidth=azimuthBandwidth)
def main(opts):
"""
resample SLC
"""
# prep SLC dataset input
productSlc = SLC(hdf5file=opts.product)
# get grids needed for resamp object instantiation
productGrid = productSlc.getRadarGrid(opts.frequency)
# instantiate resamp object based on user input
if 'cuda' not in dir(isce3) and opts.gpu:
warnings.warn('CUDA resamp not available. Switching to CPU resamp')
opts.gpu = False
if opts.gpu:
resamp = isce3.cuda.image.resampSlc(
radarGrid=productGrid,
doppler=productSlc.getDopplerCentroid(),
wavelength=productGrid.wavelength)
else:
resamp = isce3.image.resampSlc(radarGrid=productGrid,
doppler=productSlc.getDopplerCentroid(),
wavelength=productGrid.wavelength)
# set number of lines per tile if arg > 0
if opts.linesPerTile:
resamp.linesPerTile = opts.linesPerTile
# Prepare input rasters
inSlcDataset = productSlc.getSlcDataset(opts.frequency, opts.polarization)
inSlcRaster = isce3.io.raster(filename='', h5=inSlcDataset)
azOffsetRaster = isce3.io.raster(
filename=os.path.join(opts.offsetdir, 'azimuth.off'))
rgOffsetRaster = isce3.io.raster(
filename=os.path.join(opts.offsetdir, 'range.off'))
# Init output directory
if opts.outFilePath:
path, _ = os.path.split(opts.outFilePath)
os.makedirs(path, exist_ok=True)
# Prepare output raster
driver = gdal.GetDriverByName('ISCE')
outds = driver.Create(opts.outFilePath, rgOffsetRaster.width,
rgOffsetRaster.length, 1, gdal.GDT_CFloat32)
outSlcRaster = isce3.io.raster(filename='', dataset=outds)
# Run resamp
resamp.resamp(inSlc=inSlcRaster,
outSlc=outSlcRaster,
rgoffRaster=rgOffsetRaster,
azoffRaster=azOffsetRaster)
def test_run():
'''
check if resamp runs
'''
# init params
h5_path = os.path.join(iscetest.data, "envisat.h5")
grid = isce3.product.RadarGridParameters(h5_path)
slc = SLC(hdf5file=h5_path)
# init resamp obj
resamp = isce3.image.ResampSlc(grid, slc.getDopplerCentroid())
resamp.lines_per_tile = 249
# prepare rasters
h5_ds = f'//science/LSAR/SLC/swaths/frequencyA/HH'
raster_ref = f'HDF5:{h5_path}:{h5_ds}'
input_slc = isce3.io.Raster(raster_ref)
az_off_raster = isce3.io.Raster(
os.path.join(iscetest.data, "offsets/azimuth.off"))
rg_off_raster = isce3.io.Raster(
os.path.join(iscetest.data, "offsets/range.off"))
output_slc = isce3.io.Raster('warped.slc', rg_off_raster.width,
rg_off_raster.length, rg_off_raster.num_bands,
gdal.GDT_CFloat32, 'ENVI')
# run resamp
resamp.resamp(input_slc, output_slc, rg_off_raster, az_off_raster)
def test_run_raster_layers():
'''
check if topo runs
'''
# prepare Rdr2Geo init params
h5_path = os.path.join(iscetest.data, "envisat.h5")
radargrid = isce3.product.RadarGridParameters(h5_path)
slc = SLC(hdf5file=h5_path)
orbit = slc.getOrbit()
doppler = slc.getDopplerCentroid()
ellipsoid = isce3.core.Ellipsoid()
# init Rdr2Geo class
rdr2geo_obj = isce3.geometry.Rdr2Geo(radargrid, orbit, ellipsoid, doppler)
# load test DEM
dem_raster = isce3.io.Raster(
os.path.join(iscetest.data, "srtm_cropped.tif"))
x_raster = isce3.io.Raster("x.rdr", radargrid.width, radargrid.length, 1,
gdal.GDT_Float64, 'ENVI')
y_raster = isce3.io.Raster("y.rdr", radargrid.width, radargrid.length, 1,
gdal.GDT_Float64, 'ENVI')
height_raster = isce3.io.Raster("z.rdr", radargrid.width, radargrid.length,
1, gdal.GDT_Float64, 'ENVI')
incidence_angle_raster = isce3.io.Raster("inc.rdr", radargrid.width,
radargrid.length, 1,
gdal.GDT_Float32, 'ENVI')
heading_angle_raster = isce3.io.Raster("hgd.rdr", radargrid.width,
radargrid.length, 1,
gdal.GDT_Float32, 'ENVI')
local_incidence_angle_raster = isce3.io.Raster("localInc.rdr",
radargrid.width,
radargrid.length, 1,
gdal.GDT_Float32, 'ENVI')
local_Psi_raster = isce3.io.Raster("localPsi.rdr", radargrid.width,
radargrid.length, 1, gdal.GDT_Float32,
'ENVI')
simulated_amplitude_raster = isce3.io.Raster("simamp.rdr", radargrid.width,
radargrid.length, 1,
gdal.GDT_Float32, 'ENVI')
layover_shadow_raster = isce3.io.Raster("layoverShadowMask.rdr",
radargrid.width, radargrid.length,
1, gdal.GDT_Float32, 'ENVI')
# run
rdr2geo_obj.topo(dem_raster, x_raster, y_raster, height_raster,
incidence_angle_raster, heading_angle_raster,
local_incidence_angle_raster, local_Psi_raster,
simulated_amplitude_raster, layover_shadow_raster)
topo_raster = isce3.io.Raster(
"topo_layers.vrt",
raster_list=[
x_raster, y_raster, height_raster, incidence_angle_raster,
heading_angle_raster, local_incidence_angle_raster,
local_Psi_raster, simulated_amplitude_raster
])
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 = isce3.core.DateTime("2003-02-26T17:55:22.976222")
r = 826988.6900674499
h = 1777.
dem = isce3.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 = isce3.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 = isce3.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 test_run():
# load parameters shared across all test runs
# init geocode object and populate members
rslc = SLC(hdf5file=os.path.join(iscetest.data, "envisat.h5"))
orbit = rslc.getOrbit()
native_doppler = rslc.getDopplerCentroid()
native_doppler.bounds_error = False
grid_doppler = native_doppler
threshold_geo2rdr = 1e-8
numiter_geo2rdr = 25
delta_range = 1e-6
epsg = 4326
# get radar grid from HDF5
radar_grid = isce3.product.RadarGridParameters(
os.path.join(iscetest.data, "envisat.h5"))
radar_grid = radar_grid[::10, ::10]
heights = [0.0, 1000.0]
output_h5 = 'envisat_geolocation_cube.h5'
fid = h5py.File(output_h5, 'w')
cube_group_name = '/science/LSAR/SLC/metadata/radarGrid'
add_geolocation_grid_cubes_to_hdf5(fid, cube_group_name, radar_grid,
heights, orbit, native_doppler,
grid_doppler, epsg, threshold_geo2rdr,
numiter_geo2rdr, delta_range)
print('saved file:', output_h5)
def test_run():
'''
run geocodeSlc bindings with same parameters as C++ test to make sure it does not crash
'''
# load h5 for doppler and orbit
rslc = SLC(hdf5file=os.path.join(iscetest.data, "envisat.h5"))
# define geogrid
geogrid = isce.product.GeoGridParameters(start_x=-115.65,
start_y=34.84,
spacing_x=0.0002,
spacing_y=-8.0e-5,
width=500,
length=500,
epsg=4326)
# define geotransform
geotrans = [
geogrid.start_x, geogrid.spacing_x, 0.0, geogrid.start_y, 0.0,
geogrid.spacing_y
]
img_doppler = rslc.getDopplerCentroid()
native_doppler = isce.core.LUT2d(img_doppler.x_start, img_doppler.y_start,
img_doppler.x_spacing,
img_doppler.y_spacing,
np.zeros((geogrid.length, geogrid.width)))
dem_raster = isce.io.Raster(
os.path.join(iscetest.data, "geocode/zeroHeightDEM.geo"))
radargrid = isce.product.RadarGridParameters(
os.path.join(iscetest.data, "envisat.h5"))
# geocode same 2 rasters as C++ version
for xy in ['x', 'y']:
out_raster = isce.io.Raster(f"{xy}.geo", geogrid.width, geogrid.length,
1, gdal.GDT_CFloat32, "ENVI")
in_raster = isce.io.Raster(
os.path.join(iscetest.data, f"geocodeslc/{xy}.slc"))
isce.geocode.geocode_slc(output_raster=out_raster,
input_raster=in_raster,
dem_raster=dem_raster,
radargrid=radargrid,
geogrid=geogrid,
orbit=rslc.getOrbit(),
native_doppler=native_doppler,
image_grid_doppler=img_doppler,
ellipsoid=isce.core.Ellipsoid(),
threshold_geo2rdr=1.0e-9,
numiter_geo2rdr=25,
lines_per_block=1000,
dem_block_margin=0.1,
flatten=False)
out_raster.set_geotransform(geotrans)
def test_cuda_geocode():
rslc = SLC(hdf5file=os.path.join(iscetest.data, "envisat.h5"))
dem_raster = isce3.io.Raster(
os.path.join(iscetest.data, "geocode/zeroHeightDEM.geo"))
dem_margin = 0.1
# define geogrid
epsg = 4326
geogrid = isce3.product.GeoGridParameters(start_x=-115.65,
start_y=34.84,
spacing_x=0.0002,
spacing_y=-8.0e-5,
width=500,
length=500,
epsg=epsg)
geotrans = [
geogrid.start_x, geogrid.spacing_x, 0.0, geogrid.start_y, 0.0,
geogrid.spacing_y
]
# init RadarGeometry, orbit, and doppler from RSLC
radargrid = isce3.product.RadarGridParameters(
os.path.join(iscetest.data, "envisat.h5"))
orbit = rslc.getOrbit()
doppler = rslc.getDopplerCentroid()
rdr_geometry = isce3.container.RadarGeometry(radargrid, orbit, doppler)
# set interp method
interp_method = isce3.core.DataInterpMethod.BILINEAR
# init CUDA geocode obj
for xy, suffix in itertools.product(['x', 'y'], ['', '_blocked']):
lines_per_block = 126 if suffix else 1000
cu_geocode = isce3.cuda.geocode.Geocode(geogrid, rdr_geometry,
dem_raster, dem_margin,
lines_per_block, interp_method)
output_raster = isce3.io.Raster(f"{xy}{suffix}.geo", geogrid.width,
geogrid.length, 1, gdal.GDT_CFloat32,
"ENVI")
input_raster = isce3.io.Raster(
os.path.join(iscetest.data, f"geocodeslc/{xy}.slc"))
for i in range(cu_geocode.n_blocks):
cu_geocode.set_block_radar_coord_grid(i)
cu_geocode.geocode_raster_block(output_raster, input_raster)
output_raster.set_geotransform(geotrans)
def test_run():
'''
check if geo2rdr runs
'''
# prepare Geo2Rdr init params
h5_path = os.path.join(iscetest.data, "envisat.h5")
radargrid = isce.product.RadarGridParameters(h5_path)
slc = SLC(hdf5file=h5_path)
orbit = slc.getOrbit()
doppler = slc.getDopplerCentroid()
ellipsoid = isce.core.Ellipsoid()
# require geolocation accurate to one millionth of a pixel.
tol_pixels = 1e-6
tol_seconds = tol_pixels / radargrid.prf
# init Geo2Rdr class
geo2rdr_obj = isce.cuda.geometry.Geo2Rdr(radargrid,
orbit,
ellipsoid,
doppler,
threshold=tol_seconds,
numiter=50)
# load rdr2geo unit test output
rdr2geo_raster = isce.io.Raster("topo.vrt")
# run
geo2rdr_obj.geo2rdr(rdr2geo_raster, ".")
# list of test outputs
test_outputs = ["range.off", "azimuth.off"]
# check each generated raster
for test_output in test_outputs:
# load dataset and get array
test_ds = gdal.Open(test_output, gdal.GA_ReadOnly)
test_arr = test_ds.GetRasterBand(1).ReadAsArray()
# mask bad values
test_arr = np.ma.masked_array(test_arr, mask=np.abs(test_arr) > 999.0)
# compute max error (in pixels)
test_err = np.max(np.abs(test_arr))
# Error may slightly exceed tolerance since Newton step size isn't a
# perfect estimate of the error in the solution.
assert (test_err <
2 * tol_pixels), f"{test_output} accumulated error fail"
def test_run():
# load parameters shared across all test runs
# init geocode object and populate members
rslc = SLC(hdf5file=os.path.join(iscetest.data, "envisat.h5"))
geo_obj = isce.geocode.GeocodeFloat64()
geo_obj.orbit = rslc.getOrbit()
geo_obj.doppler = rslc.getDopplerCentroid()
geo_obj.ellipsoid = isce.core.Ellipsoid()
geo_obj.threshold_geo2rdr = 1e-9
geo_obj.numiter_geo2rdr = 25
geo_obj.lines_per_block = 1000
geo_obj.dem_block_margin = 1e-1
geo_obj.radar_block_margin = 10
geo_obj.data_interpolator = 'biquintic'
# prepare geogrid
geogrid_start_x = -115.6
geogrid_start_y = 34.832
reduction_factor = 10
geogrid_spacingX = reduction_factor * 0.0002
geogrid_spacingY = reduction_factor * -8.0e-5
geo_grid_length = int(380 / reduction_factor)
geo_grid_width = int(400 / reduction_factor)
epsgcode = 4326
geo_obj.geogrid(geogrid_start_x, geogrid_start_y, geogrid_spacingX,
geogrid_spacingY, geo_grid_width, geo_grid_length, epsgcode)
# get radar grid from HDF5
radar_grid = isce.product.RadarGridParameters(os.path.join(iscetest.data, "envisat.h5"))
# load test DEM
dem_raster = isce.io.Raster(os.path.join(iscetest.data, "geocode/zeroHeightDEM.geo"))
# iterate thru axis
for axis in input_axis:
# load axis input raster
input_raster = isce.io.Raster(os.path.join(iscetest.data, f"geocode/{axis}.rdr"))
# iterate thru geocode modes
for key, value in geocode_modes.items():
# prepare output raster
output_path = f"{axis}_{key}.geo"
output_raster = isce.io.Raster(output_path,
geo_grid_width, geo_grid_length, 1,
gdal.GDT_Float64, "ENVI")
# geocode based on axis and mode
geo_obj.geocode(radar_grid,
input_raster,
output_raster,
dem_raster,
value)
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.avg_lut2d_to_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 add_radar_grid_cube(cfg, freq, radar_grid, orbit, dst_h5):
''' Write radar grid cube to HDF5
Parameters
----------
cfg : dict
Dictionary containing run configuration
freq : str
Frequency in HDF5 to add cube to
radar_grid : isce3.product.radar_grid
Radar grid of current frequency of datasets other than offset and shadow
layover datasets
orbit : isce3.core.orbit
Orbit object of current SLC
dst_h5: str
Path to output GUNW HDF5
'''
radar_grid_cubes_geogrid = cfg['processing']['radar_grid_cubes']['geogrid']
radar_grid_cubes_heights = cfg['processing']['radar_grid_cubes']['heights']
threshold_geo2rdr = cfg["processing"]["geo2rdr"]["threshold"]
iteration_geo2rdr = cfg["processing"]["geo2rdr"]["maxiter"]
ref_hdf5 = cfg["input_file_group"]["input_file_path"]
slc = SLC(hdf5file=ref_hdf5)
# get doppler centroid
cube_geogrid_param = isce3.product.GeoGridParameters(
start_x=radar_grid_cubes_geogrid.start_x,
start_y=radar_grid_cubes_geogrid.start_y,
spacing_x=radar_grid_cubes_geogrid.spacing_x,
spacing_y=radar_grid_cubes_geogrid.spacing_y,
width=int(radar_grid_cubes_geogrid.width),
length=int(radar_grid_cubes_geogrid.length),
epsg=radar_grid_cubes_geogrid.epsg)
cube_group_path = '/science/LSAR/GUNW/metadata/radarGrid'
native_doppler = slc.getDopplerCentroid(frequency=freq)
grid_zero_doppler = isce3.core.LUT2d()
'''
The native-Doppler LUT bounds error is turned off to
computer cubes values outside radar-grid boundaries
'''
native_doppler.bounds_error = False
add_radar_grid_cubes_to_hdf5(dst_h5, cube_group_path,
cube_geogrid_param, radar_grid_cubes_heights,
radar_grid, orbit, native_doppler,
grid_zero_doppler, threshold_geo2rdr,
iteration_geo2rdr)
def test_point():
h5_path = os.path.join(iscetest.data, "envisat.h5")
radargrid = isce3.product.RadarGridParameters(h5_path)
slc = SLC(hdf5file=h5_path)
orbit = slc.getOrbit()
doppler = slc.getDopplerCentroid()
ellipsoid = isce3.core.Ellipsoid()
llh = np.array([
np.deg2rad(-115.72466801139711),
np.deg2rad(34.65846532785868), 1772.0
])
# run with native doppler
aztime, slant_range = isce3.geometry.geo2rdr(llh,
ellipsoid,
orbit,
doppler,
radargrid.wavelength,
radargrid.lookside,
threshold=1.0e-10,
maxiter=50,
delta_range=10.0)
azdate = orbit.reference_epoch + isce3.core.TimeDelta(aztime)
assert azdate.isoformat() == "2003-02-26T17:55:33.993088889"
npt.assert_almost_equal(slant_range, 830450.1859446081, decimal=6)
# run again with zero doppler
doppler = isce3.core.LUT2d()
aztime, slant_range = isce3.geometry.geo2rdr(llh,
ellipsoid,
orbit,
doppler,
radargrid.wavelength,
radargrid.lookside,
threshold=1.0e-10,
maxiter=50,
delta_range=10.0)
azdate = orbit.reference_epoch + isce3.core.TimeDelta(aztime)
assert azdate.isoformat() == "2003-02-26T17:55:34.122893704"
npt.assert_almost_equal(slant_range, 830449.6727720434, decimal=6)
def main(opts):
#instantiate slc object from NISAR SLC class
slc = SLC(hdf5file=opts.product)
# extract orbit
orbit = slc.getOrbit()
# extract the radar grid parameters
radarGrid = slc.getRadarGrid()
# construct ellipsoid which is by default WGS84
ellipsoid = isce3.core.ellipsoid()
# get doppler centroid
doppler = slc.getDopplerCentroid()
# instantiate geo2rdr object based on user input
if 'cuda' not in dir(isce3) and opts.gpu:
warnings.warn('CUDA geo2rdr not available. Switching to CPU geo2rdr')
opts.gpu = False
if opts.gpu:
geo2rdrObj = isce3.cuda.geometry.geo2rdr(radarGrid=radarGrid,
orbit=orbit,
ellipsoid=ellipsoid,
doppler=doppler,
threshold=1e-9)
else:
geo2rdrObj = isce3.geometry.geo2rdr(radarGrid=radarGrid,
orbit=orbit,
ellipsoid=ellipsoid,
doppler=doppler,
threshold=1e-9)
# Read topo multiband raster
topoRaster = isce3.io.raster(filename=opts.topopath)
# Init output directory
os.makedirs(opts.outdir, exist_ok=True)
# Run geo2rdr
geo2rdrObj.geo2rdr(topoRaster,
outputDir=opts.outdir,
azshift=opts.azoff,
rgshift=opts.rgoff)
def main(opts):
#instantiate slc object from NISAR SLC class
slc = SLC(hdf5file=opts.product)
# extract orbit
orbit = slc.getOrbit()
# extract the radar grid parameters
radarGrid = slc.getRadarGrid()
# construct ellipsoid which is by default WGS84
ellipsoid = isce3.core.ellipsoid()
# get doppler centroid
doppler = slc.getDopplerCentroid()
# instantiate rdr2geo object based on user input
if 'cuda' not in dir(isce3) and opts.gpu:
warnings.warn('CUDA rdr2geo not available. Switching to CPU rdr2geo')
opts.gpu = False
if opts.gpu:
rdr2geo = isce3.cuda.geometry.rdr2geo(radarGrid=radarGrid,
orbit=orbit,
ellipsoid=ellipsoid,
computeMask=opts.mask,
doppler=doppler)
else:
rdr2geo = isce3.geometry.rdr2geo(radarGrid=radarGrid,
orbit=orbit,
ellipsoid=ellipsoid,
computeMask=opts.mask,
doppler=doppler)
# Read DEM raster
demRaster = isce3.io.raster(filename=opts.dem)
# Init output directory
os.makedirs(opts.outdir, exist_ok=True)
# Run rdr2geo
rdr2geo.topo(demRaster, outputDir=opts.outdir)
return 0
def main(opts):
"""
resample SLC
"""
# prep SLC dataset input
productSlc = SLC(hdf5file=opts.product)
# get grids needed for resamp object instantiation
productGrid = productSlc.getRadarGrid(opts.frequency)
# instantiate resamp object
resamp = ResampSlc(productGrid, productSlc.getDopplerCentroid(),
productGrid.wavelength)
# set number of lines per tile if arg > 0
if opts.linesPerTile:
resamp.linesPerTile = opts.linesPerTile
# Prepare input rasters
inSlcDataset = productSlc.getSlcDataset(opts.frequency, opts.polarization)
inSlcRaster = Raster('', h5=inSlcDataset)
azOffsetRaster = Raster(
filename=os.path.join(opts.offsetdir, 'azimuth.off'))
rgOffsetRaster = Raster(filename=os.path.join(opts.offsetdir, 'range.off'))
# Init output directory
if opts.outPathAndFile:
path, file = os.path.split(opts.outPathAndFile)
if not os.path.isdir(path):
os.makedirs(path)
# Prepare output raster
driver = gdal.GetDriverByName('ISCE')
slcPathAndName = opts.outPathAndFile
outds = driver.Create(os.path.join(slcPathAndName), rgOffsetRaster.width,
rgOffsetRaster.length, 1, gdal.GDT_CFloat32)
outSlcRaster = Raster('', dataset=outds)
# Run resamp
resamp.resamp(inSlc=inSlcRaster,
outSlc=outSlcRaster,
rgoffRaster=rgOffsetRaster,
azoffRaster=azOffsetRaster)
def test_run():
# load parameters shared across all test runs
# init geocode object and populate members
rslc = SLC(hdf5file=os.path.join(iscetest.data, "envisat.h5"))
orbit = rslc.getOrbit()
native_doppler = rslc.getDopplerCentroid()
native_doppler.bounds_error = False
grid_doppler = native_doppler
threshold_geo2rdr = 1e-8
numiter_geo2rdr = 25
delta_range = 1e-8
# prepare geogrid
geogrid = isce3.product.GeoGridParameters(start_x=-115.65,
start_y=34.84,
spacing_x=0.0002,
spacing_y=-8.0e-5,
width=500,
length=500,
epsg=4326)
# get radar grid from HDF5
radar_grid = isce3.product.RadarGridParameters(
os.path.join(iscetest.data, "envisat.h5"))
heights = [0.0, 1000.0]
output_h5 = 'envisat_radar_grid_cube.h5'
fid = h5py.File(output_h5, 'w')
cube_group_name = '/science/LSAR/GCOV/metadata/radarGrid'
add_radar_grid_cubes_to_hdf5(fid, cube_group_name, geogrid, heights,
radar_grid, orbit, native_doppler,
grid_doppler, threshold_geo2rdr,
numiter_geo2rdr, delta_range)
print('saved file:', output_h5)
def test_run():
'''
check if topo runs
'''
# prepare Rdr2Geo init params
h5_path = os.path.join(iscetest.data, "envisat.h5")
radargrid = isce3.product.RadarGridParameters(h5_path)
slc = SLC(hdf5file=h5_path)
orbit = slc.getOrbit()
doppler = slc.getDopplerCentroid()
ellipsoid = isce3.core.Ellipsoid()
# init Rdr2Geo class
rdr2geo_obj = isce3.cuda.geometry.Rdr2Geo(radargrid, orbit,
ellipsoid, doppler)
# load test DEM
dem_raster = isce3.io.Raster(os.path.join(iscetest.data, "srtm_cropped.tif"))
# run
rdr2geo_obj.topo(dem_raster, ".")
def run(cfg):
'''
run geocodeSlc according to parameters in cfg dict
'''
# pull parameters from cfg
input_hdf5 = cfg['input_file_group']['input_file_path']
output_hdf5 = cfg['product_path_group']['sas_output_file']
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
geogrids = cfg['processing']['geocode']['geogrids']
radar_grid_cubes_geogrid = cfg['processing']['radar_grid_cubes']['geogrid']
radar_grid_cubes_heights = cfg['processing']['radar_grid_cubes']['heights']
dem_file = cfg['dynamic_ancillary_file_group']['dem_file']
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
# output_raster_ref = f'HDF5:{output_hdf5}:/{dataset_path}'
gslc_raster = isce3.io.Raster(
f"IH5:::ID={gslc_dataset.id.id}".encode("utf-8"))
compute_stats_complex_data(gslc_raster, gslc_dataset)
t_pol_elapsed = time.time() - t_pol
info_channel.log(
f'polarization {polarization} ran in {t_pol_elapsed:.3f} seconds'
)
if freq.upper() == 'B':
continue
cube_geogrid = isce3.product.GeoGridParameters(
start_x=radar_grid_cubes_geogrid.start_x,
start_y=radar_grid_cubes_geogrid.start_y,
spacing_x=radar_grid_cubes_geogrid.spacing_x,
spacing_y=radar_grid_cubes_geogrid.spacing_y,
width=int(radar_grid_cubes_geogrid.width),
length=int(radar_grid_cubes_geogrid.length),
epsg=radar_grid_cubes_geogrid.epsg)
cube_group_name = '/science/LSAR/GSLC/metadata/radarGrid'
native_doppler.bounds_error = False
'''
The native-Doppler LUT bounds error is turned off to
computer cubes values outside radar-grid boundaries
'''
add_radar_grid_cubes_to_hdf5(dst_h5, cube_group_name, cube_geogrid,
radar_grid_cubes_heights, radar_grid,
orbit, native_doppler,
image_grid_doppler, threshold_geo2rdr,
iteration_geo2rdr)
t_all_elapsed = time.time() - t_all
info_channel.log(
f"successfully ran geocode SLC in {t_all_elapsed:.3f} seconds")
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()
# set input parameters
input_terrain_radiometry = isce3.geometry.RtcInputTerrainRadiometry.BETA_NAUGHT
output_terrain_radiometry = isce3.geometry.RtcOutputTerrainRadiometry.GAMMA_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_BILINEAR_DISTRIBUTION
and radar_grid_str == 'cropped'):
continue
elif (rtc_algorithm ==
isce3.geometry.RtcAlgorithm.RTC_BILINEAR_DISTRIBUTION):
filename = './rtc_bilinear_distribution_' + 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.compute_rtc(radar_grid, orbit, doppler, dem_obj,
out_raster, input_terrain_radiometry,
output_terrain_radiometry,
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_BILINEAR_DISTRIBUTION
and radar_grid_str == 'cropped'):
continue
elif (rtc_algorithm ==
isce3.geometry.RtcAlgorithm.RTC_BILINEAR_DISTRIBUTION):
max_rmse = 0.7
filename = './rtc_bilinear_distribution_' + 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 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")
def run(cfg, resample_type):
'''
run resample_slc
'''
input_hdf5 = cfg['input_file_group']['secondary_file_path']
scratch_path = pathlib.Path(cfg['product_path_group']['scratch_path'])
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
# According to the type of resampling, choose proper resample cfg
resamp_args = cfg['processing'][f'{resample_type}_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 = isce3.core.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)
# Open offsets
offsets_dir = pathlib.Path(resamp_args['offsets_dir'])
# Create separate directories for coarse and fine resample
# Open corresponding range/azimuth offsets
resample_slc_scratch_path = scratch_path / \
f'{resample_type}_resample_slc' / f'freq{freq}'
if resample_type == 'coarse':
offsets_path = offsets_dir / 'geo2rdr' / f'freq{freq}'
rg_off = isce3.io.Raster(str(offsets_path / 'range.off'))
az_off = isce3.io.Raster(str(offsets_path / 'azimuth.off'))
else:
# We checked the existence of HH/VV offsets in resample_slc_runconfig.py
# Select the first offsets available between HH and VV
freq_offsets_path = offsets_dir / 'rubbersheet_offsets' / f'freq{freq}'
if os.path.isdir(str(freq_offsets_path / 'HH')):
offsets_path = freq_offsets_path / 'HH'
else:
offsets_path = freq_offsets_path / 'VV'
rg_off = isce3.io.Raster(str(offsets_path / 'range.off.vrt'))
az_off = isce3.io.Raster(str(offsets_path / 'azimuth.off.vrt'))
# Create resample slc directory
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)
# 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']
# Get polarization list for which resample SLCs
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'/{slc.SwathPath}/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")
def doppler(self):
tempSlc = SLC(hdf5file=self.state.input_hdf5)
dop = tempSlc.getDopplerCentroid()
return dop
def runGeocodeSLC(self):
self._print('starting geocode module')
state = self.state
time_id = str(time.time())
orbit = self.orbit
dem_raster = isce3.io.raster(filename=state.dem_file)
# construct ellipsoid which is by default WGS84
ellipsoid = isce3.core.ellipsoid()
slc = SLC(hdf5file=self.state.input_hdf5)
for freq in state.subset_dict.keys():
frequency = "frequency{}".format(freq)
pol_list = state.subset_dict[freq]
radar_grid = self.radar_grid_list[freq]
geo_grid = self.geogrid_dict[frequency]
for polarization in pol_list:
self._print(
f'working on frequency: {freq}, polarization: {polarization}')
# get doppler centroid
native_doppler = slc.getDopplerCentroid(frequency=freq)
# Doppler of the image grid (Zero for NISAR)
image_grid_doppler = isce3.core.lut2d()
output_dir = os.path.dirname(
os.path.abspath(self.state.output_hdf5))
os.makedirs(output_dir, exist_ok=True)
slc_dataset = self.slc_obj.getSlcDataset(freq, polarization)
slc_raster = isce3.io.raster(filename='', h5=slc_dataset)
# access the HDF5 dataset for a given frequency and polarization
dst_h5 = h5py.File(state.output_hdf5, 'a')
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(filename='',
h5=gslc_dataset,
access=gdal.GA_Update)
# This whole section requires better sanity check and handling defaults
threshold_geo2rdr = self.userconfig['runconfig']['groups'][
'processing']['geo2rdr']['threshold']
iteration_geo2rdr = self.userconfig['runconfig']['groups'][
'processing']['geo2rdr']['maxiter']
lines_per_block = self.userconfig['runconfig']['groups'][
'processing']['blocksize']['y']
dem_block_margin = self.userconfig['runconfig']['groups'][
'processing']['dem_margin']
flatten = self.userconfig['runconfig']['groups']['processing'][
'flatten']
# this may not be the best way. needs to be revised
if flatten:
self._print("flattening is True")
else:
self._print("flattening is False")
if np.isnan(threshold_geo2rdr):
threshold_geo2rdr = 1.0e-9
if np.isnan(iteration_geo2rdr):
iteration_geo2rdr = 25
if np.isnan(lines_per_block):
lines_per_block = 1000
if np.isnan(dem_block_margin):
dem_block_margin = 0.1
# run geocodeSlc :
isce3.geocode.geocodeSlc(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
dst_h5.close()
def run(cfg):
'''
run GCOV
'''
# pull parameters from cfg
input_hdf5 = cfg['input_file_group']['input_file_path']
output_hdf5 = cfg['product_path_group']['sas_output_file']
freq_pols = cfg['processing']['input_subset']['list_of_frequencies']
flag_fullcovariance = cfg['processing']['input_subset']['fullcovariance']
flag_symmetrize_cross_pol_channels = \
cfg['processing']['input_subset']['symmetrize_cross_pol_channels']
scratch_path = cfg['product_path_group']['scratch_path']
radar_grid_cubes_geogrid = cfg['processing']['radar_grid_cubes']['geogrid']
radar_grid_cubes_heights = cfg['processing']['radar_grid_cubes']['heights']
# DEM parameters
dem_file = cfg['dynamic_ancillary_file_group']['dem_file']
dem_margin = cfg['processing']['dem_margin']
dem_interp_method_enum = cfg['processing']['dem_interpolation_method_enum']
# unpack geocode run parameters
geocode_dict = cfg['processing']['geocode']
geocode_algorithm = geocode_dict['algorithm_type']
output_mode = geocode_dict['output_mode']
flag_apply_rtc = geocode_dict['apply_rtc']
memory_mode = geocode_dict['memory_mode']
geogrid_upsampling = geocode_dict['geogrid_upsampling']
abs_cal_factor = geocode_dict['abs_rad_cal']
clip_max = geocode_dict['clip_max']
clip_min = geocode_dict['clip_min']
geogrids = geocode_dict['geogrids']
flag_upsample_radar_grid = geocode_dict['upsample_radargrid']
flag_save_nlooks = geocode_dict['save_nlooks']
flag_save_rtc = geocode_dict['save_rtc']
flag_save_dem = geocode_dict['save_dem']
# unpack RTC run parameters
rtc_dict = cfg['processing']['rtc']
output_terrain_radiometry = rtc_dict['output_type']
rtc_algorithm = rtc_dict['algorithm_type']
input_terrain_radiometry = rtc_dict['input_terrain_radiometry']
rtc_min_value_db = rtc_dict['rtc_min_value_db']
rtc_upsampling = rtc_dict['dem_upsampling']
# unpack geo2rdr parameters
geo2rdr_dict = cfg['processing']['geo2rdr']
threshold = geo2rdr_dict['threshold']
maxiter = geo2rdr_dict['maxiter']
if (flag_apply_rtc and output_terrain_radiometry
== isce3.geometry.RtcOutputTerrainRadiometry.SIGMA_NAUGHT):
output_radiometry_str = "radar backscatter sigma0"
elif (flag_apply_rtc and output_terrain_radiometry
== isce3.geometry.RtcOutputTerrainRadiometry.GAMMA_NAUGHT):
output_radiometry_str = 'radar backscatter gamma0'
elif input_terrain_radiometry == isce3.geometry.RtcInputTerrainRadiometry.BETA_NAUGHT:
output_radiometry_str = 'radar backscatter beta0'
else:
output_radiometry_str = 'radar backscatter sigma0'
# unpack pre-processing
preprocess = cfg['processing']['pre_process']
rg_look = preprocess['range_looks']
az_look = preprocess['azimuth_looks']
radar_grid_nlooks = rg_look * az_look
# init parameters shared between frequencyA and frequencyB sub-bands
slc = SLC(hdf5file=input_hdf5)
dem_raster = isce3.io.Raster(dem_file)
zero_doppler = isce3.core.LUT2d()
epsg = dem_raster.get_epsg()
proj = isce3.core.make_projection(epsg)
ellipsoid = proj.ellipsoid
exponent = 2
info_channel = journal.info("gcov.run")
error_channel = journal.error("gcov.run")
info_channel.log("starting geocode COV")
t_all = time.time()
for frequency in freq_pols.keys():
t_freq = time.time()
# unpack frequency dependent parameters
radar_grid = slc.getRadarGrid(frequency)
if radar_grid_nlooks > 1:
radar_grid = radar_grid.multilook(az_look, rg_look)
geogrid = geogrids[frequency]
input_pol_list = freq_pols[frequency]
# do no processing if no polarizations specified for current frequency
if not input_pol_list:
continue
# set dict of input rasters
input_raster_dict = {}
# `input_pol_list` is the input list of polarizations that may include
# HV and VH. `pol_list` is the actual list of polarizations to be
# geocoded. It may include HV but it will not include VH if the
# polarimetric symmetrization is performed
pol_list = input_pol_list
for pol in pol_list:
temp_ref = \
f'HDF5:"{input_hdf5}":/{slc.slcPath(frequency, pol)}'
temp_raster = isce3.io.Raster(temp_ref)
input_raster_dict[pol] = temp_raster
# symmetrize cross-polarimetric channels (if applicable)
if (flag_symmetrize_cross_pol_channels and 'HV' in input_pol_list
and 'VH' in input_pol_list):
# create output raster
symmetrized_hv_temp = tempfile.NamedTemporaryFile(dir=scratch_path,
suffix='.tif')
# get cross-polarimetric channels from input_raster_dict
hv_raster_obj = input_raster_dict['HV']
vh_raster_obj = input_raster_dict['VH']
# create output symmetrized HV object
symmetrized_hv_obj = isce3.io.Raster(symmetrized_hv_temp.name,
hv_raster_obj.width,
hv_raster_obj.length,
hv_raster_obj.num_bands,
hv_raster_obj.datatype(),
'GTiff')
# call symmetrization function
isce3.polsar.symmetrize_cross_pol_channels(hv_raster_obj,
vh_raster_obj,
symmetrized_hv_obj)
# ensure changes are flushed to disk by closing & re-opening the
# raster.
del symmetrized_hv_obj
symmetrized_hv_obj = isce3.io.Raster(symmetrized_hv_temp.name)
# Since HV and VH were symmetrized into HV, remove VH from
# `pol_list` and `from input_raster_dict`.
pol_list.remove('VH')
input_raster_dict.pop('VH')
# Update `input_raster_dict` with the new `symmetrized_hv_obj`
input_raster_dict['HV'] = symmetrized_hv_obj
# construct input rasters
input_raster_list = []
for pol in pol_list:
input_raster_list.append(input_raster_dict[pol])
# set paths temporary files
input_temp = tempfile.NamedTemporaryFile(dir=scratch_path,
suffix='.vrt')
input_raster_obj = isce3.io.Raster(input_temp.name,
raster_list=input_raster_list)
# init Geocode object depending on raster type
if input_raster_obj.datatype() == gdal.GDT_Float32:
geo = isce3.geocode.GeocodeFloat32()
elif input_raster_obj.datatype() == gdal.GDT_Float64:
geo = isce3.geocode.GeocodeFloat64()
elif input_raster_obj.datatype() == gdal.GDT_CFloat32:
geo = isce3.geocode.GeocodeCFloat32()
elif input_raster_obj.datatype() == gdal.GDT_CFloat64:
geo = isce3.geocode.GeocodeCFloat64()
else:
err_str = 'Unsupported raster type for geocoding'
error_channel.log(err_str)
raise NotImplementedError(err_str)
orbit = slc.getOrbit()
# init geocode members
geo.orbit = orbit
geo.ellipsoid = ellipsoid
geo.doppler = zero_doppler
geo.threshold_geo2rdr = threshold
geo.numiter_geo2rdr = maxiter
geo.dem_block_margin = dem_margin
# set data interpolator based on the geocode algorithm
if output_mode == isce3.geocode.GeocodeOutputMode.INTERP:
geo.data_interpolator = geocode_algorithm
geo.geogrid(geogrid.start_x, geogrid.start_y, geogrid.spacing_x,
geogrid.spacing_y, geogrid.width, geogrid.length,
geogrid.epsg)
# create output raster
temp_output = tempfile.NamedTemporaryFile(dir=scratch_path,
suffix='.tif')
output_raster_obj = isce3.io.Raster(temp_output.name, geogrid.width,
geogrid.length,
input_raster_obj.num_bands,
gdal.GDT_Float32, 'GTiff')
nbands_off_diag_terms = 0
out_off_diag_terms_obj = None
if flag_fullcovariance:
nbands = input_raster_obj.num_bands
nbands_off_diag_terms = (nbands**2 - nbands) // 2
if nbands_off_diag_terms > 0:
temp_off_diag = tempfile.NamedTemporaryFile(dir=scratch_path,
suffix='.tif')
out_off_diag_terms_obj = isce3.io.Raster(
temp_off_diag.name, geogrid.width, geogrid.length,
nbands_off_diag_terms, gdal.GDT_CFloat32, 'GTiff')
if flag_save_nlooks:
temp_nlooks = tempfile.NamedTemporaryFile(dir=scratch_path,
suffix='.tif')
out_geo_nlooks_obj = isce3.io.Raster(temp_nlooks.name,
geogrid.width, geogrid.length,
1, gdal.GDT_Float32, "GTiff")
else:
temp_nlooks = None
out_geo_nlooks_obj = None
if flag_save_rtc:
temp_rtc = tempfile.NamedTemporaryFile(dir=scratch_path,
suffix='.tif')
out_geo_rtc_obj = isce3.io.Raster(temp_rtc.name, geogrid.width,
geogrid.length, 1,
gdal.GDT_Float32, "GTiff")
else:
temp_rtc = None
out_geo_rtc_obj = None
if flag_save_dem:
temp_interpolated_dem = tempfile.NamedTemporaryFile(
dir=scratch_path, suffix='.tif')
if (output_mode == isce3.geocode.GeocodeOutputMode.AREA_PROJECTION
):
interpolated_dem_width = geogrid.width + 1
interpolated_dem_length = geogrid.length + 1
else:
interpolated_dem_width = geogrid.width
interpolated_dem_length = geogrid.length
out_geo_dem_obj = isce3.io.Raster(temp_interpolated_dem.name,
interpolated_dem_width,
interpolated_dem_length, 1,
gdal.GDT_Float32, "GTiff")
else:
temp_interpolated_dem = None
out_geo_dem_obj = None
# geocode rasters
geo.geocode(radar_grid=radar_grid,
input_raster=input_raster_obj,
output_raster=output_raster_obj,
dem_raster=dem_raster,
output_mode=output_mode,
geogrid_upsampling=geogrid_upsampling,
flag_apply_rtc=flag_apply_rtc,
input_terrain_radiometry=input_terrain_radiometry,
output_terrain_radiometry=output_terrain_radiometry,
exponent=exponent,
rtc_min_value_db=rtc_min_value_db,
rtc_upsampling=rtc_upsampling,
rtc_algorithm=rtc_algorithm,
abs_cal_factor=abs_cal_factor,
flag_upsample_radar_grid=flag_upsample_radar_grid,
clip_min=clip_min,
clip_max=clip_max,
radargrid_nlooks=radar_grid_nlooks,
out_off_diag_terms=out_off_diag_terms_obj,
out_geo_nlooks=out_geo_nlooks_obj,
out_geo_rtc=out_geo_rtc_obj,
out_geo_dem=out_geo_dem_obj,
input_rtc=None,
output_rtc=None,
dem_interp_method=dem_interp_method_enum,
memory_mode=memory_mode)
del output_raster_obj
if flag_save_nlooks:
del out_geo_nlooks_obj
if flag_save_rtc:
del out_geo_rtc_obj
if flag_save_dem:
del out_geo_dem_obj
if flag_fullcovariance:
# out_off_diag_terms_obj.close_dataset()
del out_off_diag_terms_obj
with h5py.File(output_hdf5, 'a') as hdf5_obj:
hdf5_obj.attrs['Conventions'] = np.string_("CF-1.8")
root_ds = f'/science/LSAR/GCOV/grids/frequency{frequency}'
h5_ds = os.path.join(root_ds, 'listOfPolarizations')
if h5_ds in hdf5_obj:
del hdf5_obj[h5_ds]
pol_list_s2 = np.array(pol_list, dtype='S2')
dset = hdf5_obj.create_dataset(h5_ds, data=pol_list_s2)
dset.attrs['description'] = np.string_(
'List of processed polarization layers with frequency ' +
frequency)
h5_ds = os.path.join(root_ds, 'radiometricTerrainCorrectionFlag')
if h5_ds in hdf5_obj:
del hdf5_obj[h5_ds]
dset = hdf5_obj.create_dataset(h5_ds, data=bool(flag_apply_rtc))
# save GCOV diagonal elements
xds = hdf5_obj[os.path.join(root_ds, 'xCoordinates')]
yds = hdf5_obj[os.path.join(root_ds, 'yCoordinates')]
cov_elements_list = [p.upper() + p.upper() for p in pol_list]
# save GCOV imagery
_save_hdf5_dataset(temp_output.name,
hdf5_obj,
root_ds,
yds,
xds,
cov_elements_list,
long_name=output_radiometry_str,
units='',
valid_min=clip_min,
valid_max=clip_max)
# save listOfCovarianceTerms
freq_group = hdf5_obj[root_ds]
if not flag_fullcovariance:
_save_list_cov_terms(cov_elements_list, freq_group)
# save nlooks
if flag_save_nlooks:
_save_hdf5_dataset(temp_nlooks.name,
hdf5_obj,
root_ds,
yds,
xds,
'numberOfLooks',
long_name='number of looks',
units='',
valid_min=0)
# save rtc
if flag_save_rtc:
_save_hdf5_dataset(temp_rtc.name,
hdf5_obj,
root_ds,
yds,
xds,
'areaNormalizationFactor',
long_name='RTC area factor',
units='',
valid_min=0)
# save interpolated DEM
if flag_save_dem:
'''
The DEM is interpolated over the geogrid pixels vertices
rather than the pixels centers.
'''
if (output_mode ==
isce3.geocode.GeocodeOutputMode.AREA_PROJECTION):
dem_geogrid = isce3.product.GeoGridParameters(
start_x=geogrid.start_x - geogrid.spacing_x / 2,
start_y=geogrid.start_y - geogrid.spacing_y / 2,
spacing_x=geogrid.spacing_x,
spacing_y=geogrid.spacing_y,
width=int(geogrid.width) + 1,
length=int(geogrid.length) + 1,
epsg=geogrid.epsg)
yds_dem, xds_dem = \
set_get_geo_info(hdf5_obj, root_ds, dem_geogrid)
else:
yds_dem = yds
xds_dem = xds
_save_hdf5_dataset(temp_interpolated_dem.name,
hdf5_obj,
root_ds,
yds_dem,
xds_dem,
'interpolatedDem',
long_name='Interpolated DEM',
units='')
# save GCOV off-diagonal elements
if flag_fullcovariance:
off_diag_terms_list = []
for b1, p1 in enumerate(pol_list):
for b2, p2 in enumerate(pol_list):
if (b2 <= b1):
continue
off_diag_terms_list.append(p1.upper() + p2.upper())
_save_list_cov_terms(cov_elements_list + off_diag_terms_list,
freq_group)
_save_hdf5_dataset(temp_off_diag.name,
hdf5_obj,
root_ds,
yds,
xds,
off_diag_terms_list,
long_name=output_radiometry_str,
units='',
valid_min=clip_min,
valid_max=clip_max)
t_freq_elapsed = time.time() - t_freq
info_channel.log(
f'frequency {frequency} ran in {t_freq_elapsed:.3f} seconds')
if frequency.upper() == 'B':
continue
cube_geogrid = isce3.product.GeoGridParameters(
start_x=radar_grid_cubes_geogrid.start_x,
start_y=radar_grid_cubes_geogrid.start_y,
spacing_x=radar_grid_cubes_geogrid.spacing_x,
spacing_y=radar_grid_cubes_geogrid.spacing_y,
width=int(radar_grid_cubes_geogrid.width),
length=int(radar_grid_cubes_geogrid.length),
epsg=radar_grid_cubes_geogrid.epsg)
cube_group_name = '/science/LSAR/GCOV/metadata/radarGrid'
native_doppler = slc.getDopplerCentroid()
'''
The native-Doppler LUT bounds error is turned off to
computer cubes values outside radar-grid boundaries
'''
native_doppler.bounds_error = False
add_radar_grid_cubes_to_hdf5(hdf5_obj, cube_group_name,
cube_geogrid,
radar_grid_cubes_heights, radar_grid,
orbit, native_doppler, zero_doppler,
threshold, maxiter)
t_all_elapsed = time.time() - t_all
info_channel.log(
f"successfully ran geocode COV in {t_all_elapsed:.3f} seconds")