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()
# Create geo2rdr instance
geo2rdrObj = isce3.geometry.geo2rdr(radarGrid=radarGrid,
orbit=orbit,
ellipsoid=ellipsoid)
# Read topo multiband raster
topoRaster = isce3.io.raster(
filename=os.path.join(opts.topodir, 'topo.vrt'))
# Init output directory
if not os.path.isdir(opts.outdir):
os.mkdir(opts.outdir)
# Run geo2rdr
geo2rdrObj.geo2rdr(topoRaster,
outputDir=opts.outdir,
azshift=opts.azoff,
rgshift=opts.rgoff)
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 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()
rdr2geo = isce3.geometry.rdr2geo(radarGrid=radarGrid,
orbit=orbit,
ellipsoid=ellipsoid,
computeMask=opts.mask)
# Read DEM raster
demRaster = isce3.io.raster(filename=opts.dem)
# Init output directory
if not os.path.isdir(opts.outdir):
os.mkdir(opts.outdir)
# Run rdr2geo
rdr2geo.topo(demRaster, outputDir=opts.outdir)
return 0
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():
'''
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():
'''
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 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 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 get_geo_polygon(ref_slc, min_height=-500.,
max_height=9000., pts_per_edge=5):
"""Create polygon (EPSG:4326) using RSLC radar grid and orbits
Parameters:
-----------
ref_slc: str
Path to RSLC product to stage the DEM for
min_height: float
Global minimum height (in m) for DEM interpolator
max_height: float
Global maximum height (in m) for DEM interpolator
pts_per_edge: float
Number of points per edge for min/max bounding box computation
Returns:
-------
poly: shapely.Geometry.Polygon
Bounding polygon corresponding to RSLC perimeter on the ground
"""
from isce3.core import LUT2d
from isce3.geometry import DEMInterpolator, get_geo_perimeter_wkt
from nisar.products.readers import SLC
# Prepare SLC dataset input
productSlc = SLC(hdf5file=ref_slc)
# Extract orbits, radar grid, and doppler for frequency A
orbit = productSlc.getOrbit()
radar_grid = productSlc.getRadarGrid(frequency='A')
doppler = LUT2d()
# Get min and max global height DEM interpolators
dem_min = DEMInterpolator(height=min_height)
dem_max = DEMInterpolator(height=max_height)
# Get min and max bounding boxes
box_min = get_geo_perimeter_wkt(radar_grid, orbit, doppler,
dem_min, pts_per_edge)
box_max = get_geo_perimeter_wkt(radar_grid, orbit, doppler,
dem_max, pts_per_edge)
# Determine minimum and maximum polygons
poly_min = shapely.wkt.loads(box_min)
poly_max = shapely.wkt.loads(box_max)
# Get polygon from intersection of poly_min and poly_max
poly = poly_min | poly_max
return poly
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 prep_frequency_and_polarizations(self):
'''
check frequency and polarizations and fix as needed
'''
error_channel = journal.error(
'RunConfig.prep_frequency_and_polarizations')
input_path = self.cfg['input_file_group']['input_file_path']
freq_pols = self.cfg['processing']['input_subset'][
'list_of_frequencies']
slc = SLC(hdf5file=input_path)
for freq in freq_pols.keys():
if freq not in slc.frequencies:
err_str = f"Frequency {freq} invalid; not found in source frequencies."
error_channel.log(err_str)
raise ValueError(err_str)
# first check polarizations from source hdf5
rslc_pols = slc.polarizations[freq]
# use all RSLC polarizations if None provided
if freq_pols[freq] is None:
freq_pols[freq] = rslc_pols
continue
# use polarizations provided by user
# check if user provided polarizations match RSLC ones
for usr_pol in freq_pols[freq]:
if usr_pol not in rslc_pols:
err_str = f"{usr_pol} invalid; not found in source polarizations."
error_channel.log(err_str)
raise ValueError(err_str)
def determine_perimeter(opts):
"""
Determine perimeter for DEM staging
"""
from nisar.products.readers import SLC
from isce3.geometry import deminterpolator
from isce3.geometry import getGeoPerimeter
from isce3.core import projection
if opts.bbox is not None:
print('Determine perimeter from bounding box')
lat = opts.bbox[0:2]
lon = opts.bbox[2:4]
ring = LinearRing([(lon[0], lat[0]), (lon[0], lat[1]),
(lon[1], lat[1]), (lon[1], lat[0])])
else:
print('Determine perimeter from SLC radar grid')
# Prepare SLC dataset input
productSlc = SLC(hdf5file=opts.product)
# Extract orbits and radar Grid parameters
orbit = productSlc.getOrbit()
radarGrid = productSlc.getRadarGrid()
# Minimum and Maximum global heights
dem_min = deminterpolator(height=-500.)
dem_max = deminterpolator(height=9000.)
# Get Minimum and Maximum bounding boxes
epsg = projection(epsg=4326)
box_min = getGeoPerimeter(radarGrid,
orbit,
epsg,
dem=dem_min,
pointsPerEdge=5)
box_max = getGeoPerimeter(radarGrid,
orbit,
epsg,
dem=dem_max,
pointsPerEdge=5)
dummy = json.loads(box_min)['coordinates'] + \
json.loads(box_max)['coordinates']
ring = LinearRing(dummy)
return ring
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 open_product(filename: str,
product_type: str = None,
root_path: str = None):
'''
Open NISAR product (HDF5 file), instantianting an object
of an existing product class (e.g. RSLC, RRSD), if
defined, or a general product (GeneralProduct) otherwise.
Parameters
----------
filename : str
HDF5 filename
product_type : str
Preliminary product type to check (e.g. RCOV) before default product type list
root_path : str
Preliminary root path to check (e.g., XSAR, PSAR) before default root
path list
Returns
-------
object
Object derived from the base class
'''
if root_path is None:
root_path = get_hdf5_file_root_path(filename, root_path=root_path)
product_type = get_hdf5_file_product_type(filename,
product_type=product_type,
root_path=root_path)
# set keyword arguments for class constructors
kwargs = {}
kwargs['hdf5file'] = filename
kwargs['_RootPath'] = root_path
if (product_type == 'RSLC'):
# return SLC obj
from nisar.products.readers import SLC
return SLC(**kwargs)
elif (product_type == 'RRSD'):
# return Raw obj
from nisar.products.readers.Raw import Raw
return Raw(**kwargs)
kwargs['_ProductType'] = product_type
# return ProductFactory obj
return GenericProduct(**kwargs)
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 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 main(opts):
"""
Runs isce::geocode::GeocodeCov for any GDAL raster with an associated HDF5 product.
For example, to geocode a multilooked interferogram, provide the HDF5 product
for the reference scene which defined the full-resolution radar geometry.
"""
# Common driver for all output files
driver = gdal.GetDriverByName('ISCE')
# Open input raster
input_raster = Raster(opts.raster)
# Open its associated product
slc = SLC(hdf5file=opts.h5)
# Make ellipsoid
ellps = isce3.core.ellipsoid()
# Get radar grid
radar_grid = slc.getRadarGrid()
if (opts.alks > 1 or opts.rlks > 1):
radar_grid = radar_grid.multilook(opts.alks, opts.rlks)
# Get orbit
orbit = slc.getOrbit()
# Make reference epochs consistent
orbit.referenceEpoch = radar_grid.referenceEpoch
# Make a zero-Doppler LUT
doppler = isce3.core.lut2d()
# Compute DEM bounds for radar grid
proj_win = isce3.geometry.geometry.getBoundsOnGround(
orbit,
ellps,
doppler,
radar_grid,
0,
0,
radar_grid.width,
radar_grid.length,
margin=np.radians(0.01))
# GDAL expects degrees
proj_win = np.degrees(proj_win)
# Extract virtual DEM covering radar bounds
ds = gdal.Open(opts.dem, gdal.GA_ReadOnly)
crop_dem_ds = gdal.Translate('/vsimem/dem.crop',
ds,
format='ISCE',
projWin=proj_win)
ds = None
# Instantiate Geocode object
geo = Geocode(orbit=orbit, ellipsoid=ellps, inputRaster=input_raster)
# Set radar grid
# geo.radarGrid(doppler,
# radar_grid.referenceEpoch,
# radar_grid.sensingStart,
# 1.0/radar_grid.prf,
# radar_grid.length,
# radar_grid.startingRange,
# radar_grid.rangePixelSpacing,
# radar_grid.wavelength,
# radar_grid.width,
# radar_grid.lookSide)
# Get DEM geotransform from DEM raster
lon0, dlon, _, lat0, _, dlat = crop_dem_ds.GetGeoTransform()
ny_geo = crop_dem_ds.RasterYSize
nx_geo = crop_dem_ds.RasterXSize
crop_dem_ds = None
print('Cropped DEM shape: (%d, %d)' % (ny_geo, nx_geo))
# Open DEM raster as an ISCE raster
dem_raster = Raster('/vsimem/dem.crop')
# Set geographic grid
geo.geoGrid(lon0, lat0, dlon, dlat, nx_geo, ny_geo, dem_raster.EPSG)
# Create output raster
if opts.outname == '':
opts.outname = opts.raster + '.geo'
odset = driver.Create(opts.outname, nx_geo, ny_geo, 1,
input_raster.getDatatype(band=1))
output_raster = Raster('', dataset=odset)
# Run geocoding
geo.geocode(radar_grid, input_raster, output_raster, dem_raster)
# Clean up
crop_dem_ds = None
odset = None
gdal.Unlink('/vsimem/dem.crop')
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 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):
'''
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 slc_obj(self):
if self._slc_obj is None:
self._slc_obj = SLC(hdf5file=self.state.input_hdf5)
return self._slc_obj
def create(cfg,
frequency_group=None,
frequency=None,
geocode_dict=None,
default_spacing_x=None,
default_spacing_y=None):
'''
- frequency_group is the name of the sub-group that
holds the fields x_posting and y_posting, which is usually
the frequency groups "A" or "B". If these fields
are direct member of the output_posting group, e.g
for radar_grid_cubes, the frequency_group should be left
as None.
- frequency is the frequency name, if not provided, it will be
the same as the frequency_group.
- geocode_dict overwrites the default geocode_dict from
processing.geocode
- default_spacing_x is default pixel spacing in the X-direction
- default_spacing_y is default pixel spacing in the Y-direction
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
if geocode_dict is None:
geocode_dict = cfg['processing']['geocode']
input_hdf5 = cfg['input_file_group']['input_file_path']
dem_file = cfg['dynamic_ancillary_file_group']['dem_file']
slc = SLC(hdf5file=input_hdf5)
# unpack and check cfg dict values. default values set to trigger inside fix(...)
epsg = geocode_dict['output_epsg']
start_x = geocode_dict['top_left']['x_abs']
start_y = geocode_dict['top_left']['y_abs']
if frequency is None:
frequency = frequency_group
if frequency_group is None:
spacing_x = geocode_dict['output_posting']['x_posting']
spacing_y = geocode_dict['output_posting']['y_posting']
else:
spacing_x = geocode_dict['output_posting'][frequency_group][
'x_posting']
spacing_y = geocode_dict['output_posting'][frequency_group][
'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
# copy X spacing from default X spacing (if applicable)
if spacing_x is None and default_spacing_x is not None:
spacing_x = default_spacing_x
# copy Y spacing from default Y spacing (if applicable)
if spacing_y is None and default_spacing_y is not None:
spacing_y = default_spacing_y
if spacing_x is None or spacing_y is None:
dem_raster = isce3.io.Raster(dem_file)
# Set pixel spacing using the input DEM (same EPSG)
if epsg == dem_raster.get_epsg():
# 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)
else:
epsg_spatial_ref = osr.SpatialReference()
epsg_spatial_ref.ImportFromEPSG(epsg)
# Set pixel spacing in degrees (lat/lon)
if epsg_spatial_ref.IsGeographic():
if spacing_x is None:
spacing_x = 0.00017966305682390427
if spacing_y is None:
spacing_y = -0.00017966305682390427
# Set pixel spacing in meters
else:
if spacing_x is None:
spacing_x = 20
if spacing_y is None:
spacing_y = -20
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)
# init geogrid
if None in [start_x, start_y, epsg, end_x, end_y]:
# extract other geogrid params from radar grid and orbit constructed bounding box
geogrid = isce3.product.bbox_to_geogrid(slc.getRadarGrid(frequency),
slc.getOrbit(),
isce3.core.LUT2d(), 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:
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 = isce3.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