shared.write_unw_from_data_or_geotiff(geotif_or_data=incidence_data,
dest_unw=inc_file,
ifg_proc=1)
shared.write_unw_from_data_or_geotiff(geotif_or_data=elevation_data,
dest_unw=elevation_file,
ifg_proc=1)
header.update(dem_header)
header[ifc.PYRATE_TIME_SPAN] = 0
header[ifc.SLAVE_DATE] = 0
header[ifc.DATA_UNITS] = 'degrees'
header[ifc.DATA_TYPE] = ifc.INCIDENCE
header[ifc.SLAVE_TIME] = 0
shared.write_geotiff(header=header,
data_path=elevation_file,
dest=dest_lv_theta,
nodata=np.nan)
shared.write_geotiff(header=header,
data_path=inc_file,
dest=dest_inc,
nodata=np.nan)
ds = gdal.Open(dest_lv_theta, gdal.GA_ReadOnly)
data_elevation = ds.ReadAsArray()
ds = None
ds = gdal.Open(dest_inc, gdal.GA_ReadOnly)
data_inc = ds.ReadAsArray()
ds = None
def crop_resample_average(input_tif,
extents: Union[List, Tuple],
new_res,
output_file,
thresh,
hdr,
out_driver_type='GTiff',
match_pyrate=False,
coherence_path=None,
coherence_thresh=None):
"""
Crop, resample, and average a geotiff image.
:param str input_tif: Path to input geotiff to resample/crop
:param tuple extents: Cropping extents (xfirst, yfirst, xlast, ylast)
:param list new_res: [xres, yres] resolution of output image
:param str output_file: Path to output resampled/cropped geotiff
:param float thresh: NaN fraction threshold
:param str out_driver_type: The output driver; `MEM` or `GTiff` (optional)
:param bool match_pyrate: Match Legacy output (optional)
:param dict hdr: dictionary of metadata
:return: resampled_average: output cropped and resampled image
:rtype: ndarray
:return: out_ds: destination gdal dataset object
:rtype: gdal.Dataset
"""
dst_ds, _, _, _ = _crop_resample_setup(extents,
input_tif,
new_res,
output_file,
out_bands=2,
dst_driver_type='MEM')
# make a temporary copy of the dst_ds for PyRate style prepifg
if (match_pyrate and new_res[0]):
tmp_ds = gdal.GetDriverByName('MEM').CreateCopy('', dst_ds)
else:
tmp_ds = None
src_ds, src_ds_mem = _setup_source(input_tif)
if coherence_path and coherence_thresh:
coherence_masking(src_ds_mem, coherence_path, coherence_thresh)
elif coherence_path and not coherence_thresh:
raise ValueError("Coherence file provided without a coherence "
"threshold. Please ensure you provide 'cohthresh' "
"in your config if coherence masking is enabled.")
resampled_average, src_ds_mem = gdal_average(dst_ds, src_ds, src_ds_mem,
thresh)
src_ds = None
src_dtype = src_ds_mem.GetRasterBand(1).DataType
src_gt = src_ds_mem.GetGeoTransform()
# required to match Legacy output
if tmp_ds:
_alignment(input_tif, new_res, resampled_average, src_ds_mem, src_gt,
tmp_ds)
# grab metadata from existing geotiff
gt = dst_ds.GetGeoTransform()
wkt = dst_ds.GetProjection()
# insert metadata from the header
md = shared.collate_metadata(hdr)
# update metadata for output
# TODO: Metadata should be updated immediately as a prepifg/process step is applied
# move this into the respective steps
for k, v in md.items():
if k == ifc.DATA_TYPE:
# update data type metadata
if (v == ifc.ORIG) and (coherence_path is not None):
md.update({ifc.DATA_TYPE: ifc.MLOOKED_COH_MASKED_IFG})
elif (v == ifc.ORIG) and (coherence_path is None):
md.update({ifc.DATA_TYPE: ifc.MULTILOOKED})
elif v == ifc.COH:
md.update({ifc.DATA_TYPE: ifc.MULTILOOKED_COH})
elif v == ifc.DEM:
md.update({ifc.DATA_TYPE: ifc.MLOOKED_DEM})
elif v == ifc.INCIDENCE:
md.update({ifc.DATA_TYPE: ifc.MLOOKED_INC})
else:
raise TypeError(f'Data Type metadata {v} not recognised')
add_looks_and_crop_from_header(hdr, md)
# In-memory GDAL driver doesn't support compression so turn it off.
creation_opts = ['compress=packbits'] if out_driver_type != 'MEM' else []
out_ds = shared.gdal_dataset(output_file,
dst_ds.RasterXSize,
dst_ds.RasterYSize,
driver=out_driver_type,
bands=1,
dtype=src_dtype,
metadata=md,
crs=wkt,
geotransform=gt,
creation_opts=creation_opts)
if out_driver_type != 'MEM':
shared.write_geotiff(resampled_average, out_ds, np.nan)
log.info(f"Writing geotiff: {output_file}")
else:
out_ds.GetRasterBand(1).WriteArray(resampled_average)
return resampled_average, out_ds
def crop_resample_average(input_tif,
extents,
new_res,
output_file,
thresh,
out_driver_type='GTiff',
match_pyrate=False,
hdr=None,
coherence_path=None,
coherence_thresh=None):
"""
Crop, resample, and average a geotiff image.
:param str input_tif: Path to input geotiff to resample/crop
:param tuple extents: Cropping extents (xfirst, yfirst, xlast, ylast)
:param list new_res: [xres, yres] resolution of output image
:param str output_file: Path to output resampled/cropped geotiff
:param float thresh: NaN fraction threshold
:param str out_driver_type: The output driver; `MEM` or `GTiff` (optional)
:param bool match_pyrate: Match Legacy output (optional)
:param dict hdr: dictionary of metadata
:return: resampled_average: output cropped and resampled image
:rtype: ndarray
:return: out_ds: destination gdal dataset object
:rtype: gdal.Dataset
"""
dst_ds, _, _, _ = _crop_resample_setup(extents,
input_tif,
new_res,
output_file,
out_bands=2,
dst_driver_type='MEM')
# make a temporary copy of the dst_ds for PyRate style prepifg
tmp_ds = gdal.GetDriverByName('MEM').CreateCopy('', dst_ds) \
if (match_pyrate and new_res[0]) else None
src_ds, src_ds_mem = _setup_source(input_tif)
if coherence_path and coherence_thresh:
coherence_raster = prepifg_helper.dem_or_ifg(coherence_path)
coherence_raster.open()
coherence_ds = coherence_raster.dataset
coherence_masking(src_ds_mem, coherence_ds, coherence_thresh)
elif coherence_path and not coherence_thresh:
raise ValueError(f"Coherence file provided without a coherence "
f"threshold. Please ensure you provide 'cohthresh' "
f"in your config if coherence masking is enabled.")
resampled_average, src_ds_mem = \
gdal_average(dst_ds, src_ds, src_ds_mem, thresh)
src_dtype = src_ds_mem.GetRasterBand(1).DataType
src_gt = src_ds_mem.GetGeoTransform()
# required to match Legacy output
if tmp_ds:
_alignment(input_tif, new_res, resampled_average, src_ds_mem, src_gt,
tmp_ds)
# grab metadata from existing geotiff
gt = dst_ds.GetGeoTransform()
wkt = dst_ds.GetProjection()
# TEST HERE IF EXISTING FILE HAS PYRATE METADATA. IF NOT ADD HERE
if not ifc.DATA_TYPE in dst_ds.GetMetadata() and hdr is not None:
md = shared.collate_metadata(hdr)
else:
md = dst_ds.GetMetadata()
# update metadata for output
for k, v in md.items():
if k == ifc.DATA_TYPE:
# update data type metadata
if v == ifc.ORIG and coherence_path:
md.update({ifc.DATA_TYPE: ifc.COHERENCE})
elif v == ifc.ORIG and not coherence_path:
md.update({ifc.DATA_TYPE: ifc.MULTILOOKED})
elif v == ifc.DEM:
md.update({ifc.DATA_TYPE: ifc.MLOOKED_DEM})
elif v == ifc.INCIDENCE:
md.update({ifc.DATA_TYPE: ifc.MLOOKED_INC})
elif v == ifc.COHERENCE and coherence_path:
pass
elif v == ifc.MULTILOOKED and coherence_path:
md.update({ifc.DATA_TYPE: ifc.COHERENCE})
elif v == ifc.MULTILOOKED and not coherence_path:
pass
else:
raise TypeError('Data Type metadata not recognised')
# In-memory GDAL driver doesn't support compression so turn it off.
creation_opts = ['compress=packbits'] if out_driver_type != 'MEM' else []
out_ds = shared.gdal_dataset(output_file,
dst_ds.RasterXSize,
dst_ds.RasterYSize,
driver=out_driver_type,
bands=1,
dtype=src_dtype,
metadata=md,
crs=wkt,
geotransform=gt,
creation_opts=creation_opts)
shared.write_geotiff(resampled_average, out_ds, np.nan)
return resampled_average, out_ds