def process(self,
dataset: xr.Dataset,
geo_coding: GeoCoding,
output_geom: ImageGeom,
output_resampling: str,
include_non_spatial_vars=False) -> xr.Dataset:
"""
Perform reprojection using tie-points / ground control points.
"""
reprojection_info = self.get_reprojection_info(dataset)
in_rectification_mode = reprojection_info.xy_gcp_step is None
if in_rectification_mode:
warn_prefix = 'unsupported argument in np-GCP rectification mode'
if reprojection_info.xy_tp_gcp_step is not None:
warnings.warn(
f'{warn_prefix}: ignoring '
f'reprojection_info.xy_tp_gcp_step = {reprojection_info.xy_tp_gcp_step!r}'
)
if output_resampling != 'Nearest':
warnings.warn(f'{warn_prefix}: ignoring '
f'dst_resampling = {output_resampling!r}')
if include_non_spatial_vars:
warnings.warn(
f'{warn_prefix}: ignoring '
f'include_non_spatial_vars = {include_non_spatial_vars!r}')
geo_coding = geo_coding.derive(
x_name=reprojection_info.xy_names[0],
y_name=reprojection_info.xy_names[1])
dataset = rectify_dataset(dataset,
compute_subset=False,
geo_coding=geo_coding,
output_geom=output_geom,
is_y_axis_inverted=True)
if dataset is not None and geo_coding.is_geo_crs and geo_coding.xy_names != (
'lon', 'lat'):
dataset = dataset.rename({
geo_coding.x_name: 'lon',
geo_coding.y_name: 'lat'
})
return dataset
else:
return reproject_xy_to_wgs84(
dataset,
src_xy_var_names=reprojection_info.xy_names,
src_xy_tp_var_names=reprojection_info.xy_tp_names,
src_xy_crs=reprojection_info.xy_crs,
src_xy_gcp_step=reprojection_info.xy_gcp_step or 1,
src_xy_tp_gcp_step=reprojection_info.xy_tp_gcp_step or 1,
dst_size=output_geom.size,
dst_region=output_geom.xy_bbox,
dst_resampling=output_resampling,
include_non_spatial_vars=include_non_spatial_vars)
def process(self,
dataset: xr.Dataset,
geo_coding: GeoCoding,
output_geom: ImageGeom,
output_resampling: str,
include_non_spatial_vars=False) -> xr.Dataset:
"""
Perform reprojection using tie-points / ground control points.
"""
reprojection_info = self.get_reprojection_info(dataset)
in_rectification_mode = reprojection_info.xy_gcp_step is None
if in_rectification_mode:
warn_prefix = 'unsupported argument in np-GCP rectification mode'
if reprojection_info.xy_tp_gcp_step is not None:
warnings.warn(
f'{warn_prefix}: ignoring '
f'reprojection_info.xy_tp_gcp_step = {reprojection_info.xy_tp_gcp_step!r}'
)
if output_resampling != 'Nearest':
warnings.warn(f'{warn_prefix}: ignoring '
f'dst_resampling = {output_resampling!r}')
if include_non_spatial_vars:
warnings.warn(
f'{warn_prefix}: ignoring '
f'include_non_spatial_vars = {include_non_spatial_vars!r}')
geo_coding = geo_coding.derive(
x_name=reprojection_info.xy_names[0],
y_name=reprojection_info.xy_names[1])
dataset = rectify_dataset(dataset,
compute_subset=False,
geo_coding=geo_coding,
output_geom=output_geom,
is_y_reversed=True)
if output_geom.is_tiled:
# The following condition may become true, if we have used rectified_dataset(input, ..., is_y_reverse=True)
# In this case y-chunksizes will also be reversed. So that the first chunk is smaller than any other.
# Zarr will reject such datasets, when written.
if dataset.chunks.get('lat')[0] < dataset.chunks.get(
'lat')[-1]:
dataset = dataset.chunk({
'lat': output_geom.tile_height,
'lon': output_geom.tile_width
})
if dataset is not None and geo_coding.is_geo_crs and geo_coding.xy_names != (
'lon', 'lat'):
dataset = dataset.rename({
geo_coding.x_name: 'lon',
geo_coding.y_name: 'lat'
})
return dataset
else:
return reproject_xy_to_wgs84(
dataset,
src_xy_var_names=reprojection_info.xy_names,
src_xy_tp_var_names=reprojection_info.xy_tp_names,
src_xy_crs=reprojection_info.xy_crs,
src_xy_gcp_step=reprojection_info.xy_gcp_step or 1,
src_xy_tp_gcp_step=reprojection_info.xy_tp_gcp_step or 1,
dst_size=output_geom.size,
dst_region=output_geom.xy_bbox,
dst_resampling=output_resampling,
include_non_spatial_vars=include_non_spatial_vars)