def test_rectify_2x2_to_13x13_none(self):
src_ds = self.new_source_dataset()
output_geom = ImageGeom(size=(13, 13),
x_min=10.0,
y_min=50.0,
xy_res=0.5)
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
self.assertIsNone(dst_ds)
output_geom = ImageGeom(size=(13, 13),
x_min=-10.0,
y_min=50.0,
xy_res=0.5)
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
self.assertIsNone(dst_ds)
output_geom = ImageGeom(size=(13, 13),
x_min=0.0,
y_min=58.0,
xy_res=0.5)
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
self.assertIsNone(dst_ds)
output_geom = ImageGeom(size=(13, 13),
x_min=0.0,
y_min=42.0,
xy_res=0.5)
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
self.assertIsNone(dst_ds)
def test_rectify_2x2_to_13x13(self):
src_ds = self.new_source_dataset()
output_geom = ImageGeom(size=(13, 13), x_min=-0.25, y_min=49.75, xy_res=0.5)
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
lon, lat, rad = self._assert_shape_and_dim(dst_ds, 13, 13)
np.testing.assert_almost_equal(lon.values,
np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5., 5.5, 6.],
dtype=lon.dtype))
np.testing.assert_almost_equal(lat.values,
np.array([50., 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0, 54.5, 55.,
55.5, 56.],
dtype=lat.dtype))
np.testing.assert_almost_equal(rad.values,
np.array([
[nan, nan, nan, nan, 4.0, nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, 4.0, 4.0, 4.0, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, 3.0, 4.0, 4.0, 4.0, 4.0, nan, nan, nan, nan, nan, nan],
[nan, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 2.0, nan, nan, nan, nan],
[3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 2.0, 2.0, 2.0, nan, nan, nan],
[nan, 3.0, 3.0, 3.0, 3.0, 3.0, 1.0, 2.0, 2.0, 2.0, 2.0, nan, nan],
[nan, 3.0, 3.0, 3.0, 3.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
[nan, 3.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, nan, nan],
[nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, nan, nan, nan, nan],
[nan, nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, nan, nan, nan, nan, nan],
[nan, nan, 1.0, 1.0, 1.0, 1.0, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, 1.0, 1.0, nan, nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, 1.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
], dtype=rad.dtype))
def test_rectify_2x2_to_13x13_antimeridian(self):
src_ds = self.new_source_dataset_antimeridian()
output_geom = ImageGeom(size=(13, 13), x_min=177.75, y_min=49.75, xy_res=0.5)
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
self.assertIsNotNone(dst_ds)
lon, lat, rad = self._assert_shape_and_dim(dst_ds, 13, 13)
np.testing.assert_almost_equal(lon.values,
np.array([178., 178.5, 179., 179.5, 180., -179.5, -179., -178.5,
-178., -177.5, -177., -176.5, -176.],
dtype=lon.dtype))
np.testing.assert_almost_equal(lat.values,
np.array([50., 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0, 54.5, 55.,
55.5, 56.],
dtype=lat.dtype))
np.testing.assert_almost_equal(rad.values,
np.array([
[nan, nan, nan, nan, 4.0, nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, 4.0, 4.0, 4.0, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, 3.0, 4.0, 4.0, 4.0, 4.0, nan, nan, nan, nan, nan, nan],
[nan, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 2.0, nan, nan, nan, nan],
[3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 2.0, 2.0, 2.0, nan, nan, nan],
[nan, 3.0, 3.0, 3.0, 3.0, 3.0, 1.0, 2.0, 2.0, 2.0, 2.0, nan, nan],
[nan, 3.0, 3.0, 3.0, 3.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
[nan, 3.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, nan, nan],
[nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, nan, nan, nan, nan],
[nan, nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, nan, nan, nan, nan, nan],
[nan, nan, 1.0, 1.0, 1.0, 1.0, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, 1.0, 1.0, nan, nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, 1.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
], dtype=rad.dtype))
def test_rectify_2x2_to_7x7_subset(self):
src_ds = self.new_source_dataset()
output_geom = ImageGeom(size=(7, 7), x_min=1.5, y_min=50.5, xy_res=1.0)
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
lon, lat, rad = self._assert_shape_and_dim(dst_ds, (7, 7))
np.testing.assert_almost_equal(
lon.values,
np.array([2.0, 3.0, 4.0, 5., 6., 7., 8.], dtype=lon.dtype))
np.testing.assert_almost_equal(
lat.values,
np.array([51.0, 52.0, 53.0, 54.0, 55., 56., 57.], dtype=lat.dtype))
np.testing.assert_almost_equal(
rad.values,
np.array([
[4.0, 4.0, nan, nan, nan, nan, nan],
[3.0, 4.0, 2.0, nan, nan, nan, nan],
[3.0, 1.0, 2.0, 2.0, 2.0, nan, nan],
[1.0, 1.0, 2.0, nan, nan, nan, nan],
[1.0, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan],
],
dtype=rad.dtype))
def test_rectify_2x2_to_13x13_output_ij_names(self):
src_ds = self.new_source_dataset()
output_geom = ImageGeom(size=(13, 13),
x_min=-0.25,
y_min=49.75,
xy_res=0.5)
dst_ds = rectify_dataset(src_ds,
output_geom=output_geom,
output_ij_names=('source_i', 'source_j'))
lon, lat, rad, source_i, source_j = self._assert_shape_and_dim(
dst_ds, (13, 13), var_names=('rad', 'source_i', 'source_j'))
np.testing.assert_almost_equal(
lon.values,
np.array([
0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5., 5.5, 6.
],
dtype=lon.dtype))
np.testing.assert_almost_equal(
lat.values,
np.array([
50., 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0, 54.5, 55.,
55.5, 56.
],
dtype=lat.dtype))
np.testing.assert_almost_equal(rad.values,
self.expected_rad_13x13(rad.dtype))
np.testing.assert_almost_equal(np.floor(source_i.values + 0.5),
self.expected_i_13x13())
np.testing.assert_almost_equal(np.floor(source_j.values + 0.5),
self.expected_j_13x13())
def test_rectify_2x2_to_13x13_dask_13x3(self):
src_ds = self.new_source_dataset()
output_geom = ImageGeom(size=(13, 13),
x_min=-0.25,
y_min=49.75,
xy_res=0.5,
tile_size=(13, 3))
dst_ds = rectify_dataset(src_ds, output_geom=output_geom)
lon, lat, rad = self._assert_shape_and_dim(dst_ds, (13, 13),
chunks=((3, 3, 3, 3, 1),
(13, )))
np.testing.assert_almost_equal(
lon.values,
np.array([
0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5., 5.5, 6.
],
dtype=lon.dtype))
np.testing.assert_almost_equal(
lat.values,
np.array([
50., 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0, 54.5, 55.,
55.5, 56.
],
dtype=lat.dtype))
np.testing.assert_almost_equal(rad.values,
self.expected_rad_13x13(rad.dtype))
def test_rectify_dataset(self):
src_ds = create_s2plus_dataset()
expected_data = np.array(
[[
0.033001, 0.018002, 0.018002, 0.018002, 0.007999, 0.007999,
0.008999, 0.008999, 0.008999, 0.021
],
[
0.041, 0.022999, 0.022999, 0.022999, 0.007, 0.007, 0.009998,
0.009998, 0.009998, 0.021
],
[
0.036999, 0.022999, 0.022999, 0.022999, 0.007999, 0.007999,
0.008999, 0.008999, 0.023998, 0.023998
],
[
0.036999, 0.022999, 0.022999, 0.022999, 0.007999, 0.007999,
0.008999, 0.008999, 0.023998, 0.023998
],
[
0.028, 0.021, 0.021, 0.021, 0.009998, 0.009998, 0.008999,
0.008999, 0.022999, 0.022999
],
[
0.025002, 0.019001, 0.019001, 0.019001, 0.008999, 0.008999,
0.012001, 0.012001, 0.022999, nan
], [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]])
dst_ds = rectify_dataset(src_ds, tile_size=None)
self.assertEqual(None, dst_ds.rrs_665.chunks)
np.testing.assert_almost_equal(dst_ds.rrs_665.values, expected_data)
dst_ds = rectify_dataset(src_ds, tile_size=5)
self.assertEqual(((5, 2), (5, 5)), dst_ds.rrs_665.chunks)
np.testing.assert_almost_equal(dst_ds.rrs_665.values, expected_data)
dst_ds = rectify_dataset(src_ds, tile_size=None, is_y_reversed=True)
self.assertEqual(None, dst_ds.rrs_665.chunks)
np.testing.assert_almost_equal(dst_ds.rrs_665.values,
expected_data[::-1])
dst_ds = rectify_dataset(src_ds, tile_size=5, is_y_reversed=True)
self.assertEqual(((2, 5), (5, 5)), dst_ds.rrs_665.chunks)
np.testing.assert_almost_equal(dst_ds.rrs_665.values,
expected_data[::-1])
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 _rectify(input_path: str, xy_names: Optional[Tuple[str, str]],
var_names: Optional[Sequence[str]], output_path: str,
output_format: Optional[str], output_size: Optional[Tuple[int,
int]],
output_tile_size: Optional[Tuple[int, int]],
output_point: Optional[Tuple[float,
float]], output_res: Optional[float],
delta: float, dry_run: bool, monitor):
from xcube.core.dsio import guess_dataset_format
from xcube.core.dsio import open_dataset
from xcube.core.dsio import write_dataset
from xcube.core.rectify import rectify_dataset
from xcube.core.rectify import ImageGeom
from xcube.core.sentinel3 import is_sentinel3_product
from xcube.core.sentinel3 import open_sentinel3_product
if not output_format:
output_format = guess_dataset_format(output_path)
output_geom = None
if output_size is not None and output_point is not None and output_res is not None:
output_geom = ImageGeom(size=output_size,
x_min=output_point[0],
y_min=output_point[1],
xy_res=output_res)
elif output_size is not None or output_point is not None or output_res is not None:
raise click.ClickException(
'SIZE, POINT, and RES must all be given or none of them.')
monitor(f'Opening dataset from {input_path!r}...')
if is_sentinel3_product(input_path):
src_ds = open_sentinel3_product(input_path)
else:
src_ds = open_dataset(input_path)
monitor('Rectifying...')
reproj_ds = rectify_dataset(src_ds,
xy_names=xy_names,
var_names=var_names,
output_geom=output_geom,
tile_size=output_tile_size,
uv_delta=delta)
if reproj_ds is None:
monitor(
f'Dataset {input_path} does not seem to have an intersection with bounding box'
)
return
monitor(f'Writing rectified dataset to {output_path!r}...')
if not dry_run:
write_dataset(reproj_ds, output_path, output_format)
monitor(f'Done.')
def test_rectify_2x2_to_default(self):
src_ds = self.new_source_dataset()
dst_ds = rectify_dataset(src_ds)
lon, lat, rad = self._assert_shape_and_dim(dst_ds, (4, 4))
np.testing.assert_almost_equal(
lon.values, np.array([1.0, 3.0, 5., 7.], dtype=lon.dtype))
np.testing.assert_almost_equal(
lat.values, np.array([51.0, 53.0, 55., 57.], dtype=lat.dtype))
np.testing.assert_almost_equal(
rad.values,
np.array([[3.0, 4.0, nan, nan], [3.0, 1.0, 2.0, nan],
[1.0, nan, nan, nan], [nan, nan, nan, nan]],
dtype=rad.dtype))
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)
