def write_measurement_h5(
p: DatasetAssembler,
name: str,
g: h5py.Dataset,
overviews=images.DEFAULT_OVERVIEWS,
overview_resampling=Resampling.nearest,
expand_valid_data=True,
file_id: str = None,
):
"""
Write a measurement by copying it from a hdf5 dataset.
"""
if hasattr(g, "chunks"):
data = g[:]
else:
data = g
p.write_measurement_numpy(
name=name,
array=data,
grid_spec=images.GridSpec(
shape=g.shape,
transform=Affine.from_gdal(*g.attrs["geotransform"]),
crs=CRS.from_wkt(g.attrs["crs_wkt"]),
),
nodata=(g.attrs.get("no_data_value")),
overviews=overviews,
overview_resampling=overview_resampling,
expand_valid_data=expand_valid_data,
file_id=file_id,
)
def write_measurement_h5(
p: DatasetAssembler,
full_name: str,
g: h5py.Dataset,
overviews=images.DEFAULT_OVERVIEWS,
overview_resampling=Resampling.nearest,
expand_valid_data=True,
file_id: str = None,
):
"""
Write a measurement by copying it from a hdf5 dataset.
"""
if hasattr(g, "chunks"):
data = g[:]
else:
data = g
product_name, band_name = full_name.split(":")
p.write_measurement_numpy(
array=data,
grid_spec=images.GridSpec(
shape=g.shape,
transform=Affine.from_gdal(*g.attrs["geotransform"]),
crs=CRS.from_wkt(g.attrs["crs_wkt"]),
),
nodata=g.attrs.get("no_data_value"),
overviews=overviews,
overview_resampling=overview_resampling,
expand_valid_data=expand_valid_data,
file_id=file_id,
# Because of our strange sub-products and filename standards, we want the
# product_name to be included in the recorded band metadata,
# but not in its filename.
# So we manually calculate a filename without the extra product name prefix.
name=full_name,
path=p.names.measurement_filename(band_name, "tif", file_id=file_id),
)
def _create_contiguity(
p: DatasetAssembler,
product_list: Iterable[str],
resolution_yx: Tuple[float, float],
timedelta_product: str = "nbar",
timedelta_data: numpy.ndarray = None,
):
"""
Create the contiguity (all pixels valid) dataset.
Write a contiguity mask file based on the intersection of valid data pixels across all
bands from the input files.
"""
for product in product_list:
contiguity = None
for grid, band_name, path in p.iter_measurement_paths():
if not band_name.startswith(f"{product.lower()}:"):
continue
# Only our given res group (no pan band in Landsat)
if grid.resolution_yx != resolution_yx:
continue
with rasterio.open(path) as ds:
ds: DatasetReader
if contiguity is None:
contiguity = numpy.ones((ds.height, ds.width),
dtype="uint8")
geobox = GridSpec.from_rio(ds)
elif ds.shape != contiguity.shape:
raise NotImplementedError(
"Contiguity from measurements of different shape")
for band in ds.indexes:
contiguity &= ds.read(band) > 0
if contiguity is None:
secho(f"No images found for requested product {product}", fg="red")
continue
p.write_measurement_numpy(
f"oa:{product.lower()}_contiguity",
contiguity,
geobox,
nodata=255,
overviews=None,
expand_valid_data=False,
)
# masking the timedelta_data with contiguity mask to get max and min timedelta within the NBAR product
# footprint for Landsat sensor. For Sentinel sensor, it inherits from level 1 yaml file
if timedelta_data is not None and product.lower() == timedelta_product:
valid_timedelta_data = numpy.ma.masked_where(
contiguity == 0, timedelta_data)
def offset_from_center(v: numpy.datetime64):
return p.datetime + timedelta(microseconds=v.astype(float) *
1_000_000.0)
p.datetime_range = (
offset_from_center(numpy.ma.min(valid_timedelta_data)),
offset_from_center(numpy.ma.max(valid_timedelta_data)),
)