def manual_data_stack(self, datasets, measurements, mask, skip_corrections,
**kwargs):
# pylint: disable=too-many-locals, too-many-branches
# manual merge
merged = None
if mask:
bands = [self._product.pq_band]
else:
bands = self.needed_bands()
for ds in datasets:
d = read_data(ds, measurements, self._geobox, **kwargs)
extent_mask = None
for band in bands:
for f in self._product.extent_mask_func:
if extent_mask is None:
extent_mask = f(d, band)
else:
extent_mask &= f(d, band)
dm = d.where(extent_mask)
if self._product.solar_correction and not mask and not skip_corrections:
for band in bands:
if band != self._product.pq_band:
dm[band] = solar_correct_data(dm[band], ds)
if merged is None:
merged = dm
else:
merged = merged.combine_first(dm)
if mask:
merged = merged.astype('uint8', copy=True)
for band in bands:
merged[band].attrs = d[band].attrs
return merged
def data(self, datasets, mask=False, manual_merge=False, skip_corrections=False, **kwargs):
# pylint: disable=too-many-locals, consider-using-enumerate
if mask:
prod = self._product.pq_product
measurements = [prod.measurements[self._product.pq_band].copy()]
else:
prod = self._product.product
measurements = [prod.measurements[name].copy() for name in self.needed_bands()]
with datacube.set_options(reproject_threads=1, fast_load=True):
if manual_merge:
return self.manual_data_stack(datasets, measurements, mask, skip_corrections, **kwargs)
elif self._product.solar_correction and not mask and not skip_corrections:
# Merge performed already by dataset extent, but we need to
# process the data for the datasets individually to do solar correction.
merged = None
for ds in datasets:
d = read_data(ds, measurements, self._geobox, **kwargs)
for band in self.needed_bands():
if band != self._product.pq_band:
d[band] = solar_correct_data(d[band], ds)
if merged is None:
merged = d
else:
merged = merged.combine_first(d)
return merged
else:
data = read_data(datasets, measurements, self._geobox, self._resampling, **kwargs)
return data
def manual_data_stack(self, datasets, measurements, mask, skip_corrections,
use_overviews, **kwargs):
#pylint: disable=too-many-locals, too-many-branches, consider-using-enumerate
# manual merge
merged = None
if mask:
bands = [self._product.pq_band]
else:
bands = self.needed_bands()
for i in range(0, len(datasets)):
holder = numpy.empty(shape=tuple(), dtype=object)
ds = datasets[i]
d = read_data(ds, measurements, self._geobox, use_overviews,
**kwargs)
extent_mask = None
for band in bands:
for f in self._product.extent_mask_func:
if extent_mask is None:
extent_mask = f(d, band)
else:
extent_mask &= f(d, band)
dm = d.where(extent_mask)
if self._product.solar_correction and not mask and not skip_corrections:
for band in bands:
if band != self._product.pq_band:
dm[band] = solar_correct_data(dm[band], ds)
if merged is None:
merged = dm
else:
merged = merged.combine_first(dm)
if mask:
merged = merged.astype('uint8', copy=True)
for band in bands:
merged[band].attrs = d[band].attrs
return merged
def manual_data_stack(self, datasets, measurements, mask, skip_corrections):
# manual merge
merged = None
if mask:
bands = [ self._product.pq_band ]
else:
bands = self.needed_bands()
for i in range(0, len(datasets)):
holder = numpy.empty(shape=tuple(), dtype=object)
ds = datasets[i]
holder[()] = [ ds ]
sources = xarray.DataArray(holder)
d = datacube.Datacube.load_data(sources, self._geobox, measurements)
extent_mask = None
for band in bands:
for f in self._product.extent_mask_func:
if extent_mask is None:
extent_mask = f(d, band)
else:
extent_mask &= f(d, band)
dm = d.where(extent_mask)
if self._product.solar_correction and not mask and not skip_corrections:
for band in bands:
if band != self._product.pq_band:
dm[band] = solar_correct_data(dm[band], ds)
if merged is None:
merged = dm
else:
merged = merged.combine_first(dm)
if mask:
merged = merged.astype('uint8', copy=True)
for band in bands:
merged[band].attrs = d[band].attrs
return merged
def data(self,
datasets,
mask=False,
manual_merge=False,
skip_corrections=False,
use_overviews=False,
**kwargs):
#pylint: disable=too-many-locals, consider-using-enumerate
if mask:
prod = self._product.pq_product
measurements = [prod.measurements[self._product.pq_band].copy()]
else:
prod = self._product.product
measurements = [
prod.measurements[name].copy() for name in self.needed_bands()
]
with datacube.set_options(reproject_threads=1, fast_load=True):
if manual_merge:
return self.manual_data_stack(datasets, measurements, mask,
skip_corrections, use_overviews,
**kwargs)
elif self._product.solar_correction and not mask and not skip_corrections:
# Merge performed already by dataset extent, but we need to
# process the data for the datasets individually to do solar correction.
merged = None
for i in range(0, len(datasets)):
holder = numpy.empty(shape=tuple(), dtype=object)
ds = datasets[i]
d = read_data(ds, measurements, self._geobox,
use_overviews, **kwargs)
for band in self.needed_bands():
if band != self._product.pq_band:
d[band] = solar_correct_data(d[band], ds)
if merged is None:
merged = d
else:
merged = merged.combine_first(d)
return merged
else:
# Merge performed already by dataset extent
if isinstance(datasets, xarray.DataArray):
sources = datasets
else:
holder = numpy.empty(shape=tuple(), dtype=object)
holder[()] = datasets
sources = xarray.DataArray(holder)
data = read_data(datasets, measurements, self._geobox,
use_overviews, **kwargs)
return data
