def group(self, datasets: VirtualDatasetBag,
**group_settings: Dict[str, Any]) -> VirtualDatasetBox:
self._assert(
'juxtapose' in datasets.bag
and len(datasets.bag['juxtapose']) == len(self._children),
"invalid dataset bag")
groups = [
product.group(
VirtualDatasetBag(dataset_bag, datasets.geopolygon,
datasets.product_definitions),
**group_settings) for product, dataset_bag in zip(
self._children, datasets.bag['juxtapose'])
]
aligned_boxes = xarray.align(*[grouped.box for grouped in groups])
def tuplify(indexes, _):
return {
'juxtapose':
[box.sel(**indexes).item() for box in aligned_boxes]
}
return VirtualDatasetBox(
xr_apply(aligned_boxes[0], tuplify),
select_unique([grouped.geobox for grouped in groups]),
select_unique([grouped.load_natively for grouped in groups]),
merge_dicts([grouped.product_definitions for grouped in groups]),
geopolygon=select_unique(
[grouped.geopolygon for grouped in groups]))
def ingest_work(config, source_type, output_type, index, sources, geobox):
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.product_data(sources, geobox, measurements, fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, index, sources)
def _make_dataset(labels, sources):
sources_union = union_points(*[source.extent.to_crs(geobox.crs).points for source in sources])
valid_data = intersect_points(geobox.extent.points, sources_union)
dataset = make_dataset(dataset_type=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config, config['filename']),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(sources, _make_dataset, dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(nudata, global_attributes, variable_params, file_path)
return datasets
def _dask_load(sources,
geobox,
measurements,
dask_chunks,
skip_broken_datasets=False):
needed_irr_chunks, grid_chunks = _calculate_chunk_sizes(
sources, geobox, dask_chunks)
gbt = GeoboxTiles(geobox, grid_chunks)
dsk = {}
def chunk_datasets(dss, gbt):
out = {}
for ds in dss:
dsk[_tokenize_dataset(ds)] = ds
for idx in gbt.tiles(ds.extent):
out.setdefault(idx, []).append(ds)
return out
chunked_srcs = xr_apply(sources,
lambda _, dss: chunk_datasets(dss, gbt),
dtype=object)
def data_func(measurement):
return _make_dask_array(chunked_srcs,
dsk,
gbt,
measurement,
chunks=needed_irr_chunks + grid_chunks,
skip_broken_datasets=skip_broken_datasets)
return Datacube.create_storage(
OrderedDict((dim, sources.coords[dim]) for dim in sources.dims),
geobox, measurements, data_func)
def ingest_work(config, source_type, output_type, tile, tile_index):
# pylint: disable=too-many-locals
_LOG.info('Starting task %s', tile_index)
driver = storage_writer_by_name(config['storage']['driver'])
if driver is None:
_LOG.error('Failed to load storage driver %s', config['storage']['driver'])
raise ValueError('Something went wrong: no longer can find driver pointed by storage.driver option')
namemap = get_namemap(config)
# TODO: get_measurements possibly changes dtype, not sure load_data would like that
measurements = get_measurements(source_type, config)
resampling = get_resampling(config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
datasets = tile.sources.sum().item()
for dataset in datasets:
if not dataset.uris:
_LOG.error('Locationless dataset found in the database: %r', dataset)
data = Datacube.load_data(tile.sources, tile.geobox, measurements,
resampling=resampling,
fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, tile_index, tile.sources)
file_uri = driver.mk_uri(file_path, config['storage'])
def _make_dataset(labels, sources):
return make_dataset(product=output_type,
sources=sources,
extent=tile.geobox.extent,
center_time=labels['time'],
uri=file_uri,
app_info=get_app_metadata(config['filename']),
valid_data=polygon_from_sources_extents(sources, tile.geobox))
datasets = xr_apply(tile.sources, _make_dataset, dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
variable_params['dataset'] = {
'chunksizes': (1,),
'zlib': True,
'complevel': 9,
}
driver_data = driver.write_dataset_to_storage(nudata, file_uri,
global_attributes=global_attributes,
variable_params=variable_params,
storage_config=config['storage'])
if (driver_data is not None) and len(driver_data) > 0:
datasets.attrs['driver_data'] = driver_data
_LOG.info('Finished task %s', tile_index)
return datasets
def _juxtapose_group_():
self._assert(
'juxtapose' in datasets.pile
and len(datasets.pile['juxtapose']) == len(self._children),
"invalid dataset pile")
groups = [
product.group(
VirtualDatasetBag(dataset_pile, datasets.grid_spec,
datasets.geopolygon,
datasets.product_definitions),
**search_terms) for product, dataset_pile in zip(
self._children, datasets.pile['juxtapose'])
]
aligned_piles = xarray.align(*[grouped.pile for grouped in groups])
def tuplify(indexes, _):
return {
'juxtapose':
[pile.sel(**indexes).item() for pile in aligned_piles]
}
return VirtualDatasetBox(
xr_apply(aligned_piles[0], tuplify),
select_unique([grouped.geobox for grouped in groups]),
merge_dicts(
[grouped.product_definitions for grouped in groups]))
def do_fixer_task(config, task):
global_attributes = config['global_attributes']
# Don't keep the original history if we are trying to fix it
global_attributes['history'] = build_history_string(config,
task,
keep_original=False)
variable_params = config['variable_params']
output_filename = Path(task['output_filename'])
output_uri = output_filename.absolute().as_uri()
temp_filename = get_temp_file(output_filename)
tile = task['tile']
# Only use the time chunk size (eg 5), but not spatial chunks
# This means the file only gets opened once per band, and all data is available when compressing on write
# 5 * 4000 * 4000 * 2bytes == 152MB, so mem usage is not an issue
chunk_profile = {'time': config['storage']['chunking']['time']}
data = datacube.api.GridWorkflow.load(tile, dask_chunks=chunk_profile)
unwrapped_datasets = xr_apply(tile.sources,
_unwrap_dataset_list,
dtype='O')
data['dataset'] = datasets_to_doc(unwrapped_datasets)
try:
if data.geobox is None:
raise DatacubeException(
'Dataset geobox property is None, cannot write to NetCDF file.'
)
if data.geobox.crs is None:
raise DatacubeException(
'Dataset geobox.crs property is None, cannot write to NetCDF file.'
)
nco = create_netcdf_storage_unit(temp_filename, data.geobox.crs,
data.coords, data.data_vars,
variable_params, global_attributes)
write_data_variables(data.data_vars, nco)
nco.close()
temp_filename.rename(output_filename)
if config.get('check_data_identical', False):
new_tile = make_updated_tile(unwrapped_datasets, output_uri,
tile.geobox)
new_data = datacube.api.GridWorkflow.load(
new_tile, dask_chunks=chunk_profile)
check_identical(data, new_data, output_filename)
except Exception as e:
if temp_filename.exists():
temp_filename.unlink()
raise e
return unwrapped_datasets, output_uri
def _do_fc_task(config, task):
"""
Load data, run FC algorithm, attach metadata, and write output.
:param dict config: Config object
:param dict task: Dictionary of tasks
:return: Dataset objects representing the generated data that can be added to the index
:rtype: list(datacube.model.Dataset)
"""
global_attributes = config['global_attributes']
variable_params = config['variable_params']
output_product = config['fc_product']
file_path = Path(task['filename_dataset'])
uri, band_uris = calc_uris(file_path, variable_params)
output_measurements = config['fc_product'].measurements.values()
nbart = io.native_load(task['dataset'], measurements=config['load_bands'])
if config['band_mapping'] is not None:
nbart = nbart.rename(config['band_mapping'])
fc_dataset = run_fc(nbart, output_measurements,
config.get('sensor_regression_coefficients'))
def _make_dataset(labels, sources):
assert sources
dataset = make_dataset(product=output_product,
sources=sources,
extent=nbart.geobox.extent,
center_time=labels['time'],
uri=uri,
band_uris=band_uris,
app_info=_get_app_metadata(config),
valid_data=polygon_from_sources_extents(
sources, nbart.geobox))
return dataset
source = Datacube.group_datasets([task['dataset']], 'time')
datasets = xr_apply(source, _make_dataset, dtype='O')
fc_dataset['dataset'] = datasets_to_doc(datasets)
base, ext = os.path.splitext(file_path)
if ext == '.tif':
dataset_to_geotif_yaml(
dataset=fc_dataset,
odc_dataset=datasets.item(),
filename=file_path,
variable_params=variable_params,
)
else:
write_dataset_to_netcdf(
dataset=fc_dataset,
filename=file_path,
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
def make_updated_tile(old_datasets, new_uri, geobox):
def update_dataset_location(labels, dataset: Dataset) -> List[Dataset]:
new_dataset = copy.copy(dataset)
new_dataset.uris = [new_uri]
return [new_dataset]
updated_datasets = xr_apply(old_datasets, update_dataset_location, dtype='O')
return datacube.api.Tile(sources=updated_datasets, geobox=geobox)
def test_xr_apply():
src = xr.DataArray(np.asarray([1, 2, 3], dtype='uint8'), dims=['time'])
dst = xr_apply(src, lambda _, v: v, dtype='float32')
assert dst.dtype.name == 'float32'
assert dst.shape == src.shape
assert dst.values.tolist() == [1, 2, 3]
dst = xr_apply(src, lambda _, v: v)
assert dst.dtype.name == 'uint8'
assert dst.shape == src.shape
assert dst.values.tolist() == [1, 2, 3]
dst = xr_apply(src, lambda idx, _, v: idx[0] + v, with_numeric_index=True)
assert dst.dtype.name == 'uint8'
assert dst.shape == src.shape
assert dst.values.tolist() == [0 + 1, 1 + 2, 2 + 3]
def make_updated_tile(old_datasets, new_uri, geobox):
def update_dataset_location(idx, labels, dataset: Dataset) -> List[Dataset]:
idx, = idx
new_dataset = copy.copy(dataset)
new_dataset.uris = [mk_part_uri(new_uri, idx)]
return [new_dataset]
updated_datasets = xr_apply(old_datasets, update_dataset_location, with_numeric_index=True)
return datacube.api.Tile(sources=updated_datasets, geobox=geobox)
def ingest_work(config, source_type, output_type, tile, tile_index):
# pylint: disable=too-many-locals
_LOG.info('Starting task %s', tile_index)
driver = storage_writer_by_name(config['storage']['driver'])
if driver is None:
_LOG.error('Failed to load storage driver %s', config['storage']['driver'])
raise ValueError('Something went wrong: no longer can find driver pointed by storage.driver option')
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.load_data(tile.sources, tile.geobox, measurements, fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, tile_index, tile.sources)
def mk_uri(file_path):
if driver.uri_scheme == "file":
return file_path.absolute().as_uri()
return '{}://{}'.format(driver.uri_scheme, file_path)
def _make_dataset(labels, sources):
return make_dataset(product=output_type,
sources=sources,
extent=tile.geobox.extent,
center_time=labels['time'],
uri=mk_uri(file_path),
app_info=get_app_metadata(config, config['filename']),
valid_data=GeoPolygon.from_sources_extents(sources, tile.geobox))
datasets = xr_apply(tile.sources, _make_dataset, dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
variable_params['dataset'] = {
'chunksizes': (1,),
'zlib': True,
'complevel': 9,
}
storage_metadata = driver.write_dataset_to_storage(nudata, file_path,
global_attributes=global_attributes,
variable_params=variable_params,
storage_config=config['storage'])
if (storage_metadata is not None) and len(storage_metadata) > 0:
datasets.attrs['storage_metadata'] = storage_metadata
_LOG.info('Finished task %s', tile_index)
return datasets
def do_stack_task(task):
datasets_to_add = None
datasets_to_update = None
datasets_to_archive = None
global_attributes = task['global_attributes']
variable_params = task['variable_params']
output_filename = Path(task['output_filename'])
tile = task['tile']
if task.get('make_new_datasets', False):
datasets_to_add = make_datasets(tile, output_filename, task)
datasets_to_archive = xr_apply(tile.sources, _single_dataset, dtype='O')
output_datasets = datasets_to_add
else:
datasets_to_update = xr_apply(tile.sources, _single_dataset, dtype='O')
output_datasets = datasets_to_update
data = datacube.api.GridWorkflow.load(tile, dask_chunks=dict(time=1)) # TODO: chunk along output NetCDF chunk?
data['dataset'] = datasets_to_doc(output_datasets)
nco = create_netcdf_storage_unit(output_filename,
data.crs,
data.coords,
data.data_vars,
variable_params,
global_attributes)
for name, variable in data.data_vars.items():
try:
with dask.set_options(get=dask.async.get_sync):
da.store(variable.data, nco[name], lock=True)
except ValueError:
nco[name][:] = netcdf_writer.netcdfy_data(variable.values)
nco.sync()
nco.close()
return datasets_to_add, datasets_to_update, datasets_to_archive
def make_datasets(tile, file_path, config):
def _make_dataset(labels, sources):
new_dataset = make_dataset(product=tile.product,
sources=sources,
extent=tile.geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config),
valid_data=sources[0].extent)
return new_dataset
return xr_apply(tile.sources, _make_dataset, dtype='O')
def do_stack_task(config, task):
global_attributes = config['global_attributes']
global_attributes['history'] = get_history_attribute(config, task)
variable_params = config['variable_params']
variable_params['dataset'] = {
'chunksizes': (1, ),
'zlib': True,
'complevel': 9,
}
output_filename = Path(task['output_filename'])
output_uri = output_filename.absolute().as_uri()
temp_filename = get_temp_file(output_filename)
tile = task['tile']
# Only use the time chunk size (eg 5), but not spatial chunks
# This means the file only gets opened once per band, and all data is available when compressing on write
# 5 * 4000 * 4000 * 2bytes == 152MB, so mem usage is not an issue
chunk_profile = {'time': config['storage']['chunking']['time']}
data = datacube.api.GridWorkflow.load(tile, dask_chunks=chunk_profile)
unwrapped_datasets = xr_apply(tile.sources,
_unwrap_dataset_list,
dtype='O')
data['dataset'] = datasets_to_doc(unwrapped_datasets)
try:
nco = create_netcdf_storage_unit(temp_filename, data.crs, data.coords,
data.data_vars, variable_params,
global_attributes)
write_data_variables(data.data_vars, nco)
nco.close()
temp_filename.rename(output_filename)
if config.get('check_data_identical', False):
new_tile = make_updated_tile(unwrapped_datasets, output_uri,
tile.geobox)
new_data = datacube.api.GridWorkflow.load(
new_tile, dask_chunks=chunk_profile)
check_identical(data, new_data, output_filename)
except Exception as e:
if temp_filename.exists():
temp_filename.unlink()
raise e
return unwrapped_datasets, output_uri
def do_ndvi_task(config, task):
global_attributes = config['global_attributes']
variable_params = config['variable_params']
file_path = Path(task['filename'])
output_type = config['ndvi_dataset_type']
measurement = output_type.measurements['ndvi']
output_dtype = np.dtype(measurement['dtype'])
nodata_value = np.dtype(output_dtype).type(measurement['nodata'])
if file_path.exists():
raise OSError(errno.EEXIST, 'Output file already exists',
str(file_path))
measurements = ['red', 'nir']
nbar_tile = task['nbar']
nbar = GridWorkflow.load(nbar_tile, measurements)
ndvi = calculate_ndvi(nbar,
nodata=nodata_value,
dtype=output_dtype,
units=measurement['units'])
def _make_dataset(labels, sources):
assert len(sources)
geobox = nbar.geobox
source_data = union_points(
*[dataset.extent.to_crs(geobox.crs).points for dataset in sources])
valid_data = intersect_points(geobox.extent.points, source_data)
dataset = make_dataset(product=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(nbar_tile.sources, _make_dataset, dtype='O')
ndvi['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(
dataset=ndvi,
filename=Path(file_path),
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
def ingest_work(driver_manager, config, source_type, output_type, tile,
tile_index):
_LOG.info('Starting task %s', tile_index)
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.load_data(tile.sources,
tile.geobox,
measurements,
fuse_func=fuse_func,
driver_manager=driver_manager)
nudata = data.rename(namemap)
file_path = get_filename(config, tile_index, tile.sources)
def _make_dataset(labels, sources):
return make_dataset(product=output_type,
sources=sources,
extent=tile.geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config,
config['filename']),
valid_data=GeoPolygon.from_sources_extents(
sources, tile.geobox))
datasets = xr_apply(
tile.sources, _make_dataset,
dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
# Until ingest becomes a class and DriverManager an instance
# variable, we call the constructor each time. DriverManager being
# a singleton, there is little overhead, though.
datasets.attrs['storage_output'] = driver_manager.write_dataset_to_storage(
nudata, file_path, global_attributes, variable_params)
_LOG.info('Finished task %s', tile_index)
# When using multiproc executor, Driver Manager is a clone.
if driver_manager.is_clone:
driver_manager.close()
return datasets
def _do_fc_task(config, task):
"""
Load data, run FC algorithm, attach metadata, and write output.
:param dict config: Config object
:param dict task: Dictionary of tasks
:return: Dataset objects representing the generated data that can be added to the index
:rtype: list(datacube.model.Dataset)
"""
global_attributes = config['global_attributes']
variable_params = config['variable_params']
file_path = Path(task['filename'])
output_product = config['fc_product']
if file_path.exists():
raise OSError(errno.EEXIST, 'Output file already exists',
str(file_path))
nbart_tile: Tile = task['nbart']
nbart = GridWorkflow.load(nbart_tile,
['green', 'red', 'nir', 'swir1', 'swir2'])
output_measurements = config['fc_product'].measurements.values()
fc_dataset = _make_fc_tile(nbart, output_measurements,
config.get('sensor_regression_coefficients'))
def _make_dataset(labels, sources):
assert sources
dataset = make_dataset(product=output_product,
sources=sources,
extent=nbart.geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=_get_app_metadata(config),
valid_data=polygon_from_sources_extents(
sources, nbart.geobox))
return dataset
datasets = xr_apply(nbart_tile.sources, _make_dataset, dtype='O')
fc_dataset['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(
dataset=fc_dataset,
filename=file_path,
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
def do_ndvi_task(config, task):
global_attributes = config['global_attributes']
variable_params = config['variable_params']
file_path = Path(task['filename'])
output_type = config['ndvi_dataset_type']
measurement = output_type.measurements['ndvi']
output_dtype = np.dtype(measurement['dtype'])
nodata_value = np.dtype(output_dtype).type(measurement['nodata'])
if file_path.exists():
raise OSError(errno.EEXIST, 'Output file already exists', str(file_path))
measurements = ['red', 'nir']
nbar_tile = task['nbar']
nbar = GridWorkflow.load(nbar_tile, measurements)
ndvi = calculate_ndvi(nbar, nodata=nodata_value, dtype=output_dtype, units=measurement['units'])
def _make_dataset(labels, sources):
assert len(sources)
geobox = nbar.geobox
source_data = union_points(*[dataset.extent.to_crs(geobox.crs).points for dataset in sources])
valid_data = intersect_points(geobox.extent.points, source_data)
dataset = make_dataset(product=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(nbar_tile.sources, _make_dataset, dtype='O')
ndvi['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(
dataset=ndvi,
filename=Path(file_path),
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
def _dask_load(sources,
geobox,
measurements,
dask_chunks,
skip_broken_datasets=False,
extra_dims=None):
chunk_sizes = _calculate_chunk_sizes(sources, geobox, dask_chunks,
extra_dims)
needed_irr_chunks = chunk_sizes[0]
if extra_dims:
extra_dim_chunks = chunk_sizes[1]
grid_chunks = chunk_sizes[-1]
gbt = GeoboxTiles(geobox, grid_chunks)
dsk = {}
def chunk_datasets(dss, gbt):
out = {}
for ds in dss:
dsk[_tokenize_dataset(ds)] = ds
for idx in gbt.tiles(ds.extent):
out.setdefault(idx, []).append(ds)
return out
chunked_srcs = xr_apply(sources,
lambda _, dss: chunk_datasets(dss, gbt),
dtype=object)
def data_func(measurement, shape):
if 'extra_dim' in measurement:
chunks = needed_irr_chunks + extra_dim_chunks + grid_chunks
else:
chunks = needed_irr_chunks + grid_chunks
return _make_dask_array(chunked_srcs,
dsk,
gbt,
measurement,
chunks=chunks,
skip_broken_datasets=skip_broken_datasets,
extra_dims=extra_dims)
return Datacube.create_storage(sources.coords, geobox, measurements,
data_func, extra_dims)
def ingest_work(config, source_type, output_type, index, sources, geobox):
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.product_data(sources,
geobox,
measurements,
fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, index, sources)
def _make_dataset(labels, sources):
sources_union = union_points(
*[source.extent.to_crs(geobox.crs).points for source in sources])
valid_data = intersect_points(geobox.extent.points, sources_union)
dataset = make_dataset(dataset_type=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(
config, config['filename']),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(
sources, _make_dataset,
dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(nudata, global_attributes, variable_params,
file_path)
return datasets
def ingest_work(config, source_type, output_type, tile, tile_index):
_LOG.info('Starting task %s', tile_index)
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.load_data(tile.sources,
tile.geobox,
measurements,
fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, tile_index, tile.sources)
def _make_dataset(labels, sources):
return make_dataset(product=output_type,
sources=sources,
extent=tile.geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config,
config['filename']),
valid_data=GeoPolygon.from_sources_extents(
sources, tile.geobox))
datasets = xr_apply(
tile.sources, _make_dataset,
dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(nudata, file_path, global_attributes,
variable_params)
_LOG.info('Finished task %s', tile_index)
return datasets
def do_fc_task(config, task):
global_attributes = config['global_attributes']
variable_params = config['variable_params']
file_path = Path(task['filename'])
output_product = config['fc_product']
if file_path.exists():
raise OSError(errno.EEXIST, 'Output file already exists', str(file_path))
nbar_tile: Tile = task['nbar']
nbar = GridWorkflow.load(nbar_tile, ['green', 'red', 'nir', 'swir1', 'swir2'])
output_measurements = config['fc_product'].measurements.values()
fc_dataset = make_fc_tile(nbar, output_measurements, config.get('sensor_regression_coefficients'))
def _make_dataset(labels, sources):
assert sources
dataset = make_dataset(product=output_product,
sources=sources,
extent=nbar.geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config),
valid_data=GeoPolygon.from_sources_extents(sources, nbar.geobox))
return dataset
datasets = xr_apply(nbar_tile.sources, _make_dataset, dtype='O')
fc_dataset['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(
dataset=fc_dataset,
filename=file_path,
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
def map(self, func, dtype='O'):
return DatasetPile(xr_apply(self.pile, func, dtype=dtype), self.geobox)
def map(self, func, dtype='O'):
return VirtualDatasetBox(xr_apply(self.box, func, dtype=dtype),
self.geobox,
self.load_natively,
self.product_definitions,
geopolygon=self.geopolygon)
def map(self, func, dtype='O'):
return VirtualDatasetBox(xr_apply(self.pile, func, dtype=dtype),
self.geobox, self.product_definitions)
def _find_source_datasets(self,
stat: OutputProduct,
uri: str = None,
band_uris: dict = None) -> xarray.DataArray:
"""
Find all the source datasets for a task
Put them in order so that they can be assigned to a stacked output aligned against it's time dimension
:return: (datasets, sources)
datasets is a bunch of strings to dump, indexed on time
sources is more structured. An x-array of lists of dataset sources, indexed on time
"""
task = self._task
geobox = self._task.geobox
app_info = self._app_info
def add_all(iterable):
return reduce_(operator.add, iterable)
def merge_sources(prod):
# Merge data sources and mask sources
# Align the data `Tile` with potentially many mask `Tile`s along their time axis
all_sources = xarray.align(
prod.data.sources,
*[mask_tile.sources for mask_tile in prod.masks if mask_tile])
# TODO: The following can fail if prod.data and prod.masks have different times
# Which can happen in the case of a missing PQ Scene, where there is a scene overlap
# ie. Two overlapped NBAR scenes, One PQ scene (the later)
return add_all(sources_.sum() for sources_ in all_sources)
sources = add_all(merge_sources(prod) for prod in task.sources)
def unique(index, dataset_tuple):
return tuple(set(dataset_tuple))
sources = xr_apply(sources, unique, dtype='O')
# Sources has no time at this point, so insert back in the start of our stats epoch
start_time, _ = task.time_period
sources = unsqueeze_data_array(sources,
dim='time',
pos=0,
coord=start_time,
attrs=task.time_attributes)
if not sources:
raise StatsOutputError(
'No valid sources found, or supplied sources do not align to the same time.\n'
'Unable to write dataset metadata.')
def _make_dataset(labels, sources_):
return make_dataset(product=stat.product,
sources=sources_,
extent=geobox.extent,
center_time=labels['time'],
uri=uri,
band_uris=band_uris,
app_info=app_info,
valid_data=polygon_from_sources_extents(
sources_, geobox))
datasets = xr_apply(
sources, _make_dataset,
dtype='O') # Store in DataArray to associate Time -> Dataset
datasets = datasets_to_doc(datasets)
return datasets
def group(self, datasets: VirtualDatasetBag,
**search_terms: Dict[str, Any]) -> VirtualDatasetBox:
"""
Datasets grouped by their timestamps.
:param datasets: the `VirtualDatasetBag` to fetch data from
:param query: to specify a spatial sub-region
"""
grid_spec = datasets.grid_spec
geopolygon = datasets.geopolygon
if 'product' in self:
# select only those inside the ROI
# ROI could be smaller than the query for the `query` method
if query_geopolygon(**search_terms) is not None:
geopolygon = query_geopolygon(**search_terms)
selected = list(
select_datasets_inside_polygon(datasets.pile, geopolygon))
else:
selected = list(datasets.pile)
# geobox
merged = merge_search_terms(
select_keys(self, self._NON_SPATIAL_KEYS),
select_keys(search_terms, self._NON_SPATIAL_KEYS))
geobox = output_geobox(datasets=selected,
grid_spec=grid_spec,
geopolygon=geopolygon,
**select_keys(merged, self._GEOBOX_KEYS))
# group by time
group_query = query_group_by(
**select_keys(merged, self._GROUPING_KEYS))
# information needed for Datacube.load_data
return VirtualDatasetBox(
Datacube.group_datasets(selected, group_query), geobox,
datasets.product_definitions)
elif 'transform' in self:
return self._input.group(datasets, **search_terms)
elif 'collate' in self:
self._assert(
'collate' in datasets.pile
and len(datasets.pile['collate']) == len(self._children),
"invalid dataset pile")
def build(source_index, product, dataset_pile):
grouped = product.group(
VirtualDatasetBag(dataset_pile, datasets.grid_spec,
datasets.geopolygon,
datasets.product_definitions),
**search_terms)
def tag(_, value):
return {'collate': (source_index, value)}
return grouped.map(tag)
groups = [
build(source_index, product, dataset_pile)
for source_index, (product, dataset_pile) in enumerate(
zip(self._children, datasets.pile['collate']))
]
return VirtualDatasetBox(
xarray.concat([grouped.pile for grouped in groups],
dim='time'),
select_unique([grouped.geobox for grouped in groups]),
merge_dicts(
[grouped.product_definitions for grouped in groups]))
elif 'juxtapose' in self:
self._assert(
'juxtapose' in datasets.pile
and len(datasets.pile['juxtapose']) == len(self._children),
"invalid dataset pile")
groups = [
product.group(
VirtualDatasetBag(dataset_pile, datasets.grid_spec,
datasets.geopolygon,
datasets.product_definitions),
**search_terms) for product, dataset_pile in zip(
self._children, datasets.pile['juxtapose'])
]
aligned_piles = xarray.align(*[grouped.pile for grouped in groups])
def tuplify(indexes, _):
return {
'juxtapose':
[pile.sel(**indexes).item() for pile in aligned_piles]
}
return VirtualDatasetBox(
xr_apply(aligned_piles[0], tuplify),
select_unique([grouped.geobox for grouped in groups]),
merge_dicts(
[grouped.product_definitions for grouped in groups]))
else:
raise VirtualProductException("virtual product was not validated")
