def test_load_data(tmpdir):
tmpdir = Path(str(tmpdir))
group_by = query_group_by('time')
spatial = dict(resolution=(15, -15),
offset=(11230, 1381110),)
nodata = -999
aa = mk_test_image(96, 64, 'int16', nodata=nodata)
ds, gbox = gen_tiff_dataset([SimpleNamespace(name='aa', values=aa, nodata=nodata)],
tmpdir,
prefix='ds1-',
timestamp='2018-07-19',
**spatial)
assert ds.time is not None
ds2, _ = gen_tiff_dataset([SimpleNamespace(name='aa', values=aa, nodata=nodata)],
tmpdir,
prefix='ds2-',
timestamp='2018-07-19',
**spatial)
assert ds.time is not None
assert ds.time == ds2.time
sources = Datacube.group_datasets([ds], 'time')
sources2 = Datacube.group_datasets([ds, ds2], group_by)
mm = ['aa']
mm = [ds.type.measurements[k] for k in mm]
ds_data = Datacube.load_data(sources, gbox, mm)
assert ds_data.aa.nodata == nodata
np.testing.assert_array_equal(aa, ds_data.aa.values[0])
custom_fuser_call_count = 0
def custom_fuser(dest, delta):
nonlocal custom_fuser_call_count
custom_fuser_call_count += 1
dest[:] += delta
progress_call_data = []
def progress_cbk(n, nt):
progress_call_data.append((n, nt))
ds_data = Datacube.load_data(sources2, gbox, mm, fuse_func=custom_fuser,
progress_cbk=progress_cbk)
assert ds_data.aa.nodata == nodata
assert custom_fuser_call_count > 0
np.testing.assert_array_equal(nodata + aa + aa, ds_data.aa.values[0])
assert progress_call_data == [(1, 2), (2, 2)]
def group(self, datasets: VirtualDatasetBag, **group_settings: Dict[str, Any]) -> VirtualDatasetBox:
geopolygon = datasets.geopolygon
selected = list(datasets.bag)
# geobox
merged = merge_search_terms(self, group_settings)
try:
geobox = output_geobox(datasets=selected,
grid_spec=datasets.product_definitions[self._product].grid_spec,
geopolygon=geopolygon, **select_keys(merged, self._GEOBOX_KEYS))
load_natively = False
except ValueError:
# we are not calculating geoboxes here for the moment
# since it may require filesystem access
# in ODC 2.0 the dataset should know the information required
geobox = None
load_natively = True
# 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,
load_natively,
datasets.product_definitions,
geopolygon=None if not load_natively else geopolygon)
def __call__(self, index, product, time, group_by) -> Tile:
# Do for a specific poly whose boundary is known
output_crs = CRS(self.storage['crs'])
filtered_items = [
'geopolygon', 'lon', 'lat', 'longitude', 'latitude', 'x', 'y'
]
filtered_dict = {
k: v
for k, v in self.input_region.items() if k in filtered_items
}
if self.feature is not None:
filtered_dict['geopolygon'] = self.feature.geopolygon
geopoly = filtered_dict['geopolygon']
else:
geopoly = query_geopolygon(**self.input_region)
dc = Datacube(index=index)
datasets = dc.find_datasets(product=product,
time=time,
group_by=group_by,
**filtered_dict)
group_by = query_group_by(group_by=group_by)
sources = dc.group_datasets(datasets, group_by)
output_resolution = [
self.storage['resolution'][dim] for dim in output_crs.dimensions
]
geopoly = geopoly.to_crs(output_crs)
geobox = GeoBox.from_geopolygon(geopoly, resolution=output_resolution)
return Tile(sources, geobox)
def native_load(ds, measurements=None, basis=None, **kw):
"""Load single dataset in native resolution.
:param ds: Dataset
:param measurements: List of band names to load
:param basis: Name of the band to use for computing reference frame, other
bands might be reprojected if they use different pixel grid
:param **kw: Any other parameter load_data accepts
:return: Xarray dataset
"""
from datacube import Datacube
geobox = native_geobox(
ds, measurements,
basis) # early exit via exception if no compatible grid exists
if measurements is not None:
mm = [ds.type.measurements[n] for n in measurements]
else:
mm = ds.type.measurements
return Datacube.load_data(Datacube.group_datasets([ds], 'time'),
geobox,
measurements=mm,
**kw)
def test_grouping_datasets():
def group_func(d):
return d.time
dimension = 'time'
units = None
datasets = [
SimpleNamespace(time=datetime.datetime(2016, 1, 1),
value='foo',
id=UUID(int=10)),
SimpleNamespace(time=datetime.datetime(2016, 2, 1),
value='bar',
id=UUID(int=1)),
SimpleNamespace(time=datetime.datetime(2016, 1, 1),
value='flim',
id=UUID(int=9)),
]
group_by = GroupBy(dimension, group_func, units, sort_key=group_func)
grouped = Datacube.group_datasets(datasets, group_by)
dss = grouped.isel(time=0).values[()]
assert isinstance(dss, tuple)
assert len(dss) == 2
assert [ds.value for ds in dss] == ['flim', 'foo']
dss = grouped.isel(time=1).values[()]
assert isinstance(dss, tuple)
assert len(dss) == 1
assert [ds.value for ds in dss] == ['bar']
assert str(grouped.time.dtype) == 'datetime64[ns]'
assert grouped.loc['2016-01-01':'2016-01-15']
def check_data_with_api(index, time_slices):
"""Chek retrieved data for specific values.
We scale down by 100 and check for predefined values in the
corners.
"""
from datacube import Datacube
dc = Datacube(index=index)
# Make the retrieved data 100 less granular
shape_x = int(GEOTIFF['shape']['x'] / 100.0)
shape_y = int(GEOTIFF['shape']['y'] / 100.0)
pixel_x = int(GEOTIFF['pixel_size']['x'] * 100)
pixel_y = int(GEOTIFF['pixel_size']['y'] * 100)
input_type_name = 'ls5_nbar_albers'
input_type = dc.index.products.get_by_name(input_type_name)
geobox = geometry.GeoBox(
shape_x + 1, shape_y + 1,
Affine(pixel_x, 0.0, GEOTIFF['ul']['x'], 0.0, pixel_y,
GEOTIFF['ul']['y']), geometry.CRS(GEOTIFF['crs']))
observations = dc.find_datasets(product='ls5_nbar_albers',
geopolygon=geobox.extent)
group_by = query_group_by('time')
sources = dc.group_datasets(observations, group_by)
data = dc.load_data(sources, geobox, input_type.measurements.values())
assert hashlib.md5(
data.green.data).hexdigest() == '7f5ace486e88d33edf3512e8de6b6996'
assert hashlib.md5(
data.blue.data).hexdigest() == 'b58204f1e10dd678b292df188c242c7e'
for time_slice in range(time_slices):
assert data.blue.values[time_slice][-1, -1] == -999
def _product_group_():
# 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:
geopolygon = datasets.geopolygon
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=datasets.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)
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 _group_datasets_by_date(datasets):
def group_func(d):
return d['time'].date()
def sort_key(d):
return d['time']
dimension = 'time'
units = None
group_by = GroupBy(dimension, group_func, units, sort_key)
return Datacube.group_datasets(datasets, group_by)
def check_open_with_api(driver_manager, time_slices):
from datacube import Datacube
dc = Datacube(driver_manager=driver_manager)
input_type_name = 'ls5_nbar_albers'
input_type = dc.index.products.get_by_name(input_type_name)
geobox = geometry.GeoBox(200, 200, Affine(25, 0.0, 638000, 0.0, -25, 6276000), geometry.CRS('EPSG:28355'))
observations = dc.find_datasets(product='ls5_nbar_albers', geopolygon=geobox.extent)
group_by = query_group_by('time')
sources = dc.group_datasets(observations, group_by)
data = dc.load_data(sources, geobox, input_type.measurements.values(), driver_manager=driver_manager)
assert data.blue.shape == (time_slices, 200, 200)
def __call__(self, tile_idx, _y=None):
from datacube import Datacube
from datacube.api.grid_workflow import Tile
if _y is not None:
tile_idx = (tile_idx, _y)
k = self._key_fmt.format(*tile_idx)
dss = list(self._cache.stream_group(k))
geobox = self._grid_spec.tile_geobox(tile_idx)
sources = Datacube.group_datasets(dss, self._grouper)
return Tile(sources, geobox)
def __call__(self, tile_idx, _y=None, group_by=None):
if _y is not None:
tile_idx = (tile_idx, _y)
if group_by is None:
group_by = self._default_groupby
dss = list(self._cache.stream_grid_tile(tile_idx, grid=self._grid))
if group_by == "nothing":
sources = group_by_nothing(dss)
else:
sources = Datacube.group_datasets(dss, group_by)
geobox = self._gs.tile_geobox(tile_idx)
return Tile(sources, geobox)
def test_group_datasets_by_time():
bands = [dict(name='a')]
# Same time instant but one explicitly marked as UTC
ds1 = mk_sample_dataset(bands, timestamp="2019-01-01T23:24:00Z")
ds2 = mk_sample_dataset(bands, timestamp="2019-01-01T23:24:00")
# Same "time" but in a different timezone, and actually later
ds3 = mk_sample_dataset(bands, timestamp="2019-01-01T23:24:00-1")
assert ds1.center_time.tzinfo is not None
assert ds2.center_time.tzinfo is None
assert ds3.center_time.tzinfo is not None
xx = Datacube.group_datasets([ds1, ds2, ds3], 'time')
assert xx.time.shape == (2, )
assert len(xx.data[0]) == 2
assert len(xx.data[1]) == 1
def check_open_with_api(index):
from datacube import Datacube
dc = Datacube(index=index)
input_type_name = 'ls5_nbar_albers'
input_type = dc.index.products.get_by_name(input_type_name)
geobox = GeoBox(200, 200, Affine(25, 0.0, 1500000, 0.0, -25, -3900000),
CRS('EPSG:3577'))
observations = dc.find_datasets(product='ls5_nbar_albers',
geopolygon=geobox.extent)
group_by = query_group_by('time')
sources = dc.group_datasets(observations, group_by)
data = dc.load_data(sources, geobox, input_type.measurements.values())
assert data.blue.shape == (1, 200, 200)
class ArbitraryTileMaker(object):
"""
Create a :class:`Tile` which can be used by :class:`GridWorkflow` to later load the required data.
:param input_region: dictionary of spatial limits for searching for datasets. eg:
geopolygon
lat, lon boundaries
"""
def __init__(self, index, input_region, storage):
self.dc = Datacube(index=index)
self.input_region = input_region
self.storage = storage
def __call__(self, product, time, group_by) -> Tile:
# Do for a specific poly whose boundary is known
output_crs = CRS(self.storage['crs'])
filtered_item = [
'geopolygon', 'lon', 'lat', 'longitude', 'latitude', 'x', 'y'
]
filtered_dict = {
k: v
for k, v in filter(lambda t: t[0] in filtered_item,
self.input_region.items())
}
if 'feature_id' in self.input_region:
filtered_dict['geopolygon'] = Geometry(
self.input_region['geom_feat'],
CRS(self.input_region['crs_txt']))
geopoly = filtered_dict['geopolygon']
else:
geopoly = query_geopolygon(**self.input_region)
datasets = self.dc.find_datasets(product=product,
time=time,
group_by=group_by,
**filtered_dict)
group_by = query_group_by(group_by=group_by)
sources = self.dc.group_datasets(datasets, group_by)
output_resolution = [
self.storage['resolution'][dim] for dim in output_crs.dimensions
]
geopoly = geopoly.to_crs(output_crs)
geobox = GeoBox.from_geopolygon(geopoly, resolution=output_resolution)
return Tile(sources, geobox)
def check_open_with_api(index, time_slices):
with rasterio.Env():
from datacube import Datacube
dc = Datacube(index=index)
input_type_name = 'ls5_nbar_albers'
input_type = dc.index.products.get_by_name(input_type_name)
geobox = geometry.GeoBox(200, 200, Affine(25, 0.0, 638000, 0.0, -25, 6276000), geometry.CRS('EPSG:28355'))
observations = dc.find_datasets(product='ls5_nbar_albers', geopolygon=geobox.extent)
group_by = query_group_by('time')
sources = dc.group_datasets(observations, group_by)
data = dc.load_data(sources, geobox, input_type.measurements.values())
assert data.blue.shape == (time_slices, 200, 200)
chunk_profile = {'time': 1, 'x': 100, 'y': 100}
lazy_data = dc.load_data(sources, geobox, input_type.measurements.values(), dask_chunks=chunk_profile)
assert lazy_data.blue.shape == (time_slices, 200, 200)
assert (lazy_data.blue.load() == data.blue).all()
def check_data_with_api(index, time_slices):
"""Chek retrieved data for specific values.
We scale down by 100 and check for predefined values in the
corners.
"""
from datacube import Datacube
dc = Datacube(index=index)
# TODO: this test needs to change, it tests that results are exactly the
# same as some time before, but with the current zoom out factor it's
# hard to verify that results are as expected even with human
# judgement. What it should test is that reading native from the
# ingested product gives exactly the same results as reading into the
# same GeoBox from the original product. Separate to that there
# should be a read test that confirms that what you read from native
# product while changing projection is of expected value
# Make the retrieved data lower res
ss = 100
shape_x = int(GEOTIFF['shape']['x'] / ss)
shape_y = int(GEOTIFF['shape']['y'] / ss)
pixel_x = int(GEOTIFF['pixel_size']['x'] * ss)
pixel_y = int(GEOTIFF['pixel_size']['y'] * ss)
input_type_name = 'ls5_nbar_albers'
input_type = dc.index.products.get_by_name(input_type_name)
geobox = geometry.GeoBox(
shape_x + 2, shape_y + 2,
Affine(pixel_x, 0.0, GEOTIFF['ul']['x'], 0.0, pixel_y,
GEOTIFF['ul']['y']), geometry.CRS(GEOTIFF['crs']))
observations = dc.find_datasets(product='ls5_nbar_albers',
geopolygon=geobox.extent)
group_by = query_group_by('time')
sources = dc.group_datasets(observations, group_by)
data = dc.load_data(sources, geobox, input_type.measurements.values())
assert hashlib.md5(
data.green.data).hexdigest() == '0f64647bad54db4389fb065b2128025e'
assert hashlib.md5(
data.blue.data).hexdigest() == '41a7b50dfe5c4c1a1befbc378225beeb'
for time_slice in range(time_slices):
assert data.blue.values[time_slice][-1, -1] == -999
def group(self, datasets: VirtualDatasetBag,
**search_terms: Dict[str, Any]) -> VirtualDatasetBox:
geopolygon = datasets.geopolygon
selected = list(datasets.pile)
# geobox
merged = merge_search_terms(self, search_terms)
geobox = output_geobox(
datasets=selected,
grid_spec=datasets.product_definitions[self._product].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)
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")
def dc_load(
datasets: Sequence[Dataset],
measurements: Optional[Union[str, Sequence[str]]] = None,
geobox: Optional[GeoBox] = None,
groupby: Optional[str] = None,
resampling: Optional[Union[str, Dict[str, str]]] = None,
skip_broken_datasets: bool = False,
chunks: Optional[Dict[str, int]] = None,
progress_cbk: Optional[Callable[[int, int], Any]] = None,
fuse_func=None,
**kw,
) -> xr.Dataset:
assert len(datasets) > 0
# dask_chunks is a backward-compatibility alias for chunks
if chunks is None:
chunks = kw.pop("dask_chunks", None)
# group_by is a backward-compatibility alias for groupby
if groupby is None:
groupby = kw.pop("group_by", "time")
# bands alias for measurements
if measurements is None:
measurements = kw.pop("bands", None)
# extract all "output_geobox" inputs
geo_keys = {
k: kw.pop(k)
for k in [
"like",
"geopolygon",
"resolution",
"output_crs",
"crs",
"align",
"x",
"y",
"lat",
"lon",
] if k in kw
}
ds = datasets[0]
product = ds.type
if geobox is None:
geobox = output_geobox(
grid_spec=product.grid_spec,
load_hints=product.load_hints(),
**geo_keys,
datasets=datasets,
)
elif len(geo_keys):
warn(f"Supplied 'geobox=' parameter aliases {list(geo_keys)} inputs")
grouped = Datacube.group_datasets(datasets, groupby)
mm = product.lookup_measurements(measurements)
return Datacube.load_data(
grouped,
geobox,
mm,
resampling=resampling,
fuse_func=fuse_func,
dask_chunks=chunks,
skip_broken_datasets=skip_broken_datasets,
progress_cbk=progress_cbk,
**kw,
)
def test_load_data_cbk(tmpdir):
from datacube.api import TerminateCurrentLoad
tmpdir = Path(str(tmpdir))
spatial = dict(
resolution=(15, -15),
offset=(11230, 1381110),
)
nodata = -999
aa = mk_test_image(96, 64, 'int16', nodata=nodata)
bands = [
SimpleNamespace(name=name, values=aa, nodata=nodata)
for name in ['aa', 'bb']
]
ds, gbox = gen_tiff_dataset(bands,
tmpdir,
prefix='ds1-',
timestamp='2018-07-19',
**spatial)
assert ds.time is not None
ds2, _ = gen_tiff_dataset(bands,
tmpdir,
prefix='ds2-',
timestamp='2018-07-19',
**spatial)
assert ds.time is not None
assert ds.time == ds2.time
sources = Datacube.group_datasets([ds, ds2], 'time')
progress_call_data = []
def progress_cbk(n, nt):
progress_call_data.append((n, nt))
ds_data = Datacube.load_data(sources,
gbox,
ds.type.measurements,
progress_cbk=progress_cbk)
assert progress_call_data == [(1, 4), (2, 4), (3, 4), (4, 4)]
np.testing.assert_array_equal(aa, ds_data.aa.values[0])
np.testing.assert_array_equal(aa, ds_data.bb.values[0])
def progress_cbk_fail_early(n, nt):
progress_call_data.append((n, nt))
raise TerminateCurrentLoad()
def progress_cbk_fail_early2(n, nt):
progress_call_data.append((n, nt))
if n > 1:
raise KeyboardInterrupt()
progress_call_data = []
ds_data = Datacube.load_data(sources,
gbox,
ds.type.measurements,
progress_cbk=progress_cbk_fail_early)
assert progress_call_data == [(1, 4)]
assert ds_data.dc_partial_load is True
np.testing.assert_array_equal(aa, ds_data.aa.values[0])
np.testing.assert_array_equal(nodata, ds_data.bb.values[0])
progress_call_data = []
ds_data = Datacube.load_data(sources,
gbox,
ds.type.measurements,
progress_cbk=progress_cbk_fail_early2)
assert ds_data.dc_partial_load is True
assert progress_call_data == [(1, 4), (2, 4)]
def group(self, datasets, **search_terms):
# type: (QueryResult, Dict[str, Any]) -> DatasetPile
"""
Datasets grouped by their timestamps.
:param datasets: the `QueryResult` to fetch data from
:param query: to specify a spatial sub-region
"""
grid_spec = datasets.grid_spec
if 'product' in self:
# select only those inside the ROI
# ROI could be smaller than the query for `query`
spatial_query = reject_keys(search_terms, self._NON_SPATIAL_KEYS)
selected = list(
select_datasets_inside_polygon(
datasets.pile, query_geopolygon(**spatial_query)))
# geobox
merged = merge_search_terms(
select_keys(self, self._NON_SPATIAL_KEYS),
select_keys(spatial_query, self._NON_SPATIAL_KEYS))
geobox = output_geobox(datasets=selected,
grid_spec=grid_spec,
**select_keys(merged, self._GEOBOX_KEYS),
**spatial_query)
# group by time
group_query = query_group_by(
**select_keys(merged, self._GROUPING_KEYS))
def wrap(_, value):
return QueryResult(value, grid_spec)
# information needed for Datacube.load_data
return DatasetPile(Datacube.group_datasets(selected, group_query),
geobox).map(wrap)
elif 'transform' in self:
return self._input.group(datasets, **search_terms)
elif 'collate' in self:
self._assert(
len(datasets.pile) == len(self._children),
"invalid dataset pile")
def build(source_index, product, dataset_pile):
grouped = product.group(dataset_pile, **search_terms)
def tag(_, value):
in_position = [
value if i == source_index else None
for i, _ in enumerate(datasets.pile)
]
return QueryResult(in_position, grid_spec)
return grouped.map(tag)
groups = [
build(source_index, product, dataset_pile)
for source_index, (product, dataset_pile) in enumerate(
zip(self._children, datasets.pile))
]
return DatasetPile(
xarray.concat([grouped.pile for grouped in groups],
dim='time'),
select_unique([grouped.geobox for grouped in groups]))
elif 'juxtapose' in self:
self._assert(
len(datasets.pile) == len(self._children),
"invalid dataset pile")
groups = [
product.group(datasets, **search_terms)
for product, datasets in zip(self._children, datasets.pile)
]
aligned_piles = xarray.align(*[grouped.pile for grouped in groups])
child_groups = [
DatasetPile(aligned_piles[i], grouped.geobox)
for i, grouped in enumerate(groups)
]
def tuplify(indexes, _):
return QueryResult([
grouped.pile.sel(**indexes).item()
for grouped in child_groups
], grid_spec)
return DatasetPile(
child_groups[0].map(tuplify).pile,
select_unique([grouped.geobox for grouped in groups]))
else:
raise VirtualProductException("virtual product was not validated")
