def test_band_dimension_set_labels():
bdim = BandDimension(name="bs", bands=[Band('some_name',None,None)])
metadata = CollectionMetadata({},dimensions=[bdim])
newdim = metadata.rename_labels("bs",target=['1','2','3']).band_dimension
assert metadata.band_dimension.band_names == ['some_name']
assert newdim.band_names == ['1','2','3']
def test_band_dimension_rename_labels():
b02 = Band("B02", "blue", 0.490)
b03 = Band("B03", "green", 0.560)
b04 = Band("B04", "red", 0.665)
bdim = BandDimension(name="bs", bands=[b02, b03, b04])
metadata = CollectionMetadata({},dimensions=[bdim])
newdim = metadata.rename_labels("bs",target=['1','2','3']).band_dimension
assert metadata.band_dimension.band_names == ['B02','B03','B04']
assert newdim.band_names == ['1','2','3']
def load_collection(self, collection_id: str, load_params: LoadParameters,
env: EvalEnv) -> DummyDataCube:
_register_load_collection_call(collection_id, load_params)
if collection_id in _collections:
return _collections[collection_id]
metadata = CollectionMetadata(
metadata=self.get_collection_metadata(collection_id))
if load_params.bands:
metadata = metadata.filter_bands(load_params.bands)
image_collection = DummyDataCube(metadata=metadata)
_collections[collection_id] = image_collection
return image_collection
def test_get_dimensions_cube_dimensions_eo_bands():
dims = CollectionMetadata._parse_dimensions({
"cube:dimensions": {
"x": {"type": "spatial", "extent": [-10, 10]},
"y": {"type": "spatial", "extent": [-56, 83], "reference_system": 123},
"t": {"type": "temporal", "extent": ["2020-02-20", None]},
"spectral": {"type": "bands", "values": ["r", "g", "b"]},
},
"summaries": {
"eo:bands": [
{"name": "r", "common_name": "red", "center_wavelength": 5},
{"name": "g", "center_wavelength": 8},
{"name": "b", "common_name": "blue"},
]
}
})
assert_same_dimensions(dims, [
SpatialDimension(name="x", extent=[-10, 10]),
SpatialDimension(name="y", extent=[-56, 83], crs=123),
TemporalDimension(name="t", extent=["2020-02-20", None]),
BandDimension(name="spectral", bands=[
Band("r", "red", 5),
Band("g", None, 8),
Band("b", "blue", None),
])
])
def load_collection(self,
collection_id: str,
arguments: dict,
metadata: dict = None) -> 'DryRunDataCube':
"""Create a DryRunDataCube from a `load_collection` process."""
# TODO: avoid VITO/Terrascope specific handling here?
properties = {
**CollectionMetadata(metadata).get("_vito",
"properties",
default={}),
**arguments.get("properties", {})
}
trace = DataSource.load_collection(collection_id=collection_id,
properties=properties)
self.add_trace(trace)
cube = DryRunDataCube(traces=[trace],
data_tracer=self,
metadata=metadata)
if "temporal_extent" in arguments:
cube = cube.filter_temporal(*arguments["temporal_extent"])
if "spatial_extent" in arguments:
cube = cube.filter_bbox(**arguments["spatial_extent"])
if "bands" in arguments:
cube = cube.filter_bands(arguments["bands"])
if properties:
cube = cube.filter_properties(properties)
return cube
def __init__(self,
node_id: str,
builder: GraphBuilder,
session: 'Connection',
metadata: CollectionMetadata = None):
self.node_id = node_id
self.builder = builder
self.session = session
self.graph = builder.processes
self.metadata = CollectionMetadata.get_or_create(metadata)
def test_get_dimensions_cube_dimensions_no_band_names():
logs = []
dims = CollectionMetadata._parse_dimensions({
"cube:dimensions": {
"spectral": {"type": "bands"},
},
}, complain=logs.append)
assert_same_dimensions(dims, [
BandDimension(name="spectral", bands=[])
])
assert logs == ["No band names in dimension 'spectral'"]
def test_get_dimensions_cube_dimensions_non_standard_type():
logs = []
dims = CollectionMetadata._parse_dimensions({
"cube:dimensions": {
"bar": {"type": "foo"},
},
}, complain=logs.append)
assert_same_dimensions(dims, [
Dimension(type="foo", name="bar")
])
assert logs == ["Unknown dimension type 'foo'"]
def test_get_dimensions_cube_dimensions_spatial_xyt():
dims = CollectionMetadata._parse_dimensions({
"cube:dimensions": {
"xx": {"type": "spatial", "extent": [-10, 10]},
"yy": {"type": "spatial", "extent": [-56, 83], "reference_system": 123},
"tt": {"type": "temporal", "extent": ["2020-02-20", None]},
}
})
assert_same_dimensions(dims, [
SpatialDimension(name="xx", extent=[-10, 10]),
SpatialDimension(name="yy", extent=[-56, 83], crs=123),
TemporalDimension(name="tt", extent=["2020-02-20", None]),
])
def test_aggregate_polygon_result_basic(tmp_path):
timeseries = {
"2019-11-11T01:11:11Z": [[7, 8, 9], [10, 11, 12]],
"2019-10-15T08:15:45Z": [[1, 2, 3], [4, 5, 6]],
}
regions = GeometryCollection(
[Polygon([(0, 0), (5, 1), (1, 4)]),
Polygon([(6, 1), (1, 7), (9, 9)])])
metadata = CollectionMetadata({
"cube:dimensions": {
"x": {
"type": "spatial"
},
"b": {
"type": "bands",
"values": ["red", "green", "blue"]
}
}
})
result = AggregatePolygonResult(timeseries,
regions=regions,
metadata=metadata)
result.set_format("netcdf")
assets = result.write_assets(tmp_path)
theAsset = assets.popitem()[1]
filename = theAsset['href']
assert 'application/x-netcdf' == theAsset['type']
assert ["red", "green", "blue"] == [b['name'] for b in theAsset['bands']]
timeseries_ds = xr.open_dataset(filename)
print(timeseries_ds)
assert_array_equal(
timeseries_ds.band_0.coords['t'].data,
np.asarray([
np.datetime64('2019-10-15T08:15:45'),
np.datetime64('2019-11-11T01:11:11')
]))
timeseries_ds.band_0.sel(feature=1)
timeseries_ds.band_0.sel(t='2019-10-16')
print(timeseries_ds)
assert_array_equal(
4,
timeseries_ds.band_0.sel(feature=1).sel(t="2019-10-15T08:15:45Z").data)
def test_metadata_bands_dimension_no_band_dimensions():
metadata = CollectionMetadata({
"cube:dimensions": {
"x": {"type": "spatial", "axis": "x"},
}
})
with pytest.raises(MetadataException, match="No band dimension"):
metadata.band_dimension
with pytest.raises(MetadataException, match="No band dimension"):
metadata.bands
with pytest.raises(MetadataException, match="No band dimension"):
metadata.band_common_names
with pytest.raises(MetadataException, match="No band dimension"):
metadata.get_band_index("red")
with pytest.raises(MetadataException, match="No band dimension"):
metadata.filter_bands(["red"])
def collection_metadata(self, name) -> CollectionMetadata:
return CollectionMetadata(metadata=self.describe_collection(name))
