def test_reduce_node_process_graph():
reduce_pg = PGNode("array_element",
data={"from_argument": "data"},
index=3)
a = PGNode("load_collection", collection_id="S2")
graph = ReduceNode(a, reducer=reduce_pg, dimension="time")
assert graph.to_dict() == {
'process_id': 'reduce_dimension',
'arguments': {
'data': {
'from_node': {
'process_id': 'load_collection',
'arguments': {
'collection_id': 'S2'
},
}
},
'reducer': {
"process_graph": {
"process_id": "array_element",
"arguments": {
"data": {
"from_argument": "data"
},
"index": 3
}
}
},
'dimension': 'time',
},
}
def test_pgnode_to_dict_subprocess_graphs():
load_collection = PGNode("load_collection", collection_id="S2")
band2 = PGNode("array_element", data={"from_argument": "data"}, index=2)
band2_plus3 = PGNode("add", x={"from_node": band2}, y=2)
graph = ReduceNode(data=load_collection, reducer=band2_plus3, dimension='bands')
assert graph.to_dict() == {
'process_id': 'reduce_dimension',
'arguments': {
'data': {'from_node': {
'process_id': 'load_collection',
'arguments': {'collection_id': 'S2'},
}},
'dimension': 'bands',
'reducer': {'process_graph': {
'process_id': 'add',
'arguments': {
'x': {"from_node": {
'process_id': 'array_element',
'arguments': {'data': {'from_argument': 'data'}, 'index': 2},
}},
'y': 2
},
}}
},
}
assert graph.flat_graph() == {
"loadcollection1": {
'process_id': 'load_collection',
'arguments': {'collection_id': 'S2'},
},
"reducedimension1": {
'process_id': 'reduce_dimension',
'arguments': {
'data': {'from_node': "loadcollection1"},
'dimension': 'bands',
'reducer': {'process_graph': {
"arrayelement1": {
'process_id': 'array_element',
'arguments': {'data': {'from_argument': 'data'}, 'index': 2},
},
"add1": {
'process_id': 'add',
'arguments': {
'x': {"from_node": "arrayelement1"},
'y': 2
},
'result': True,
}}
}
},
"result": True,
}
}
def test_reduce_node():
a = PGNode("load_collection", collection_id="S2")
graph = ReduceNode(a, reducer="mean", dimension="time")
assert graph.to_dict() == {
'process_id': 'reduce_dimension',
'arguments': {
'data': {'from_node': {
'process_id': 'load_collection',
'arguments': {'collection_id': 'S2'},
}},
'reducer': 'mean',
'dimension': 'time',
},
}
def reduce_dimension(self, dimension: str, reducer: Union[PGNode, str],
process_id="reduce_dimension", band_math_mode: bool = False) -> 'DataCube':
"""
Add a reduce process with given reducer callback along given dimension
"""
# TODO: check if dimension is valid according to metadata? #116
# TODO: #125 use/test case for `reduce_dimension_binary`?
if isinstance(reducer, str):
# Assume given reducer is a simple predefined reduce process_id
reducer = PGNode(process_id=reducer, arguments={"data": {"from_parameter": "data"}})
return self.process_with_node(ReduceNode(
process_id=process_id,
data=self._pg,
reducer=reducer,
dimension=self.metadata.assert_valid_dimension(dimension),
# TODO #123 is it (still) necessary to make "band" math a special case?
band_math_mode=band_math_mode
# TODO: add `context` argument #125
), metadata=self.metadata.reduce_dimension(dimension_name=dimension))