def build():
data = numpy.random.random((10, 10, 10, 10))
time_series_index = time_series_index_factory(data=data)
time_series = h5.load_from_index(time_series_index)
n_comp = 5
ica = mode_decompositions.IndependentComponents(source=time_series,
component_time_series=numpy.random.random((10, n_comp, 10, 10)),
prewhitening_matrix=numpy.random.random((n_comp, 10, 10, 10)),
unmixing_matrix=numpy.random.random((n_comp, n_comp, 10, 10)),
n_components=n_comp)
ica.compute_norm_source()
ica.compute_normalised_component_time_series()
op = operation_factory()
ica_index = IndependentComponentsIndex()
ica_index.fk_from_operation = op.id
ica_index.fill_from_has_traits(ica)
independent_components_h5_path = h5.path_for_stored_index(ica_index)
with IndependentComponentsH5(independent_components_h5_path) as f:
f.store(ica)
ica_index = dao.store_entity(ica_index)
return ica_index
def launch(self, time_series, n_components=None):
"""
Launch algorithm and build results.
"""
# --------- Prepare a IndependentComponents object for result ----------##
ica_index = IndependentComponentsIndex()
ica_index.source_gid = time_series.gid
time_series_h5 = h5.h5_file_for_index(time_series)
result_path = h5.path_for(self.storage_path, IndependentComponentsH5, ica_index.gid)
ica_h5 = IndependentComponentsH5(path=result_path)
ica_h5.gid.store(uuid.UUID(ica_index.gid))
ica_h5.source.store(time_series_h5.gid.load())
ica_h5.n_components.store(self.algorithm.n_components)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
input_shape = time_series_h5.data.shape
node_slice = [slice(input_shape[0]), None, slice(input_shape[2]), slice(input_shape[3])]
# ---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries()
for var in range(input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_ica = self.algorithm.evaluate()
ica_h5.write_data_slice(partial_ica)
ica_h5.close()
time_series_h5.close()
return ica_index
def launch(self, view_model):
# type: (ICAAdapterModel) -> [IndependentComponentsIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the ica index for the specified time series
"""
# --------------------- Prepare result entities ---------------------##
ica_index = IndependentComponentsIndex()
result_path = h5.path_for(self.storage_path, IndependentComponentsH5,
ica_index.gid)
ica_h5 = IndependentComponentsH5(path=result_path)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
input_shape = time_series_h5.data.shape
node_slice = [
slice(input_shape[0]), None,
slice(input_shape[2]),
slice(input_shape[3])
]
# ---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries()
for var in range(input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
partial_ica = compute_ica_decomposition(small_ts,
view_model.n_components)
ica_h5.write_data_slice(partial_ica)
time_series_h5.close()
partial_ica.source.gid = view_model.time_series
partial_ica.gid = uuid.UUID(ica_index.gid)
ica_h5.store(partial_ica, scalars_only=True)
ica_h5.close()
ica_index.fill_from_has_traits(partial_ica)
return ica_index
def launch(self, view_model):
# type: (ICAAdapterModel) -> [IndependentComponentsIndex]
"""
Launch algorithm and build results.
"""
# --------- Prepare a IndependentComponents object for result ----------##
ica_index = IndependentComponentsIndex()
ica_index.fk_source_gid = view_model.time_series.hex
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
result_path = h5.path_for(self.storage_path, IndependentComponentsH5,
ica_index.gid)
ica_h5 = IndependentComponentsH5(path=result_path)
ica_h5.gid.store(uuid.UUID(ica_index.gid))
ica_h5.source.store(view_model.time_series)
ica_h5.n_components.store(self.algorithm.n_components)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
input_shape = time_series_h5.data.shape
node_slice = [
slice(input_shape[0]), None,
slice(input_shape[2]),
slice(input_shape[3])
]
# ---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries()
for var in range(input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_ica = self.algorithm.evaluate()
ica_h5.write_data_slice(partial_ica)
array_metadata = ica_h5.unmixing_matrix.get_cached_metadata()
ica_index.array_has_complex = array_metadata.has_complex
ica_index.shape = json.dumps(ica_h5.unmixing_matrix.shape)
ica_index.ndim = len(ica_h5.unmixing_matrix.shape)
ica_h5.close()
time_series_h5.close()
return ica_index