def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `Covariance` built with the given timeseries as source
"""
#Create a FourierSpectrum dataType object.
covariance = Covariance(source=time_series,
storage_path=self.storage_path)
#NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [
slice(self.input_shape[0]), None,
slice(self.input_shape[2]), None
]
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries(use_storage=False)
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_cov = self.algorithm.evaluate()
covariance.write_data_slice(partial_cov.array_data)
covariance.close_file()
return covariance
def create_covariance(self, time_series):
"""
:returns: a stored DataType Covariance.
"""
operation, _, storage_path = self.__create_operation()
covariance = Covariance(storage_path=storage_path, source=time_series)
covariance.write_data_slice(numpy.random.random((10, 10, 10)))
adapter_instance = StoreAdapter([covariance])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return covariance
def create_covariance(self, time_series):
"""
:returns: a stored DataType Covariance.
"""
operation, _, storage_path = self.__create_operation()
covariance = Covariance(storage_path=storage_path, source=time_series)
covariance.write_data_slice(numpy.random.random((10, 10, 10)))
adapter_instance = StoreAdapter([covariance])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return covariance
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `Covariance` built with the given timeseries as source
"""
#Create a FourierSpectrum dataType object.
covariance = Covariance(source=time_series, storage_path=self.storage_path)
#NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [slice(self.input_shape[0]), None, slice(self.input_shape[2]), None]
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries(use_storage=False)
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_cov = self.algorithm.evaluate()
covariance.write_data_slice(partial_cov.array_data)
covariance.close_file()
return covariance
