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