def launch(self, view_model):
# type: (NodeCovarianceAdapterModel) -> [CovarianceIndex]
"""
Launch algorithm and build results.
:returns: the `CovarianceIndex` built with the given time_series index as source
"""
# Create an index for the computed covariance.
covariance_index = CovarianceIndex()
covariance_h5_path = h5.path_for(self.storage_path, CovarianceH5,
covariance_index.gid)
covariance_h5 = CovarianceH5(covariance_h5_path)
# NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [
slice(self.input_shape[0]), None,
slice(self.input_shape[2]), None
]
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries()
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cov = self._compute_node_covariance(
small_ts, ts_h5)
covariance_h5.write_data_slice(partial_cov.array_data)
array_metadata = covariance_h5.array_data.get_cached_metadata()
covariance_index.fk_source_gid = self.input_time_series_index.gid
covariance_index.subtype = type(covariance_index).__name__
covariance_index.array_has_complex = array_metadata.has_complex
if not covariance_index.array_has_complex:
covariance_index.array_data_min = float(array_metadata.min)
covariance_index.array_data_max = float(array_metadata.max)
covariance_index.array_data_mean = float(array_metadata.mean)
covariance_index.array_is_finite = array_metadata.is_finite
covariance_index.shape = json.dumps(covariance_h5.array_data.shape)
covariance_index.ndim = len(covariance_h5.array_data.shape)
# TODO write this part better, by moving into the Model fill_from...
covariance_h5.gid.store(uuid.UUID(covariance_index.gid))
covariance_h5.source.store(view_model.time_series)
covariance_h5.close()
return covariance_index
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `CovarianceIndex` built with the given time_series index as source
"""
# Create an index for the computed covariance.
covariance_index = CovarianceIndex()
covariance_h5_path = h5.path_for(self.storage_path, CovarianceH5,
covariance_index.gid)
covariance_h5 = CovarianceH5(covariance_h5_path)
# NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [
slice(self.input_shape[0]), None,
slice(self.input_shape[2]), None
]
with h5.h5_file_for_index(time_series) as ts_h5:
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries()
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cov = self._compute_node_covariance(
small_ts, ts_h5)
covariance_h5.write_data_slice(partial_cov.array_data)
ts_array_metadata = covariance_h5.array_data.get_cached_metadata()
covariance_index.source_gid = time_series.gid
covariance_index.subtype = type(covariance_index).__name__
covariance_index.array_data_min = ts_array_metadata.min
covariance_index.array_data_max = ts_array_metadata.max
covariance_index.array_data_mean = ts_array_metadata.mean
covariance_index.ndim = len(covariance_h5.array_data.shape)
covariance_h5.gid.store(uuid.UUID(covariance_index.gid))
covariance_h5.source.store(uuid.UUID(time_series.gid))
covariance_h5.close()
return covariance_index