def build():
ts_index = time_series_factory()
ts_h5 = h5_file_for_index(ts_index)
ts = TimeSeries()
ts_h5.load_into(ts)
ts_h5.close()
data_shape = ts.data.shape
result_shape = (data_shape[2], data_shape[2], data_shape[1],
data_shape[3])
result = numpy.zeros(result_shape)
for mode in range(data_shape[3]):
for var in range(data_shape[1]):
data = ts_h5.data[:, var, :, mode]
data = data - data.mean(axis=0)[numpy.newaxis, 0]
result[:, :, var, mode] = numpy.cov(data.T)
covariance = Covariance(source=ts, array_data=result)
op = operation_factory()
covariance_db = CovarianceIndex()
covariance_db.fk_from_operation = op.id
covariance_db.fill_from_has_traits(covariance)
covariance_h5_path = h5.path_for_stored_index(covariance_db)
with TimeSeriesH5(covariance_h5_path) as f:
f.store(ts)
session.add(covariance_db)
session.commit()
return covariance_db
def build():
time_series_index = time_series_index_factory()
time_series = h5.load_from_index(time_series_index)
data = numpy.random.random((10, 10))
covariance = graph.Covariance(source=time_series, array_data=data)
op = operation_factory()
covariance_index = CovarianceIndex()
covariance_index.fk_from_operation = op.id
covariance_index.fill_from_has_traits(covariance)
covariance_h5_path = h5.path_for_stored_index(covariance_index)
with CovarianceH5(covariance_h5_path) as f:
f.store(covariance)
covariance_index = dao.store_entity(covariance_index)
return covariance_index