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 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 _compute_node_covariance(self, small_ts, input_ts_h5):
"""
Compute the temporal covariance between nodes in a TimeSeries dataType.
A nodes x nodes matrix is returned for each (state-variable, mode).
"""
data_shape = small_ts.data.shape
# (nodes, nodes, state-variables, modes)
result_shape = (data_shape[2], data_shape[2], data_shape[1],
data_shape[3])
self.log.info("result shape will be: %s" % str(result_shape))
result = numpy.zeros(result_shape)
# One inter-node temporal covariance matrix for each state-var & mode.
for mode in range(data_shape[3]):
for var in range(data_shape[1]):
data = input_ts_h5.data[:, var, :, mode]
data = data - data.mean(axis=0)[numpy.newaxis, 0]
result[:, :, var, mode] = numpy.cov(data.T)
self.log.debug("result")
self.log.debug(narray_describe(result))
covariance = Covariance(source=small_ts, array_data=result)
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
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
