def launch(self, time_series):
"""
Launch algorithm and build results.
:param time_series: the input time series for which the correlation should be computed
:returns: the cross correlation for the given time series
:rtype: `CrossCorrelation`
"""
##--------- Prepare a CrossCorrelation object for result ------------##
cross_corr = CrossCorrelation(source=time_series,
storage_path=self.storage_path)
node_slice = [slice(self.input_shape[0]), None, slice(self.input_shape[2]), slice(self.input_shape[3])]
##---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries(use_storage=False)
small_ts.sample_period = time_series.sample_period
partial_cross_corr = None
for var in range(self.input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_cross_corr = self.algorithm.evaluate()
cross_corr.write_data_slice(partial_cross_corr)
cross_corr.time = partial_cross_corr.time
cross_corr.labels_ordering[1] = time_series.labels_ordering[2]
cross_corr.labels_ordering[2] = time_series.labels_ordering[2]
cross_corr.close_file()
return cross_corr
def create_crosscorrelation(self, time_series):
"""
:returns: `CrossCorrelation` stored entity.
"""
operation, _, storage_path = self.__create_operation()
partial_corr = CrossCorrelation(array_data=numpy.random.random((10, 10, 10, 10, 10)), use_storage=False)
crossc = CrossCorrelation(source=time_series, storage_path=storage_path, time=range(10))
crossc.write_data_slice(partial_corr)
crossc.close_file()
adapter_instance = StoreAdapter([crossc])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return crossc
def _compute_cross_correlation(self, small_ts, input_ts_h5):
"""
Cross-correlate two one-dimensional arrays. Return a CrossCorrelation datatype with result.
"""
# (tpts, nodes, nodes, state-variables, modes)
result_shape = self._result_shape(small_ts.data.shape)
self.log.info("result shape will be: %s" % str(result_shape))
result = numpy.zeros(result_shape)
# TODO: For region level, 4s, 2000Hz, this takes ~3hours...(which makes node_coherence seem positively speedy
# Probably best to add a keyword for offsets, so we just compute +- some "small" range...
# One inter-node correlation, across offsets, for each state-var & mode.
for mode in range(result_shape[4]):
for var in range(result_shape[3]):
data = input_ts_h5.data[:, var, :, mode]
data = data - data.mean(axis=0)[numpy.newaxis, :]
# TODO: Work out a way around the 4 level loop:
for n1 in range(result_shape[1]):
for n2 in range(result_shape[2]):
result[:, n1, n2, var, mode] = correlate(data[:, n1], data[:, n2], mode="same")
self.log.debug("result")
self.log.debug(narray_describe(result))
offset = (small_ts.sample_period *
numpy.arange(-numpy.floor(result_shape[0] / 2.0), numpy.ceil(result_shape[0] / 2.0)))
cross_corr = CrossCorrelation(source=small_ts, array_data=result, time=offset)
return cross_corr
def create_crosscorrelation(self, time_series):
"""
:returns: `CrossCorrelation` stored entity.
"""
operation, _, storage_path = self.__create_operation()
partial_corr = CrossCorrelation(array_data=numpy.random.random((10, 10, 10, 10, 10)), use_storage=False)
crossc = CrossCorrelation(source=time_series, storage_path=storage_path, time=range(10))
crossc.write_data_slice(partial_corr)
crossc.close_file()
adapter_instance = StoreAdapter([crossc])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return crossc
