def launch(self, view_model):
# type: (CrossCorrelateAdapterModel) -> [CrossCorrelationIndex]
"""
Launch algorithm and build results.
Compute the node-pairwise cross-correlation of the source 4D TimeSeries represented by the index given as input.
Return a CrossCorrelationIndex. Create a CrossCorrelationH5 that contains the cross-correlation
sequences for all possible combinations of the nodes.
See: http://www.scipy.org/doc/api_docs/SciPy.signal.signaltools.html#correlate
:param time_series: the input time series index for which the correlation should be computed
:returns: the cross correlation index for the given time series
:rtype: `CrossCorrelationIndex`
"""
# --------- Prepare CrossCorrelationIndex and CrossCorrelationH5 objects for result ------------##
cross_corr_index = CrossCorrelationIndex()
cross_corr_h5_path = h5.path_for(self.storage_path, CrossCorrelationH5,
cross_corr_index.gid)
cross_corr_h5 = CrossCorrelationH5(cross_corr_h5_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()
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
small_ts.sample_period = ts_h5.sample_period.load()
partial_cross_corr = None
labels_ordering = ts_h5.labels_ordering.load()
for var in range(self.input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cross_corr = self._compute_cross_correlation(
small_ts, ts_h5)
cross_corr_h5.write_data_slice(partial_cross_corr)
ts_array_metadata = cross_corr_h5.array_data.get_cached_metadata()
cross_corr_h5.time.store(partial_cross_corr.time)
cross_corr_labels_ordering = list(partial_cross_corr.labels_ordering)
cross_corr_labels_ordering[1] = labels_ordering[2]
cross_corr_labels_ordering[2] = labels_ordering[2]
cross_corr_h5.labels_ordering.store(
json.dumps(tuple(cross_corr_labels_ordering)))
cross_corr_h5.source.store(uuid.UUID(self.input_time_series_index.gid))
cross_corr_h5.gid.store(uuid.UUID(cross_corr_index.gid))
cross_corr_index.source_gid = self.input_time_series_index.gid
cross_corr_index.labels_ordering = cross_corr_h5.labels_ordering.load()
cross_corr_index.type = type(cross_corr_index).__name__
cross_corr_index.array_data_min = ts_array_metadata.min
cross_corr_index.array_data_max = ts_array_metadata.max
cross_corr_index.array_data_mean = ts_array_metadata.mean
cross_corr_h5.close()
return cross_corr_index
def build():
time_series_index = time_series_index_factory()
time_series = h5.load_from_index(time_series_index)
data = numpy.random.random((10, 10, 10, 10, 10))
cross_correlation = temporal_correlations.CrossCorrelation(source=time_series, array_data=data)
op = operation_factory()
cross_correlation_index = CrossCorrelationIndex()
cross_correlation_index.fk_from_operation = op.id
cross_correlation_index.fill_from_has_traits(cross_correlation)
cross_correlation_h5_path = h5.path_for_stored_index(cross_correlation_index)
with CrossCorrelationH5(cross_correlation_h5_path) as f:
f.store(cross_correlation)
cross_correlation_index = dao.store_entity(cross_correlation_index)
return cross_correlation_index
def launch(self, view_model):
# type: (CrossCorrelateAdapterModel) -> [CrossCorrelationIndex]
"""
Launch algorithm and build results.
Compute the node-pairwise cross-correlation of the source 4D TimeSeries represented by the index given as input.
Return a CrossCorrelationIndex. Create a CrossCorrelationH5 that contains the cross-correlation
sequences for all possible combinations of the nodes.
See: http://www.scipy.org/doc/api_docs/SciPy.signal.signaltools.html#correlate
:param view_model: the ViewModel keeping the algorithm inputs
:return: the cross correlation index for the given time series
:rtype: `CrossCorrelationIndex`
"""
# --------- Prepare CrossCorrelationIndex and CrossCorrelationH5 objects for result ------------##
cross_corr_index = CrossCorrelationIndex()
cross_corr_h5_path = h5.path_for(self.storage_path, CrossCorrelationH5, cross_corr_index.gid)
cross_corr_h5 = CrossCorrelationH5(cross_corr_h5_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()
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
small_ts.sample_period = ts_h5.sample_period.load()
small_ts.sample_period_unit = ts_h5.sample_period_unit.load()
partial_cross_corr = None
for var in range(self.input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cross_corr = self._compute_cross_correlation(small_ts, ts_h5)
cross_corr_h5.write_data_slice(partial_cross_corr)
partial_cross_corr.source.gid = view_model.time_series
partial_cross_corr.gid = uuid.UUID(cross_corr_index.gid)
cross_corr_index.fill_from_has_traits(partial_cross_corr)
self.fill_index_from_h5(cross_corr_index, cross_corr_h5)
cross_corr_h5.store(partial_cross_corr, scalars_only=True)
cross_corr_h5.close()
return cross_corr_index