def test_crosscorrelation(self):
data = numpy.random.random((10, 10))
ts = time_series.TimeSeries(data=data)
dt = temporal_correlations.CrossCorrelation(source=ts)
summary_info = dt.summary_info
self.assertEqual(summary_info['Dimensions'], ['Offsets', 'Node', 'Node', 'State Variable', 'Mode'])
self.assertEqual(summary_info['Source'], '')
self.assertEqual(summary_info['Temporal correlation type'], 'CrossCorrelation')
self.assertEqual(dt.labels_ordering, ['Offsets', 'Node', 'Node', 'State Variable', 'Mode'])
self.assertTrue(dt.source is not None)
self.assertEqual(dt.time.shape, (0,))
def test_crosscorrelation(self):
data = numpy.random.random((10, 10))
ts = time_series.TimeSeries(data=data)
dt = temporal_correlations.CrossCorrelation(source=ts)
summary_info = dt.summary_info
assert summary_info['Dimensions'] == ['Offsets', 'Node', 'Node', 'State Variable', 'Mode']
assert summary_info['Source'] == ''
assert summary_info['Temporal correlation type'] == 'CrossCorrelation'
assert dt.labels_ordering == ['Offsets', 'Node', 'Node', 'State Variable', 'Mode']
assert dt.source is not None
assert dt.time.shape == (0,)
def test_crosscorrelation(self):
data = numpy.random.random((10, 10))
ts = time_series.TimeSeries(data=data, title='meh')
dt = temporal_correlations.CrossCorrelation(source=ts, array_data=numpy.array([0]))
summary_info = dt.summary_info()
assert summary_info['Dimensions'] == ('Offsets', 'Node', 'Node', 'State Variable', 'Mode')
assert summary_info['Source'] == 'meh'
assert summary_info['Temporal correlation type'] == 'CrossCorrelation'
assert dt.labels_ordering == ('Offsets', 'Node', 'Node', 'State Variable', 'Mode')
assert dt.source is not None
assert dt.time is None
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 evaluate(self):
"""
Cross-correlate two one-dimensional arrays.
"""
cls_attr_name = self.__class__.__name__ + ".time_series"
self.time_series.trait["data"].log_debug(owner=cls_attr_name)
#(tpts, nodes, nodes, state-variables, modes)
result_shape = self.result_shape(self.time_series.data.shape)
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 = self.time_series.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")
util.log_debug_array(LOG, result, "result")
offset = (self.time_series.sample_period *
numpy.arange(-numpy.floor(result_shape[0] / 2.0),
numpy.ceil(result_shape[0] / 2.0)))
cross_corr = temporal_correlations.CrossCorrelation(
source=self.time_series,
array_data=result,
time=offset,
use_storage=False)
return cross_corr
