def evaluate(self):
"""
Compute the temporal covariance between nodes in the time_series.
"""
cls_attr_name = self.__class__.__name__ + ".time_series"
self.time_series.trait["data"].log_debug(owner=cls_attr_name)
data_shape = self.time_series.data.shape
#(nodes, nodes, state-variables, modes)
result_shape = (data_shape[2], data_shape[2], data_shape[1],
data_shape[3])
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 = self.time_series.data[:, var, :, mode]
data = data - data.mean(axis=0)[numpy.newaxis, 0]
result[:, :, var, mode] = numpy.cov(data.T)
util.log_debug_array(LOG, result, "result")
covariance = graph.Covariance(source=self.time_series,
array_data=result,
use_storage=False)
return covariance
def test_covariance(self):
data = numpy.random.random((10, 10))
ts = time_series.TimeSeries(data=data)
dt = graph.Covariance(source=ts)
self.assertEqual(dt.shape, (0, ))
self.assertEqual(dt.array_data.shape, (0, ))
summary = dt.summary_info
self.assertEqual(summary['Graph type'], "Covariance")
self.assertEqual(summary['Shape'], (0, ))
def test_covariance(self):
data = numpy.random.random((10, 10))
ts = time_series.TimeSeries(data=data, title="test")
dt = graph.Covariance(source=ts, array_data=data)
assert dt.array_data.shape == (10, 10)
summary = dt.summary_info()
assert summary['Graph type'] == "Covariance"
assert summary['Source'] == "test"
