def test_correlation_coefficients(self):
data = numpy.random.random((13, 14))
ts = time_series.TimeSeries(data=data, title="test-ts")
dt = graph.CorrelationCoefficients(source=ts, array_data=data)
assert dt.array_data.shape == (13, 14)
summary = dt.summary_info()
assert summary['Graph type'] == "CorrelationCoefficients"
assert summary['Source'] == "test-ts"
assert summary['Dimensions'] == dt.labels_ordering
assert dt.labels_ordering == ("Node", "Node", "State Variable", "Mode")
def test_correlation_coefficients(self):
data = numpy.random.random((13, 14))
ts = time_series.TimeSeries(data=data, title="test-ts")
dt = graph.CorrelationCoefficients(source=ts, array_data=data)
self.assertEqual(dt.shape, (13, 14))
self.assertEqual(dt.array_data.shape, (13, 14))
summary = dt.summary_info
self.assertEqual(summary['Graph type'], "CorrelationCoefficients")
self.assertEqual(summary['Source'], "test-ts")
self.assertEqual(summary['Dimensions'], dt.labels_ordering)
self.assertEqual(dt.labels_ordering,
["Node", "Node", "State Variable", "Mode"])
def evaluate(self):
"""
Compute the correlation coefficients of a 2D array (tpts x nodes).
Yields an array of size nodes x nodes x state-variables x modes.
The time interval over which the correlation coefficients are computed
is defined by t_start, t_end
"""
cls_attr_name = self.__class__.__name__ + ".time_series"
self.time_series.trait["data"].log_debug(owner=cls_attr_name)
#(nodes, nodes, state-variables, modes)
input_shape = self.time_series.read_data_shape()
result_shape = self.result_shape(input_shape)
LOG.info("result shape will be: %s" % str(result_shape))
result = numpy.zeros(result_shape)
t_lo = int((1. / self.time_series.sample_period) *
(self.t_start - self.time_series.sample_period))
t_hi = int((1. / self.time_series.sample_period) *
(self.t_end - self.time_series.sample_period))
t_lo = max(t_lo, 0)
t_hi = max(t_hi, input_shape[0])
#One correlation coeff matrix, for each state-var & mode.
for mode in range(result_shape[3]):
for var in range(result_shape[2]):
current_slice = tuple([
slice(t_lo, t_hi + 1),
slice(var, var + 1),
slice(input_shape[2]),
slice(mode, mode + 1)
])
data = self.time_series.read_data_slice(
current_slice).squeeze()
result[:, :, var, mode] = numpy.corrcoef(data.T)
util.log_debug_array(LOG, result, "result")
corr_coeff = graph.CorrelationCoefficients(source=self.time_series,
array_data=result,
use_storage=False)
return corr_coeff