Source code for tvb.analyzers.metric_variance_of_node_variance

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Filler analyzer: Takes a TimeSeries object and returns a Float.

.. moduleauthor:: Bogdan Neacsa <>
.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>


from tvb.basic.logger.builder import get_logger

Zero-centres all the time-series, calculates the variance for each node 
time-series and returns the variance of the node variances. 

TimeSeries DataType
This is a crude indicator of how different the "excitability" of the model is
from node to node.

log = get_logger(__name__)

[docs]def compute_variance_of_node_variance_metric(params): """ # type: dict(TimeSeries, float, int) -> float Compute the zero centered variance of node variances for the time_series. Parameters ---------- params : a dictionary containing time_series : TimeSeries Input time series for which the metric will be computed. start_point : float Determines how many points of the TimeSeries will be discarded before computing the metric segment : int Divides the input time-series into discrete equally sized sequences and use the last segment to compute the metric. Only used when the start point is larger than the time-series length """ time_series = params['time_series'] start_point = params['start_point'] segment = params['segment'] shape = tpts = shape[0] if start_point != 0.0: start_tpt = start_point / time_series.sample_period log.debug("Will discard: %s time points" % start_tpt) else: start_tpt = 0 if start_tpt > tpts: log.warning("The time-series is shorter than the starting point") log.debug("Will divide the time-series into %d segments." % segment) # Lazy strategy start_tpt = int((segment - 1) * (tpts // segment)) start_tpt = int(start_tpt) zero_mean_data = ([start_tpt:, :] -[start_tpt:, :].mean(axis=0)) # reshape by concatenating the time-series of each var and modes for each node. zero_mean_data = zero_mean_data.transpose((0, 1, 3, 2)) cat_tpts = zero_mean_data.shape[0] * shape[1] * shape[3] zero_mean_data = zero_mean_data.reshape((cat_tpts, shape[2]), order="F") # Variance over time-points, state-variables, and modes for each node. node_variance = zero_mean_data.var(axis=0) # Variance of that variance over nodes result = node_variance.var() return result