Source code for tvb.analyzers.metric_variance_of_node_variance

# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
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# (c) 2012-2023, Baycrest Centre for Geriatric Care ("Baycrest") and others
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"""
Filler analyzer: Takes a TimeSeries object and returns a Float.

.. moduleauthor:: Bogdan Neacsa <bogdan.neacsa@codemart.ro>
.. 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. 

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

log = get_logger(__name__)


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 = time_series.data.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 = (time_series.data[start_tpt:, :] - time_series.data[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