# 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
#
# (c) 2012-2023, Baycrest Centre for Geriatric Care ("Baycrest") and others
#
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program. If not, see <http://www.gnu.org/licenses/>.
#
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
#
#
"""
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__)
[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 = 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
```