Source code for tvb.analyzers.metric_kuramoto_index

# -*- 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:: Paula Sanz Leon <paula@tvb.invalid>

"""

import cmath
import numpy
from tvb.basic.logger.builder import get_logger


"""
Return the Kuramoto synchronization index. 
    
Useful metric for a parameter analysis when the collective brain dynamics
represent coupled oscillatory processes.
    
The *order* parameters are :math:`r` and :math:`Psi`.
    
.. math::
    r e^{i * \\psi} = \\frac{1}{N}\\,\\sum_{k=1}^N(e^{i*\\theta_k})
    
The first is the phase coherence of the population of oscillators (KSI) 
and the second is the average phase.
    
When :math:`r=0` means 0 coherence among oscillators.
    
    
Input:
TimeSeries DataType
    
Output: 
Float
    
This is a crude indicator of synchronization among nodes over the entire network.

#NOTE: For the time being it is meant to be another global metric.
However, it should be consider to have a sort of TimeSeriesDatatype for this
analyzer.
    
"""


log = get_logger(__name__)




def compute_kuramoto_index_metric(params):
    """
    # type: dict(TimeSeries)  -> float
    Kuramoto Synchronization Index

    Parameters
    ----------
    params : a dictionary containing
        time_series : TimeSeries
        Input time series for which the metric will be computed.
    """

    time_series = params['time_series']
    if time_series.data.shape[1] < 2:
        msg = " The number of state variables should be at least 2."
        log.error(msg)
        raise Exception(msg)
                
    # TODO: Should be computed for each possible combination of var, mode
    #      for var, mode in itertools.product(range(self.time_series.data.shape[1]),
    #                                         range(self.time_series.data.shape[3])):
        
    # TODO: Generalise. The Kuramoto order parameter is computed over sliding
    #      time windows and then normalised

    theta_sum = numpy.sum(numpy.exp(0.0 + 1j * (numpy.vectorize(cmath.polar)
                (numpy.vectorize(complex)(time_series.data[:, 0, :, 0],
                    time_series.data[:, 1, :, 0]))[1])), axis=1)
                     
    result = numpy.vectorize(cmath.polar)(theta_sum / time_series.data.shape[2])

    return result[0].mean()