# -*- 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
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# 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
#
#
"""
The Mode Decomposition datatypes.
.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
.. moduleauthor:: Paula Sanz Leon <paula@tvb.invalid>
"""
import numpy
import tvb.datatypes.time_series as time_series
from tvb.basic.neotraits.api import HasTraits, Attr, NArray, Int
[docs]class PrincipalComponents(HasTraits):
"""
Result of a Principal Component Analysis (PCA).
"""
source = Attr(
field_type=time_series.TimeSeries,
label="Source time-series",
doc="Links to the time-series on which the PCA is applied.")
weights = NArray(
label="Principal vectors",
doc="""The vectors of the 'weights' with which each time-series is represented in each component.""")
fractions = NArray(
label="Fraction explained",
doc="""A vector or collection of vectors representing the fraction of
the variance explained by each principal component.""")
norm_source = NArray(label="Normalised source time series")
component_time_series = NArray(label="Component time series")
normalised_component_time_series = NArray(label="Normalised component time series")
[docs] def summary_info(self):
"""
Gather scientifically interesting summary information from an instance
of this datatype.
"""
summary = {
"Mode decomposition type": self.__class__.__name__,
"Source": self.source.title
}
return summary
[docs] def compute_norm_source(self):
"""Normalised source time-series."""
self.norm_source = ((self.source.data - self.source.data.mean(axis=0)) /
self.source.data.std(axis=0))
# self.trait["norm_source"].log_debug(owner=self.__class__.__name__)
# TODO: ??? Any value in making this a TimeSeries datatypes ???
[docs] def compute_component_time_series(self):
"""Compnent time-series."""
# TODO: Generalise -- it currently assumes 4D TimeSeriesSimulator...
ts_shape = self.source.data.shape
component_ts = numpy.zeros(ts_shape)
for var in range(ts_shape[1]):
for mode in range(ts_shape[3]):
w = self.weights[:, :, var, mode]
ts = self.source.data[:, var, :, mode]
component_ts[:, var, :, mode] = numpy.dot(w, ts.T).T
self.component_time_series = component_ts
# self.trait["component_time_series"].log_debug(owner=self.__class__.__name__)
# TODO: ??? Any value in making this a TimeSeries datatypes ???
[docs] def compute_normalised_component_time_series(self):
"""normalised_Compnent time-series."""
# TODO: Generalise -- it currently assumes 4D TimeSeriesSimulator...
ts_shape = self.source.data.shape
component_ts = numpy.zeros(ts_shape)
for var in range(ts_shape[1]):
for mode in range(ts_shape[3]):
w = self.weights[:, :, var, mode]
nts = self.norm_source[:, var, :, mode]
component_ts[:, var, :, mode] = numpy.dot(w, nts.T).T
self.normalised_component_time_series = component_ts
# self.trait["normalised_component_time_series"].log_debug(owner=self.__class__.__name__)
[docs]class IndependentComponents(HasTraits):
"""
Result of an Independent Component Analysis.
"""
source = Attr(
field_type=time_series.TimeSeries,
label="Source time-series",
doc="Links to the time-series on which the ICA is applied.")
mixing_matrix = NArray(
label="Mixing matrix - Spatial Maps",
required=False,
doc="""The linear mixing matrix (Mixing matrix) """)
unmixing_matrix = NArray(
label="Unmixing matrix - Spatial maps",
doc="""The estimated unmixing matrix used to obtain the unmixed sources from the data""")
prewhitening_matrix = NArray(
label="Pre-whitening matrix",
doc=""" """)
n_components = Int(
label="Number of independent components",
doc=""" Observed data matrix is considered to be a linear combination
of :math:`n` non-Gaussian independent components""")
norm_source = NArray(label="Normalised source time series. Zero centered and whitened.")
component_time_series = NArray(label="Component time series. Unmixed sources.")
normalised_component_time_series = NArray(label="Normalised component time series")
[docs] def compute_norm_source(self):
"""Normalised source time-series."""
self.norm_source = ((self.source.data - self.source.data.mean(axis=0)) /
self.source.data.std(axis=0))
[docs] def compute_component_time_series(self):
ts_shape = self.source.data.shape
component_ts_shape = (ts_shape[0], ts_shape[1], self.n_components, ts_shape[3])
component_ts = numpy.zeros(component_ts_shape)
for var in range(ts_shape[1]):
for mode in range(ts_shape[3]):
w = self.unmixing_matrix[:, :, var, mode]
k = self.prewhitening_matrix[:, :, var, mode]
ts = self.source.data[:, var, :, mode]
component_ts[:, var, :, mode] = numpy.dot(w, numpy.dot(k, ts.T)).T
self.component_time_series = component_ts
[docs] def compute_normalised_component_time_series(self):
ts_shape = self.source.data.shape
component_ts_shape = (ts_shape[0], ts_shape[1], self.n_components, ts_shape[3])
component_nts = numpy.zeros(component_ts_shape)
for var in range(ts_shape[1]):
for mode in range(ts_shape[3]):
w = self.unmixing_matrix[:, :, var, mode]
k = self.prewhitening_matrix[:, :, var, mode]
nts = self.norm_source[:, var, :, mode]
component_nts[:, var, :, mode] = numpy.dot(w, numpy.dot(k, nts.T)).T
self.normalised_component_time_series = component_nts
[docs] def compute_mixing_matrix(self):
"""
Compute the linear mixing matrix A, so X = A * S ,
where X is the observed data and S contain the independent components
"""
ts_shape = self.source.data.shape
mixing_matrix_shape = (ts_shape[2], self.n_components, ts_shape[1], ts_shape[3])
mixing_matrix = numpy.zeros(mixing_matrix_shape)
for var in range(ts_shape[1]):
for mode in range(ts_shape[3]):
w = self.unmixing_matrix[:, :, var, mode]
k = self.prewhitening_matrix[:, :, var, mode]
temp = numpy.matrix(numpy.dot(w, k))
mixing_matrix[:, :, var, mode] = numpy.array(numpy.dot(temp.T, (numpy.dot(temp, temp.T)).T))
self.mixing_matrix = mixing_matrix
[docs] def summary_info(self):
"""
Gather scientifically interesting summary information from an instance
of this datatype.
"""
return {
"Mode decomposition type": self.__class__.__name__,
"Source": self.source.title
}
