Source code for tvb.datatypes.mode_decompositions

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"""
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 }