# -*- 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
#
#
"""
The Spectral datatypes.
.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
"""
import numpy
from tvb.basic.neotraits.api import HasTraits, Attr, NArray, Int, Float, EnumAttr, TVBEnum
from tvb.datatypes import time_series
[docs]class WindowingFunctionsEnum(TVBEnum):
HAMMING = "hamming"
BARTLETT = "bartlett"
BLACKMAN = "blackman"
HANNING = "hanning"
[docs]class FourierSpectrum(HasTraits):
"""
Result of a Fourier Analysis.
"""
# Overwrite attribute from superclass
array_data = NArray(dtype=numpy.complex128)
source = Attr(
field_type=time_series.TimeSeries,
label="Source time-series",
doc="Links to the time-series on which the FFT is applied.")
segment_length = Float(
label="Segment length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
windowing_function = EnumAttr(
default=WindowingFunctionsEnum.HAMMING,
required=False,
label="Windowing function",
doc="""The windowing function applied to each time segment prior to
application of the FFT.""")
amplitude = NArray(label="Amplitude")
phase = NArray(label="Phase")
power = NArray(label="Power")
average_power = NArray(label="Average Power")
normalised_average_power = NArray(label="Normalised Power", required=False)
_frequency = None
_freq_step = None
_max_freq = None
[docs] def summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
return {
"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Segment length": self.segment_length,
"Windowing function": self.windowing_function,
"Frequency step": self.freq_step,
"Maximum frequency": self.max_freq
}
@property
def freq_step(self):
""" Frequency step size of the complex Fourier spectrum."""
if self._freq_step is None:
self._freq_step = 1.0 / self.segment_length
msg = "%s: Frequency step size is %s"
self.log.debug(msg % (str(self), str(self._freq_step)))
return self._freq_step
@property
def max_freq(self):
""" Amplitude of the complex Fourier spectrum."""
if self._max_freq is None:
self._max_freq = 0.5 / self.source.sample_period
msg = "%s: Max frequency is %s"
self.log.debug(msg % (str(self), str(self._max_freq)))
return self._max_freq
@property
def frequency(self):
""" Frequencies represented the complex Fourier spectrum."""
if self._frequency is None:
self._frequency = numpy.arange(self.freq_step,
self.max_freq + self.freq_step,
self.freq_step)
return self._frequency
[docs] def compute_amplitude(self):
""" Amplitude of the complex Fourier spectrum."""
self.amplitude = numpy.abs(self.array_data)
[docs] def compute_phase(self):
""" Phase of the Fourier spectrum."""
self.phase = numpy.angle(self.array_data)
[docs] def compute_power(self):
""" Power of the complex Fourier spectrum."""
self.power = numpy.abs(self.array_data) ** 2
[docs] def compute_average_power(self):
""" Average-power of the complex Fourier spectrum."""
self.average_power = numpy.mean(numpy.abs(self.array_data) ** 2, axis=-1)
[docs] def compute_normalised_average_power(self):
""" Normalised-average-power of the complex Fourier spectrum."""
self.normalised_average_power = (self.average_power /
numpy.sum(self.average_power, axis=0))
[docs]class WaveletCoefficients(HasTraits):
"""
This class bundles all the elements of a Wavelet Analysis into a single
object, including the input TimeSeries datatype and the output results as
arrays (FloatArray)
"""
# Overwrite attribute from superclass
array_data = NArray(dtype=numpy.complex128)
source = Attr(field_type=time_series.TimeSeries, label="Source time-series")
mother = Attr(
field_type=str,
label="Mother wavelet",
default="morlet",
doc="""A string specifying the type of mother wavelet to use,
default is 'morlet'.""") # default to 'morlet'
sample_period = Float(label="Sample period")
# sample_rate = basic.Integer(label = "") inversely related
frequencies = NArray(
label="Frequencies",
doc="A vector that maps scales to frequencies.")
normalisation = Attr(field_type=str, label="Normalisation type")
# 'unit energy' | 'gabor'
q_ratio = Float(label="Q-ratio", default=5.0)
amplitude = NArray(label="Amplitude")
phase = NArray(label="Phase")
power = NArray(label="Power")
_frequency = None
_time = None
[docs] def summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
return {
"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Wavelet type": self.mother,
"Normalisation": self.normalisation,
"Q-ratio": self.q_ratio,
"Sample period": self.sample_period,
"Number of scales": self.frequencies.shape[0],
"Minimum frequency": self.frequencies[0],
"Maximum frequency": self.frequencies[-1]
}
@property
def frequency(self):
""" Frequencies represented by the wavelet spectrogram."""
if self._frequency is None:
self._frequency = numpy.arange(self.frequencies.lo,
self.frequencies.hi,
self.frequencies.step)
return self._frequency
[docs] def compute_amplitude(self):
""" Amplitude of the complex Wavelet coefficients."""
self.amplitude = numpy.abs(self.array_data)
[docs] def compute_phase(self):
""" Phase of the Wavelet coefficients."""
self.phase = numpy.angle(self.array_data)
[docs] def compute_power(self):
""" Power of the complex Wavelet coefficients."""
self.power = numpy.abs(self.array_data) ** 2
[docs]class CoherenceSpectrum(HasTraits):
"""
Result of a NodeCoherence Analysis.
"""
# Overwrite attribute from superclass
array_data = NArray()
source = Attr(
field_type=time_series.TimeSeries,
label="Source time-series",
doc="""Links to the time-series on which the node_coherence is
applied.""")
nfft = Int(
label="Data-points per block",
default=256,
doc="""NOTE: must be a power of 2""")
frequency = NArray(label="Frequency")
[docs] def summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
return {
"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Number of frequencies": self.frequency.shape[0],
"Minimum frequency": self.frequency[0],
"Maximum frequency": self.frequency[-1],
"FFT length (time-points)": self.nfft
}
[docs]class ComplexCoherenceSpectrum(HasTraits):
"""
Result of a NodeComplexCoherence Analysis.
"""
cross_spectrum = NArray(
dtype=numpy.complex128,
label="The cross spectrum",
doc=""" A complex ndarray that contains the nodes x nodes cross
spectrum for every frequency frequency and for every segment.""")
array_data = NArray(
dtype=numpy.complex128,
label="Complex Coherence",
doc="""The complex coherence coefficients calculated from the cross
spectrum. The imaginary values of this complex ndarray represent the
imaginary coherence.""")
source = Attr(
field_type=time_series.TimeSeries,
label="Source time-series",
doc="""Links to the time-series on which the node_coherence is
applied.""")
epoch_length = Float(
label="Epoch length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
segment_length = Float(
label="Segment length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
windowing_function = Attr(
field_type=str,
label="Windowing function",
doc="""The windowing function applied to each time segment prior to
application of the FFT.""")
_frequency = None
_freq_step = None
_max_freq = None
spectrum_types = ["Imaginary", "Real", "Absolute"]
[docs] def summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
return {
"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Frequency step": self.freq_step,
"Maximum frequency": self.max_freq,
"Epoch length": self.epoch_length,
"Segment length": self.segment_length,
"Windowing function": self.windowing_function
}
@property
def freq_step(self):
""" Frequency step size of the Complex Coherence Spectrum."""
if self._freq_step is None:
self._freq_step = 1.0 / self.segment_length
msg = "%s: Frequency step size is %s"
self.log.debug(msg % (str(self), str(self._freq_step)))
return self._freq_step
@property
def max_freq(self):
""" Maximum frequency represented in the Complex Coherence Spectrum."""
if self._max_freq is None:
self._max_freq = 0.5 / self.source.sample_period
msg = "%s: Max frequency is %s"
self.log.debug(msg % (str(self), str(self._max_freq)))
return self._max_freq
@property
def frequency(self):
""" Frequencies represented in the Complex Coherence Spectrum."""
if self._frequency is None:
self._frequency = numpy.arange(self.freq_step,
self.max_freq + self.freq_step,
self.freq_step)
return self._frequency
