# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
# Web-UI helpful to run brain-simulations. To use it, you also need to download
# TheVirtualBrain-Scientific Package (for simulators). 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
#
#
from sqlalchemy import Column, Integer, ForeignKey, String, Float, Enum
from sqlalchemy.orm import relationship
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.core.entities.model.model_datatype import DataTypeMatrix
from tvb.core.neotraits.db import from_ndarray
from tvb.datatypes.spectral import FourierSpectrum, WaveletCoefficients, CoherenceSpectrum, ComplexCoherenceSpectrum, \
WindowingFunctionsEnum
[docs]class FourierSpectrumIndex(DataTypeMatrix):
id = Column(Integer, ForeignKey(DataTypeMatrix.id), primary_key=True)
segment_length = Column(Float, nullable=False)
windowing_function = Column(Enum(WindowingFunctionsEnum), nullable=True)
frequency_step = Column(Float, nullable=False)
max_frequency = Column(Float, nullable=False)
fk_source_gid = Column(String(32), ForeignKey(TimeSeriesIndex.gid), nullable=not FourierSpectrum.source.required)
source = relationship(TimeSeriesIndex, foreign_keys=fk_source_gid, primaryjoin=TimeSeriesIndex.gid == fk_source_gid)
[docs] def fill_from_has_traits(self, datatype):
# type: (FourierSpectrum) -> None
super(FourierSpectrumIndex, self).fill_from_has_traits(datatype)
self.segment_length = datatype.segment_length
self.windowing_function = datatype.windowing_function
self.frequency_step = datatype.freq_step
self.max_frequency = datatype.max_freq
self.fk_source_gid = datatype.source.gid.hex
[docs]class WaveletCoefficientsIndex(DataTypeMatrix):
id = Column(Integer, ForeignKey(DataTypeMatrix.id), primary_key=True)
fk_source_gid = Column(String(32), ForeignKey(TimeSeriesIndex.gid),
nullable=not WaveletCoefficients.source.required)
source = relationship(TimeSeriesIndex, foreign_keys=fk_source_gid, primaryjoin=TimeSeriesIndex.gid == fk_source_gid)
mother = Column(String, nullable=False)
normalisation = Column(String, nullable=False)
q_ratio = Column(Float, nullable=False)
sample_period = Column(Float, nullable=False)
number_of_scales = Column(Integer, nullable=False)
frequencies_min = Column(Float)
frequencies_max = Column(Float)
[docs] def fill_from_has_traits(self, datatype):
# type: (WaveletCoefficients) -> None
super(WaveletCoefficientsIndex, self).fill_from_has_traits(datatype)
self.mother = datatype.mother
self.normalisation = datatype.normalisation
self.q_ratio = datatype.q_ratio
self.sample_period = datatype.sample_period
self.number_of_scales = datatype.frequencies.shape[0]
self.frequencies_min, self.frequencies_max, _ = from_ndarray(datatype.frequencies)
self.fk_source_gid = datatype.source.gid.hex
[docs]class CoherenceSpectrumIndex(DataTypeMatrix):
id = Column(Integer, ForeignKey(DataTypeMatrix.id), primary_key=True)
fk_source_gid = Column(String(32), ForeignKey(TimeSeriesIndex.gid), nullable=not CoherenceSpectrum.source.required)
source = relationship(TimeSeriesIndex, foreign_keys=fk_source_gid, primaryjoin=TimeSeriesIndex.gid == fk_source_gid)
nfft = Column(Integer, nullable=False)
frequencies_min = Column(Float)
frequencies_max = Column(Float)
[docs] def fill_from_has_traits(self, datatype):
# type: (CoherenceSpectrum) -> None
super(CoherenceSpectrumIndex, self).fill_from_has_traits(datatype)
self.nfft = datatype.nfft
self.frequencies_min, self.frequencies_max, _ = from_ndarray(datatype.frequency)
self.fk_source_gid = datatype.source.gid.hex
[docs]class ComplexCoherenceSpectrumIndex(DataTypeMatrix):
id = Column(Integer, ForeignKey(DataTypeMatrix.id), primary_key=True)
fk_source_gid = Column(String(32), ForeignKey(TimeSeriesIndex.gid),
nullable=not ComplexCoherenceSpectrum.source.required)
source = relationship(TimeSeriesIndex, foreign_keys=fk_source_gid, primaryjoin=TimeSeriesIndex.gid == fk_source_gid)
epoch_length = Column(Float, nullable=False)
segment_length = Column(Float, nullable=False)
windowing_function = Column(String, nullable=False)
frequency_step = Column(Float, nullable=False)
max_frequency = Column(Float, nullable=False)
[docs] def fill_from_has_traits(self, datatype):
# type: (ComplexCoherenceSpectrum) -> None
super(ComplexCoherenceSpectrumIndex, self).fill_from_has_traits(datatype)
self.epoch_length = datatype.epoch_length
self.segment_length = datatype.segment_length
self.windowing_function = datatype.windowing_function
self.frequency_step = datatype.freq_step
self.max_frequency = datatype.max_freq
self.fk_source_gid = datatype.source.gid.hex
