Source code for tvb.adapters.datatypes.db.spectral

# -*- coding: utf-8 -*-
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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] def get_extra_info(self): labels_dict = {} labels_dict["labels_ordering"] = self.source.labels_ordering labels_dict["labels_dimensions"] = self.source.labels_dimensions return labels_dict
[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] def get_extra_info(self): labels_dict = {} labels_dict["labels_ordering"] = self.source.labels_ordering labels_dict["labels_dimensions"] = self.source.labels_dimensions return labels_dict
[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] def get_extra_info(self): labels_dict = {} labels_dict["labels_ordering"] = self.source.labels_ordering labels_dict["labels_dimensions"] = self.source.labels_dimensions return labels_dict
[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
[docs] def get_extra_info(self): labels_dict = {} labels_dict["labels_ordering"] = self.source.labels_ordering labels_dict["labels_dimensions"] = self.source.labels_dimensions return labels_dict