# -*- 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
#
#
import json
from tvb.datatypes.time_series import *
from sqlalchemy import Column, Integer, ForeignKey, String, Float, Boolean
from sqlalchemy.orm import relationship
from tvb.adapters.datatypes.db.sensors import SensorsIndex
from tvb.adapters.datatypes.db.connectivity import ConnectivityIndex
from tvb.adapters.datatypes.db.region_mapping import RegionMappingIndex, RegionVolumeMappingIndex
from tvb.adapters.datatypes.db.surface import SurfaceIndex
from tvb.adapters.datatypes.db.volume import VolumeIndex
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.entities.model.model_datatype import DataType
[docs]class TimeSeriesIndex(DataType):
id = Column(Integer, ForeignKey(DataType.id), primary_key=True)
time_series_type = Column(String, nullable=False)
data_ndim = Column(Integer, nullable=False)
data_length_1d = Column(Integer)
data_length_2d = Column(Integer)
data_length_3d = Column(Integer)
data_length_4d = Column(Integer)
start_time = Column(Float, default=0)
sample_period_unit = Column(String, nullable=False)
sample_period = Column(Float, nullable=False)
sample_rate = Column(Float)
labels_ordering = Column(String, nullable=False)
labels_dimensions = Column(String, nullable=False)
has_volume_mapping = Column(Boolean, nullable=False, default=False)
has_surface_mapping = Column(Boolean, nullable=False, default=False)
[docs] def fill_from_has_traits(self, datatype):
# type: (TimeSeries) -> None
super(TimeSeriesIndex, self).fill_from_has_traits(datatype)
self.title = datatype.title
self.time_series_type = type(datatype).__name__
self.start_time = datatype.start_time
self.sample_period_unit = datatype.sample_period_unit
self.sample_period = datatype.sample_period
self.sample_rate = datatype.sample_rate
self.labels_ordering = json.dumps(datatype.labels_ordering)
self.labels_dimensions = json.dumps(datatype.labels_dimensions)
# REVIEW THIS.
# In general constructing graphs here is a bad ideea
# But these NArrayIndex-es can be treated as part of this entity
# never to be referenced by any other row or table.
if hasattr(datatype, 'data'):
self.data_ndim = datatype.data.ndim
self.fill_shape(datatype.data.shape)
[docs] def fill_from_h5(self, h5_file):
super(TimeSeriesIndex, self).fill_from_h5(h5_file)
self.time_series_type = type(h5_file).__name__.replace('H5', '')
self.title = h5_file.title.load()
self.start_time = h5_file.start_time.load()
self.sample_period_unit = h5_file.sample_period_unit.load()
self.sample_period = h5_file.sample_period.load()
self.sample_rate = h5_file.sample_rate.load()
self.labels_ordering = json.dumps(h5_file.labels_ordering.load())
self.labels_dimensions = json.dumps(h5_file.labels_dimensions.load())
self.fill_shape(h5_file.data.shape)
[docs] def fill_shape(self, final_shape):
self.data_ndim = len(final_shape)
self.data_length_1d = final_shape[0]
if self.data_ndim > 1:
self.data_length_2d = final_shape[1]
if self.data_ndim > 2:
self.data_length_3d = final_shape[2]
if self.data_ndim > 3:
self.data_length_4d = final_shape[3]
[docs] @staticmethod
def accepted_filters():
filters = DataType.accepted_filters()
filters.update(
{FilterChain.datatype + '.data_ndim':
{'type': 'int', 'display': 'No of Dimensions', 'operations': ['==', '<', '>']},
FilterChain.datatype + '.sample_period':
{'type': 'float', 'display': 'Sample Period', 'operations': ['==', '<', '>']},
FilterChain.datatype + '.sample_rate':
{'type': 'float', 'display': 'Sample Rate', 'operations': ['==', '<', '>']},
FilterChain.datatype + '.title':
{'type': 'string', 'display': 'Title', 'operations': ['==', '!=', 'like']}
})
return filters
[docs] def get_data_shape(self):
if self.data_ndim == 1:
return self.data_length_1d
if self.data_ndim == 2:
return self.data_length_1d, self.data_length_2d
if self.data_ndim == 3:
return self.data_length_1d, self.data_length_2d, self.data_length_3d
return self.data_length_1d, self.data_length_2d, self.data_length_3d, self.data_length_4d
[docs] def get_labels_for_dimension(self, idx):
label_dimensions = json.loads(self.labels_dimensions)
labels_ordering = json.loads(self.labels_ordering)
return label_dimensions.get(labels_ordering[idx], ["0"])
[docs]class TimeSeriesEEGIndex(TimeSeriesIndex):
id = Column(Integer, ForeignKey(TimeSeriesIndex.id), primary_key=True)
fk_sensors_gid = Column(String(32), ForeignKey(SensorsIndex.gid), nullable=not TimeSeriesEEG.sensors.required)
sensors = relationship(SensorsIndex, foreign_keys=fk_sensors_gid)
[docs] def fill_from_has_traits(self, datatype):
# type: (TimeSeriesEEG) -> None
super(TimeSeriesEEGIndex, self).fill_from_has_traits(datatype)
self.fk_sensors_gid = datatype.sensors.gid.hex
# Because we had a ProjectionMatrix in the monitor
self.has_surface_mapping = True
[docs] def fill_from_h5(self, h5_file):
super(TimeSeriesEEGIndex, self).fill_from_h5(h5_file)
self.fk_sensors_gid = h5_file.sensors.load().hex
self.has_surface_mapping = True
[docs]class TimeSeriesMEGIndex(TimeSeriesIndex):
id = Column(Integer, ForeignKey(TimeSeriesIndex.id), primary_key=True)
fk_sensors_gid = Column(String(32), ForeignKey(SensorsIndex.gid), nullable=not TimeSeriesMEG.sensors.required)
sensors = relationship(SensorsIndex, foreign_keys=fk_sensors_gid)
[docs] def fill_from_has_traits(self, datatype):
# type: (TimeSeriesMEG) -> None
super(TimeSeriesMEGIndex, self).fill_from_has_traits(datatype)
self.fk_sensors_gid = datatype.sensors.gid.hex
self.has_surface_mapping = True
[docs] def fill_from_h5(self, h5_file):
super(TimeSeriesMEGIndex, self).fill_from_h5(h5_file)
self.fk_sensors_gid = h5_file.sensors.load().hex
self.has_surface_mapping = True
[docs]class TimeSeriesSEEGIndex(TimeSeriesIndex):
id = Column(Integer, ForeignKey(TimeSeriesIndex.id), primary_key=True)
fk_sensors_gid = Column(String(32), ForeignKey(SensorsIndex.gid), nullable=not TimeSeriesSEEG.sensors.required)
sensors = relationship(SensorsIndex, foreign_keys=fk_sensors_gid)
[docs] def fill_from_has_traits(self, datatype):
# type: (TimeSeriesSEEG) -> None
super(TimeSeriesSEEGIndex, self).fill_from_has_traits(datatype)
self.fk_sensors_gid = datatype.sensors.gid.hex
self.has_surface_mapping = True
[docs] def fill_from_h5(self, h5_file):
super(TimeSeriesSEEGIndex, self).fill_from_h5(h5_file)
self.fk_sensors_gid = h5_file.sensors.load().hex
self.has_surface_mapping = True
[docs]class TimeSeriesRegionIndex(TimeSeriesIndex):
id = Column(Integer, ForeignKey(TimeSeriesIndex.id), primary_key=True)
fk_connectivity_gid = Column(String(32), ForeignKey(ConnectivityIndex.gid),
nullable=not TimeSeriesRegion.connectivity.required)
connectivity = relationship(ConnectivityIndex, foreign_keys=fk_connectivity_gid,
primaryjoin=ConnectivityIndex.gid == fk_connectivity_gid)
fk_region_mapping_volume_gid = Column(String(32), ForeignKey(RegionVolumeMappingIndex.gid),
nullable=not TimeSeriesRegion.region_mapping_volume.required)
region_mapping_volume = relationship(RegionVolumeMappingIndex, foreign_keys=fk_region_mapping_volume_gid,
primaryjoin=RegionVolumeMappingIndex.gid == fk_region_mapping_volume_gid)
fk_region_mapping_gid = Column(String(32), ForeignKey(RegionMappingIndex.gid),
nullable=not TimeSeriesRegion.region_mapping.required)
region_mapping = relationship(RegionMappingIndex, foreign_keys=fk_region_mapping_gid,
primaryjoin=RegionMappingIndex.gid == fk_region_mapping_gid)
[docs] def fill_from_has_traits(self, datatype):
# type: (TimeSeriesRegion) -> None
super(TimeSeriesRegionIndex, self).fill_from_has_traits(datatype)
self.fk_connectivity_gid = datatype.connectivity.gid.hex
if datatype.region_mapping_volume is not None:
self.fk_region_mapping_volume_gid = datatype.region_mapping_volume.gid.hex
self.has_volume_mapping = True
if datatype.region_mapping is not None:
self.fk_region_mapping_gid = datatype.region_mapping.gid.hex
self.has_surface_mapping = True
[docs] def fill_from_h5(self, h5_file):
super(TimeSeriesRegionIndex, self).fill_from_h5(h5_file)
self.fk_connectivity_gid = h5_file.connectivity.load().hex
region_mapping_volume = h5_file.region_mapping_volume.load()
if region_mapping_volume is not None:
self.fk_region_mapping_volume_gid = region_mapping_volume.hex
self.has_volume_mapping = True
region_mapping = h5_file.region_mapping.load()
if region_mapping is not None:
self.fk_region_mapping_gid = region_mapping.hex
self.has_surface_mapping = True
[docs]class TimeSeriesSurfaceIndex(TimeSeriesIndex):
id = Column(Integer, ForeignKey(TimeSeriesIndex.id), primary_key=True)
fk_surface_gid = Column(String(32), ForeignKey(SurfaceIndex.gid), nullable=not TimeSeriesSurface.surface.required)
surface = relationship(SurfaceIndex, foreign_keys=fk_surface_gid)
[docs] def fill_from_has_traits(self, datatype):
# type: (TimeSeriesSurface) -> None
super(TimeSeriesSurfaceIndex, self).fill_from_has_traits(datatype)
self.fk_surface_gid = datatype.surface.gid.hex
self.has_surface_mapping = True
[docs] def fill_from_h5(self, h5_file):
super(TimeSeriesSurfaceIndex, self).fill_from_h5(h5_file)
self.fk_surface_gid = h5_file.surface.load().hex
self.has_surface_mapping = True
[docs]class TimeSeriesVolumeIndex(TimeSeriesIndex):
id = Column(Integer, ForeignKey(TimeSeriesIndex.id), primary_key=True)
fk_volume_gid = Column(String(32), ForeignKey(VolumeIndex.gid), nullable=not TimeSeriesVolume.volume.required)
volume = relationship(VolumeIndex, foreign_keys=fk_volume_gid)
[docs] def fill_from_has_traits(self, datatype):
# type: (TimeSeriesVolume) -> None
super(TimeSeriesVolumeIndex, self).fill_from_has_traits(datatype)
self.fk_volume_gid = datatype.volume.gid.hex
self.has_volume_mapping = True
[docs] def fill_from_h5(self, h5_file):
super(TimeSeriesVolumeIndex, self).fill_from_h5(h5_file)
self.fk_volume_gid = h5_file.volume.load().hex
self.has_volume_mapping = True
