Source code for tvb.adapters.uploaders.gifti_timeseries_importer

# -*- 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
# (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 <>.
# When using The Virtual Brain for scientific publications, please cite it as explained here:
.. moduleauthor:: Calin Pavel <>

import json
import uuid
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.adapters.abcuploader import ABCUploader, ABCUploaderForm
from tvb.core.adapters.exceptions import LaunchException, ParseException
from tvb.adapters.uploaders.gifti.parser import GIFTIParser
from tvb.basic.logger.builder import get_logger
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesSurfaceH5
from tvb.adapters.datatypes.db.time_series import TimeSeriesSurfaceIndex
from tvb.core.neotraits.forms import TraitUploadField, TraitDataTypeSelectField
from tvb.core.neotraits.db import prepare_array_shape_meta
from tvb.core.neotraits.uploader_view_model import UploaderViewModel
from tvb.core.neotraits.view_model import Str, DataTypeGidAttr
from tvb.datatypes.surfaces import Surface

[docs]class GIFTITimeSeriesImporterModel(UploaderViewModel): data_file = Str( label='Please select file to import (.gii)' ) surface = DataTypeGidAttr( linked_datatype=Surface, label='Brain Surface', doc='The Brain Surface used to generate imported TimeSeries.' )
[docs]class GIFTITimeSeriesImporterForm(ABCUploaderForm): def __init__(self): super(GIFTITimeSeriesImporterForm, self).__init__() self.data_file = TraitUploadField(GIFTITimeSeriesImporterModel.data_file, '.gii', 'data_file') surface_conditions = FilterChain(fields=[FilterChain.datatype + '.surface_type'], operations=["=="], values=['Cortical Surface']) self.surface = TraitDataTypeSelectField(GIFTITimeSeriesImporterModel.surface, name='surface', conditions=surface_conditions)
[docs] @staticmethod def get_view_model(): return GIFTITimeSeriesImporterModel
[docs] @staticmethod def get_upload_information(): return { 'data_file': '.gii' }
[docs]class GIFTITimeSeriesImporter(ABCUploader): """ This importer is responsible for import of a TimeSeries from GIFTI format (XML file) and store them in TVB. """ _ui_name = "TimeSeries GIFTI" _ui_subsection = "gifti_timeseries_importer" _ui_description = "Import TimeSeries from GIFTI"
[docs] def get_form_class(self): return GIFTITimeSeriesImporterForm
[docs] def get_output(self): return [TimeSeriesSurfaceIndex]
[docs] def launch(self, view_model): # type: (GIFTITimeSeriesImporterModel) -> [TimeSeriesSurfaceIndex] """ Execute import operations: """ if view_model.surface is None: raise LaunchException("No surface selected. Please initiate upload again and select a brain surface.") parser = GIFTIParser(self.operation_id) try: partial_time_series, gifti_data_arrays = parser.parse(view_model.data_file) ts_idx = TimeSeriesSurfaceIndex() ts_h5_path = self.path_for(TimeSeriesSurfaceH5, ts_idx.gid) ts_h5 = TimeSeriesSurfaceH5(ts_h5_path) # todo : make sure that write_time_slice is not required here for data_array in gifti_data_arrays: ts_h5.write_data_slice([]), scalars_only=True, store_references=False) ts_data_shape = ts_h5.read_data_shape() surface = self.load_entity_by_gid(view_model.surface) if surface.number_of_vertices != ts_data_shape[1]: msg = "Imported time series doesn't have values for all surface vertices. Surface has %d vertices " \ "while time series has %d values." % (surface.number_of_vertices, ts_data_shape[1]) raise LaunchException(msg) else: ts_idx.fk_surface_gid = surface.gid ts_h5.close() ts_idx.sample_period_unit = partial_time_series.sample_period_unit ts_idx.sample_period = partial_time_series.sample_period ts_idx.sample_rate = partial_time_series.sample_rate ts_idx.labels_ordering = json.dumps(partial_time_series.labels_ordering) ts_idx.labels_dimensions = json.dumps(partial_time_series.labels_dimensions) ts_idx.data_ndim = len(ts_data_shape) ts_idx.data_length_1d, ts_idx.data_length_2d, ts_idx.data_length_3d, ts_idx.data_length_4d = prepare_array_shape_meta( ts_data_shape) return [ts_idx] except ParseException as excep: logger = get_logger(__name__) logger.exception(excep) raise LaunchException(excep)