Source code for tvb.adapters.uploaders.projection_matrix_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 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
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"""
.. moduleauthor:: Marmaduke Woodman <marmaduke.woodman@univ-amu.fr>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
"""
from tvb.adapters.datatypes.db.projections import ProjectionMatrixIndex
from tvb.adapters.datatypes.db.sensors import SensorsIndex
from tvb.basic.logger.builder import get_logger
from tvb.core.adapters.abcuploader import ABCUploader, ABCUploaderForm
from tvb.core.adapters.exceptions import LaunchException
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitUploadField, StrField, TraitDataTypeSelectField
from tvb.core.neotraits.uploader_view_model import UploaderViewModel
from tvb.core.neotraits.view_model import Str, DataTypeGidAttr
from tvb.datatypes.projections import *
from tvb.datatypes.projections import ProjectionMatrix
from tvb.datatypes.sensors import SensorsEEG, SensorsMEG, Sensors
from tvb.datatypes.surfaces import Surface
DEFAULT_DATASET_NAME = "ProjectionMatrix"
[docs]def determine_projection_type(sensors):
# type: (SensorsIndex) -> str
if sensors.sensors_type == SensorsEEG.sensors_type.default:
projection_matrix_type = ProjectionSurfaceEEG.projection_type.default
elif sensors.sensors_type == SensorsMEG.sensors_type.default:
projection_matrix_type = ProjectionSurfaceMEG.projection_type.default
else:
projection_matrix_type = ProjectionSurfaceSEEG.projection_type.default
return projection_matrix_type
[docs]class ProjectionMatrixImporterModel(UploaderViewModel):
projection_file = Str(
label='Projection matrix file (.mat or .npy format)',
doc='Expected a file containing projection matrix (one vector of length '
'number of surface vertices nd values in the sensors range).'
)
dataset_name = Attr(
field_type=str,
required=False,
default=DEFAULT_DATASET_NAME,
label='Matlab dataset name',
doc='Name of the MATLAB dataset where data is stored. Required only for .mat files'
)
surface = DataTypeGidAttr(
linked_datatype=Surface,
label='Brain Cortical Surface',
doc='The Brain Surface used by the uploaded projection matrix.'
)
sensors = DataTypeGidAttr(
linked_datatype=Sensors,
label='Sensors',
doc='The Sensors used in for current projection.'
)
[docs]class ProjectionMatrixSurfaceEEGImporter(ABCUploader):
"""
Upload ProjectionMatrix Cortical Surface -> EEG/MEG/SEEG Sensors from a MAT or NPY file.
"""
_ui_name = "Gain Matrix for Sensors"
_ui_description = "Upload a Projection Matrix between a Brain Cortical Surface and EEG/MEG Sensors."
logger = get_logger(__name__)
[docs] def get_output(self):
return [ProjectionMatrixIndex]
[docs] def launch(self, view_model):
# type: (ProjectionMatrixImporterModel) -> [ProjectionMatrixIndex]
"""
Creates ProjectionMatrix entity from uploaded data.
:raises LaunchException: when
* no projection_file or sensors are specified
* the dataset is invalid
* number of sensors is different from the one in dataset
"""
if view_model.projection_file is None:
raise LaunchException("Please select MATLAB file which contains data to import")
if view_model.sensors is None:
raise LaunchException("No sensors selected. Please initiate upload again and select one.")
if view_model.surface is None:
raise LaunchException("No source selected. Please initiate upload again and select a source.")
sensors_ht = self.load_traited_by_gid(view_model.sensors)
expected_sensors_shape = sensors_ht.number_of_sensors
self.logger.debug("Reading projection matrix from uploaded file...")
if view_model.projection_file.endswith(".mat"):
projection_data = self.read_matlab_data(view_model.projection_file, view_model.dataset_name)
else:
projection_data = self.read_list_data(view_model.projection_file)
if projection_data is None or len(projection_data) == 0:
raise LaunchException("Invalid (empty) dataset...")
if projection_data.shape[0] != expected_sensors_shape:
raise LaunchException("Invalid Projection Matrix shape[0]: %d Expected: %d" % (projection_data.shape[0],
expected_sensors_shape))
surface_idx = self.load_entity_by_gid(view_model.surface)
expected_surface_shape = surface_idx.number_of_vertices
if projection_data.shape[1] != expected_surface_shape:
raise LaunchException("Invalid Projection Matrix shape[1]: %d Expected: %d" % (projection_data.shape[1],
expected_surface_shape))
surface_ht = h5.load_from_index(surface_idx)
projection_matrix_type = determine_projection_type(sensors_ht)
projection_matrix = ProjectionMatrix(sources=surface_ht, sensors=sensors_ht,
projection_type=projection_matrix_type,
projection_data=projection_data)
return self.store_complete(projection_matrix)
