Source code for tvb.adapters.uploaders.nifti.parser

# -*- 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:: Lia Domide <>
.. moduleauthor:: Calin Pavel <>

import os
import numpy
import nibabel
from tvb.basic.logger.builder import get_logger
from tvb.core.adapters.exceptions import ParseException

[docs] class NIFTIParser(object): """ This class reads content of a NIFTI file and writes a 4D array [time, x, y, z]. """ def __init__(self, data_file): self.logger = get_logger(__name__) if data_file is None: raise ParseException("Please select NIFTI file which contains data to import") if not os.path.exists(data_file): raise ParseException("Provided file %s does not exists" % data_file) try: self.nifti_image = nibabel.load(data_file) except nibabel.spatialimages.ImageFileError as e: self.logger.exception(e) msg = "File: %s does not have a valid NIFTI-1 format." % data_file raise ParseException(msg) nifti_image_hdr = self.nifti_image.header # Check if there is a time dimensions (4th dimension). nifti_data_shape = nifti_image_hdr.get_data_shape() self.nr_dims = len(nifti_data_shape) self.has_time_dimension = self.nr_dims > 3 self.time_dim_size = nifti_data_shape[3] if self.has_time_dimension else 1 # Extract sample unit measure self.units = nifti_image_hdr.get_xyzt_units() # Usually zooms defines values for x, y, z, time and other dimensions self.zooms = nifti_image_hdr.get_zooms()
[docs] def parse(self): """ Parse NIFTI file and write in result_dt a 4D or 3D array [time*, x, y, z]. """ # Copy data from NIFTI file to our TVB storage # In NIFTI format time is the 4th dimension, while our TimeSeries has # it as first dimension, so we have to adapt imported data nifti_data = self.nifti_image.dataobj return numpy.array(nifti_data, dtype=numpy.int32)