Source code for tvb.adapters.uploaders.nifti.parser
# -*- coding: utf-8 -*-
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
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from tvb.basic.logger.builder import get_logger
from tvb.core.adapters.exceptions import ParseException
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)
self.nifti_image = nibabel.load(data_file)
except nibabel.spatialimages.ImageFileError as e:
msg = "File: %s does not have a valid NIFTI-1 format." % data_file
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 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)