def load_example_fmri_dataset(name='1slice', literal=False):
"""Load minimal fMRI dataset that is shipped with PyMVPA."""
from mvpa2.datasets.sources.openfmri import OpenFMRIDataset
from mvpa2.datasets.mri import fmri_dataset
from mvpa2.misc.io import SampleAttributes
basedir = pathjoin(pymvpa_dataroot, 'haxby2001')
mask = {'1slice': pathjoin(pymvpa_dataroot, 'mask.nii.gz'),
'25mm': pathjoin(basedir, 'sub001', 'masks', '25mm',
'brain.nii.gz')}[name]
if literal:
model = 1
subj = 1
openfmri = OpenFMRIDataset(basedir)
ds = openfmri.get_model_bold_dataset(model, subj, flavor=name,
mask=mask, noinfolabel='rest')
# re-imagine the global time_coords of a concatenated time series
# this is only for the purpose of keeping the example data in the
# exact same shape as it has always been. in absolute terms this makes no
# sense as there is no continuous time in this dataset
ds.sa['run_time_coords'] = ds.sa.time_coords
ds.sa['time_coords'] = np.arange(len(ds)) * 2.5
else:
if name == '25mm':
raise ValueError("The 25mm dataset is no longer available with "
"numerical labels")
attr = SampleAttributes(pathjoin(pymvpa_dataroot, 'attributes.txt'))
ds = fmri_dataset(samples=pathjoin(pymvpa_dataroot, 'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=mask)
return ds
def load_example_fmri_dataset():
"""Load minimal fMRI dataset that is shipped with PyMVPA."""
from mvpa2.datasets.mri import fmri_dataset
from mvpa2.misc.io import SampleAttributes
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
return ds
def load_example_fmri_dataset(name='1slice', literal=False):
"""Load minimal fMRI dataset that is shipped with PyMVPA."""
from mvpa2.datasets.mri import fmri_dataset
from mvpa2.misc.io import SampleAttributes
dspath, mask = {
'1slice': (pymvpa_dataroot, 'mask.nii.gz'),
'25mm': (os.path.join(pymvpa_dataroot, 'tutorial_data_25mm',
'data'), 'mask_brain.nii.gz')
}[name]
if literal:
attr = SampleAttributes(os.path.join(dspath, 'attributes_literal.txt'))
else:
attr = SampleAttributes(os.path.join(dspath, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(dspath, 'bold.nii.gz'),
targets=attr.targets,
chunks=attr.chunks,
mask=os.path.join(dspath, mask))
return ds
def load_datadb_tutorial_data(path=os.path.join(
pymvpa_datadbroot, 'tutorial_data', 'tutorial_data', 'data'),
roi='brain'):
"""Loads the block-design demo dataset from PyMVPA dataset DB.
Parameters
----------
path : str
Path of the directory containing the dataset files.
roi : str or int or tuple or None
Region Of Interest to be used for masking the dataset. If a string is
given a corresponding mask image from the demo dataset will be used
(mask_<str>.nii.gz). If an int value is given, the corresponding ROI
is determined from the atlas image (mask_hoc.nii.gz). If a tuple is
provided it may contain int values that a processed as explained
before, but the union of a ROIs is taken to produce the final mask.
If None, no masking is performed.
"""
import nibabel as nb
from mvpa2.datasets.mri import fmri_dataset
from mvpa2.misc.io import SampleAttributes
if roi is None:
mask = None
elif isinstance(roi, str):
mask = os.path.join(path, 'mask_' + roi + '.nii.gz')
elif isinstance(roi, int):
nimg = nb.load(os.path.join(path, 'mask_hoc.nii.gz'))
tmpmask = nimg.get_data() == roi
mask = nb.Nifti1Image(tmpmask.astype(int), nimg.get_affine(),
nimg.get_header())
elif isinstance(roi, tuple) or isinstance(roi, list):
nimg = nb.load(os.path.join(path, 'mask_hoc.nii.gz'))
if externals.versions['nibabel'] >= '1.2':
img_shape = nimg.shape
else:
img_shape = nimg.get_shape()
tmpmask = np.zeros(img_shape, dtype='bool')
for r in roi:
tmpmask = np.logical_or(tmpmask, nimg.get_data() == r)
mask = nb.Nifti1Image(tmpmask.astype(int), nimg.get_affine(),
nimg.get_header())
else:
raise ValueError("Got something as mask that I cannot handle.")
attr = SampleAttributes(os.path.join(path, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(path, 'bold.nii.gz'),
targets=attr.targets,
chunks=attr.chunks,
mask=mask)
return ds