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#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""
Basic (f)MRI plotting
=====================
.. index:: plotting
Estimate basic univariate sensitivity (ANOVA) an plot it overlayed on top
of the anatomical.
We start with basic steps: loading PyMVPA and the example fMRI
dataset, basic preprocessing, estimation of the ANOVA scores and
plotting.
"""
from mvpa.suite import *
# load PyMVPA example dataset
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes_literal.txt'))
dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
labels=attr.labels,
labels_map=True,
chunks=attr.chunks,
mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
# since we don't have a proper anatomical -- lets overlay on BOLD
nianat = NiftiImage(dataset.O[0], header=dataset.niftihdr)
# do chunkswise linear detrending on dataset
detrend(dataset, perchunk=True, model='linear')
# define sensitivity analyzer
sensana = OneWayAnova(transformer=N.abs)
sens = sensana(dataset)
"""
It might be convinient to pre-define common arguments for multiple calls to
plotMRI
"""
mri_args = {
'background' : nianat, # could be a filename
'background_mask' : os.path.join(pymvpa_dataroot, 'mask.nii.gz'),
'overlay_mask' : os.path.join(pymvpa_dataroot, 'mask.nii.gz'),
'do_stretch_colors' : False,
'cmap_bg' : 'gray',
'cmap_overlay' : 'autumn', # YlOrRd_r # P.cm.autumn
'fig' : None, # create new figure
'interactive' : cfg.getboolean('examples', 'interactive', True),
}
fig = plotMRI(overlay=dataset.map2Nifti(sens),
vlim=(0.5, None),
#vlim_type="symneg_z",
**mri_args)
"""
Output of the example analysis:
.. image:: ../pics/ex_plotMRI.*
:align: center
:alt: Simple plotting facility for (f)MRI
"""
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