def plot_cv_tc(epi_data, session_ids, subject_id,
do_plot=True,
write_image=True, mask=True, bg_image=False,
plot_diff=True,
_output_dir=None,
cv_tc_plot_outfile=None):
""" Compute coefficient of variation of the data and plot it
Parameters
----------
epi_data: list of strings, input fMRI 4D images
session_ids: list of strings of the same length as epi_data,
session indexes (for figures)
subject_id: string, id of the subject (for figures)
do_plot: bool, optional,
should we plot the resulting time course
write_image: bool, optional,
should we write the cv image
mask: bool or string, optional,
(string) path of a mask or (bool) should we mask the data
bg_image: bool or string, optional,
(string) pasth of a background image for display or (bool)
should we compute such an image as the mean across inputs.
if no, an MNI template is used (works for normalized data)
"""
if _output_dir is None:
if not cv_tc_plot_outfile is None:
_output_dir = os.path.dirname(cv_tc_plot_outfile)
else:
_output_dir = tempfile.mkdtemp()
cv_tc_ = []
if isinstance(mask, basestring):
mask_array = nibabel.load(mask).get_data() > 0
elif mask == True:
mask_array = compute_mask_files(epi_data[0])
else:
mask_array = None
for (session_id, fmri_file) in zip(session_ids, epi_data):
nim = do_3Dto4D_merge(fmri_file, output_dir=_output_dir)
affine = nim.get_affine()
if len(nim.shape) == 4:
# get the data
data = nim.get_data()
else:
raise TypeError("Expecting 4D image!")
pass
# compute the CV for the session
cache_dir = os.path.join(_output_dir, "CV")
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
mem = joblib.Memory(cachedir=cache_dir, verbose=5)
cv = mem.cache(compute_cv)(data, mask_array=mask_array)
if write_image:
# write an image
nibabel.save(nibabel.Nifti1Image(cv, affine),
os.path.join(_output_dir, 'cv_%s.nii' % session_id))
if bg_image == False:
try:
viz.plot_map(
cv, affine, threshold=.01, cmap=viz.cm.cold_hot)
except IndexError:
print traceback.format_exc()
else:
if isinstance(bg_image, basestring):
_tmp = nibabel.load(bg_image)
anat, anat_affine = (
_tmp.get_data(),
_tmp.get_affine())
else:
anat, anat_affine = data.mean(-1), affine
try:
viz.plot_map(
cv, affine, threshold=.01, cmap=viz.cm.cold_hot,
anat=anat, anat_affine=anat_affine)
except IndexError:
print traceback.format_exc()
# compute the time course of cv
cv_tc_sess = np.median(
np.sqrt((data[mask_array > 0].T /
data[mask_array > 0].mean(-1) - 1) ** 2), 1)
cv_tc_.append(cv_tc_sess)
cv_tc = np.concatenate(cv_tc_)
if do_plot:
# plot the time course of cv for different subjects
pl.figure()
pl.plot(cv_tc, label=subject_id)
pl.legend()
pl.xlabel('time(scans)')
pl.ylabel('Median coefficient of variation')
pl.axis('tight')
if not cv_tc_plot_outfile is None:
pl.savefig(cv_tc_plot_outfile,
bbox_inches="tight", dpi=200)
return cv_tc
dataset_description=DATASET_DESCRIPTION,
do_shutdown_reloaders=False,
)
"""collect preprocessed data"""
"""collect preprocessed data"""
fmri_files = results[0]['func']
anat_file = results[0]['anat']
import nibabel as ni
if isinstance(fmri_files, basestring):
fmri_img = ni.load(fmri_files)
else:
output_filename = '/tmp/spm_auditory.nii.gz'
fmri_img = do_3Dto4D_merge(fmri_files,
output_filename=output_filename)
fmri_files = output_filename
"""construct design matrix"""
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
design_matrix = make_dmtx(frametimes,
paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut)
"""show design matrix"""
ax = design_matrix.show()
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
dmat_outfile = os.path.join(subject_data.output_dir, 'design_matrix.png')
