"""construct experimental paradigm"""
stats_start_time = time.ctime()
tr = 7.
n_scans = 96
_duration = 6
epoch_duration = _duration * tr
conditions = ['rest', 'active'] * 8
duration = epoch_duration * np.ones(len(conditions))
onset = np.linspace(0, (len(conditions) - 1) * epoch_duration,
len(conditions))
paradigm = BlockParadigm(con_id=conditions, onset=onset, duration=duration)
hfcut = 2 * 2 * epoch_duration
"""fetch spm auditory data"""
_subject_data = fetch_spm_auditory_data(DATA_DIR)
subject_data = nipype_preproc_spm_utils.SubjectData()
subject_data.subject_id = "sub001"
subject_data.func = _subject_data["func"]
subject_data.anat = _subject_data["anat"]
subject_data.output_dir = os.path.join(
OUTPUT_DIR, subject_data.subject_id)
print subject_data.func
"""preprocess the data"""
results = nipype_preproc_spm_utils.do_subjects_preproc(
[subject_data],
output_dir=OUTPUT_DIR,
dataset_id="SPM single-subject auditory",
dataset_description=DATASET_DESCRIPTION,
do_shutdown_reloaders=False,
def demo_BOLD(dataset='spm-auditory',
QA=True,
data_dir='/tmp/stc_demo',
output_dir='/tmp',
compare_with=None,
):
"""XXX This only works on my machine since you surely don't have
SPM single-subject auditory data or FSL FEEDS data installed on yours ;)
XXX TODO: interpolation can produce signal out-side the brain;
solve this with proper masking
Raises
------
Exception
"""
# sanitize dataset name
assert isinstance(dataset, basestring)
dataset = dataset.lower()
# sanitize output dir
if output_dir is None:
output_dir = '/tmp'
# demo specific imports
import nibabel as ni
import os
import sys
# load the data
slice_order = 'ascending'
interleaved = False
if dataset == 'spm-auditory':
# pypreproces path
PYPREPROCESS_DIR = os.path.dirname(os.path.split(
os.path.abspath(__file__))[0])
sys.path.append(PYPREPROCESS_DIR)
from datasets_extras import fetch_spm_auditory_data
_subject_data = fetch_spm_auditory_data(data_dir)
fmri_img = ni.concat_images(_subject_data['func'],)
fmri_data = fmri_img.get_data()[:, :, :, 0, :]
compare_with = ni.concat_images(
[os.path.join(os.path.dirname(x),
"a" + os.path.basename(x))
for x in _subject_data['func']]).get_data()
TR = 7.
elif dataset == 'fsl-feeds':
PYPREPROCESS_DIR = os.path.dirname(os.path.split(
os.path.abspath(__file__))[0])
sys.path.append(PYPREPROCESS_DIR)
from datasets_extras import fetch_fsl_feeds_data
_subject_data = fetch_fsl_feeds_data(data_dir)
if not _subject_data['func'].endswith('.gz'):
_subject_data['func'] += '.gz'
fmri_img = ni.load(_subject_data['func'],)
fmri_data = fmri_img.get_data()
TR = 3.
elif dataset == 'localizer':
output_filename = "/tmp/st_corrected_localizer.nii.gz"
fmri_img = ni.load(
"/home/elvis/.nipy/tests/data/s12069_swaloc1_corr.nii.gz")
fmri_data = fmri_img.get_data()
TR = 2.4
elif dataset == 'face-rep-spm5':
# XXX nibabel says the affines of the 3Ds are different
fmri_img = ni.load(
"/home/elvis/CODE/datasets/face_rep_SPM5/RawEPI/4D.nii.gz")
fmri_data = fmri_img.get_data()
TR = 2.
slice_order = 'descending'
else:
raise Exception("Unknown dataset: %s" % dataset)
output_filename = os.path.join(
output_dir,
"st_corrected_" + dataset.rstrip(" ").replace("-", "_") + ".nii.gz",
)
print "\r\n\t\t ---demo_BOLD (%s)---" % dataset
# fit STC
stc = STC()
stc.fit(raw_data=fmri_data, slice_order=slice_order,
interleaved=interleaved,
)
# do full-brain ST correction
print "Applying full-brain STC transform..."
corrected_fmri_data = stc.transform()
print "Done."
# save output unto disk
print "Saving ST corrected image to %s..." % output_filename
ni.save(ni.Nifti1Image(corrected_fmri_data, fmri_img.get_affine()),
output_filename)
print "Done."
# QA clinic
if QA:
x = 32
y = 32
print "Starting QA clinic (free entrance to the masses)..."
STC_QA(fmri_data, corrected_fmri_data, TR, x, y,
compare_with=compare_with)
def demo_real_BOLD(dataset='localizer',
data_dir='/tmp/stc_demo',
output_dir='/tmp',
compare_with=None,
QA=True,
):
"""Demo for real data.
Parameters
----------
dataset: string (optiona, defaul 'localizer')
name of dataset to demo. Possible values are:
spm-auditory: SPM single-subject auditory data (if absent,
will try to grab it over the net)
fsl-feeds: FSL-Feeds fMRI data (if absent, will try to grab
it over the net)
localizer: data used with nipy's localize_glm_ar.py demo; you'll
need nipy test data installed
face-rep-SPM5 (you need to download the data and point data_dir
to the containing folder)
data_dir: string (optional, '/tmp/stc_demo')
path to directory containing data; or destination
for downloaded data (in case we fetch from the net)
output_dir: string (optional, default "/tmp")
path to directory where all output (niftis, etc.)
will be written
compare_with: 4D array (optional, default None)
data to compare STC results with, must be same shape as
corrected data
QA: boolean (optional, default True)
if set, then QA plots will be generated after STC
Raises
------
Exception
"""
# sanitize dataset name
assert isinstance(dataset, basestring)
dataset = dataset.lower()
# sanitize output dir
if output_dir is None:
output_dir = '/tmp'
print "\r\n\t\t ---demo_real_BOLD (%s)---" % dataset
# load the data
slice_order = 'ascending'
interleaved = False
ref_slice = 0
print("Loading data...")
if dataset == 'spm-auditory':
# pypreproces path
PYPREPROCESS_DIR = os.path.dirname(os.path.split(
os.path.abspath(__file__))[0])
sys.path.append(PYPREPROCESS_DIR)
from datasets_extras import fetch_spm_auditory_data
_subject_data = fetch_spm_auditory_data(data_dir)
fmri_img = ni.concat_images(_subject_data['func'],)
fmri_data = fmri_img.get_data()[:, :, :, 0, :]
TR = 7.
elif dataset == 'fsl-feeds':
PYPREPROCESS_DIR = os.path.dirname(os.path.split(
os.path.abspath(__file__))[0])
sys.path.append(PYPREPROCESS_DIR)
from datasets_extras import fetch_fsl_feeds_data
_subject_data = fetch_fsl_feeds_data(data_dir)
if not _subject_data['func'].endswith('.gz'):
_subject_data['func'] += '.gz'
fmri_img = ni.load(_subject_data['func'],)
fmri_data = fmri_img.get_data()
TR = 3.
elif dataset == 'localizer':
data_path = os.path.join(
os.environ["HOME"],
".nipy/tests/data/s12069_swaloc1_corr.nii.gz")
if not os.path.exists(data_path):
raise RuntimeError("You don't have nipy test data installed!")
fmri_img = ni.load(data_path)
fmri_data = fmri_img.get_data()
TR = 2.4
elif dataset == 'face-rep-spm5':
# XXX nibabel says the affines of the 3Ds are different
fmri_data = np.array([ni.load(x).get_data() for x in sorted(glob.glob(
os.path.join(
data_dir,
"RawEPI/sM03953_0005_*.img")))])[:, :, :, 0, :]
if len(fmri_data) == 0:
raise RuntimeError(
"face-rep-SPM5 data not found in %s; install it it set the "
"parameter data_dir to the directory containing it")
TR = 2.
slice_order = 'descending'
ref_slice = fmri_data.shape[2] / 2 # middle slice
else:
raise RuntimeError("Unknown dataset: %s" % dataset)
output_filename = os.path.join(
output_dir,
"st_corrected_" + dataset.rstrip(" ").replace("-", "_") + ".nii",
)
print("Done.")
# fit STC
stc = STC()
stc.fit(raw_data=fmri_data,
slice_order=slice_order,
interleaved=interleaved,
ref_slice=ref_slice,
)
# do full-brain ST correction
stc.transform(fmri_data)
corrected_fmri_data = stc.get_last_output_data()
# save output unto disk
print "Saving ST corrected image to %s..." % output_filename
ni.save(ni.Nifti1Image(corrected_fmri_data, fmri_img.get_affine()),
output_filename)
print "Done."
# QA clinic
if QA:
plot_slicetiming_results(fmri_data,
corrected_fmri_data,
TR=TR,
compare_with=compare_with,
suptitle_prefix=dataset,
)
