"""
:Author: DOHMATOB Elvis Dopgima
:Synopsis: single_subject_pipeline.py demo
"""
import os
from pypreprocess.datasets import fetch_spm_auditory_data
from pypreprocess.purepython_preproc_utils import do_subject_preproc
from pypreprocess.subject_data import SubjectData
import nibabel
# fetch data
sd = fetch_spm_auditory_data(os.path.join(os.path.abspath('.'),
"spm_auditory"))
sd.output_dir = "/tmp/sub001"
sd.func = [sd.func]
# preproc data
do_subject_preproc(sd.__dict__, concat=False, coregister=True,
stc=True, cv_tc=True, realign=True,
report=True)
def _preprocess_and_analysis_subject(subject_data,
do_normalize=False,
fwhm=0.,
slicer='z',
cut_coords=6,
threshold=3.,
cluster_th=15
):
"""
Preprocesses the subject and then fits (mass-univariate) GLM thereupon.
"""
# sanitize run_ids:
# Sub14/BOLD/Run_02/fMR09029-0004-00010-000010-01.nii is garbage,
# for example
run_ids = range(9)
if subject_data['subject_id'] == "Sub14":
run_ids = [0] + range(2, 9)
subject_data['func'] = [subject_data['func'][0]] + subject_data[
'func'][2:]
subject_data['session_id'] = [subject_data['session_id'][0]
] + subject_data['session_id'][2:]
# sanitize subject output dir
if not 'output_dir' in subject_data:
subject_data['output_dir'] = os.path.join(
output_dir, subject_data['subject_id'])
# preprocess the data
subject_data = do_subject_preproc(SubjectData(**subject_data),
do_realign=True,
do_coreg=True,
do_report=False,
do_cv_tc=False
)
assert not subject_data.anat is None
# norm
if do_normalize:
subject_data = nipype_do_subject_preproc(
subject_data,
do_realign=False,
do_coreg=False,
do_segment=True,
do_normalize=True,
func_write_voxel_sizes=[3, 3, 3],
anat_write_voxel_sizes=[2, 2, 2],
fwhm=fwhm,
hardlink_output=False,
do_report=False
)
# chronometry
stats_start_time = pretty_time()
# to-be merged lists, one item per run
paradigms = []
frametimes_list = []
design_matrices = [] # one
list_of_contrast_dicts = [] # one dict per run
n_scans = []
for run_id in run_ids:
_n_scans = len(subject_data.func[run_id])
n_scans.append(_n_scans)
# make paradigm
paradigm = make_paradigm(getattr(subject_data, 'timing')[run_id])
# make design matrix
tr = 2.
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
hfcut = 128.
frametimes = np.linspace(0, (_n_scans - 1) * tr, _n_scans)
design_matrix = make_dmtx(
frametimes,
paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut,
add_regs=np.loadtxt(getattr(subject_data,
'realignment_parameters')[run_id]),
add_reg_names=[
'Translation along x axis',
'Translation along yaxis',
'Translation along z axis',
'Rotation along x axis',
'Rotation along y axis',
'Rotation along z axis'
]
)
# import matplotlib.pyplot as plt
# design_matrix.show()
# plt.show()
paradigms.append(paradigm)
design_matrices.append(design_matrix)
frametimes_list.append(frametimes)
n_scans.append(_n_scans)
# specify contrasts
contrasts = {}
n_columns = len(design_matrix.names)
for i in xrange(paradigm.n_conditions):
contrasts['%s' % design_matrix.names[2 * i]] = np.eye(
n_columns)[2 * i]
# more interesting contrasts"""
contrasts['Famous-Unfamiliar'] = contrasts[
'Famous'] - contrasts['Unfamiliar']
contrasts['Unfamiliar-Famous'] = -contrasts['Famous-Unfamiliar']
contrasts['Famous-Scrambled'] = contrasts[
'Famous'] - contrasts['Scrambled']
contrasts['Scrambled-Famous'] = -contrasts['Famous-Scrambled']
contrasts['Unfamiliar-Scrambled'] = contrasts[
'Unfamiliar'] - contrasts['Scrambled']
contrasts['Scrambled-Unfamiliar'] = -contrasts['Unfamiliar-Scrambled']
list_of_contrast_dicts.append(contrasts)
# importat maps
z_maps = {}
effects_maps = {}
# fit GLM
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FMRILinearModel([nibabel.concat_images(sess_func)
for sess_func in subject_data.func],
[design_matrix.matrix
for design_matrix in design_matrices],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
print "... done.\r\n"
# save computed mask
mask_path = os.path.join(subject_data.output_dir, "mask.nii.gz")
print "Saving mask image to %s ..." % mask_path
nibabel.save(fmri_glm.mask, mask_path)
print "... done.\r\n"
# replicate contrasts across runs
contrasts = dict((cid, [contrasts[cid]
for contrasts in list_of_contrast_dicts])
for cid, cval in contrasts.iteritems())
# compute effects
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
z_map, eff_map = fmri_glm.contrast(
contrast_val,
con_id=contrast_id,
output_z=True,
output_stat=False,
output_effects=True,
output_variance=False
)
# store stat maps to disk
for map_type, out_map in zip(['z', 'effects'], [z_map, eff_map]):
map_dir = os.path.join(
subject_data.output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(
map_dir, '%s.nii.gz' % contrast_id)
print "\t\tWriting %s ..." % map_path
nibabel.save(out_map, map_path)
# collect zmaps for contrasts we're interested in
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# remove repeated contrasts
contrasts = dict((cid, cval[0]) for cid, cval in contrasts.iteritems())
# do stats report
stats_report_filename = os.path.join(getattr(subject_data,
'reports_output_dir',
subject_data.output_dir),
"report_stats.html")
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.mask,
threshold=threshold,
cluster_th=cluster_th,
slicer=slicer,
cut_coords=cut_coords,
anat=nibabel.load(subject_data.anat).get_data(),
anat_affine=nibabel.load(subject_data.anat).get_affine(),
design_matrices=design_matrices,
subject_id=subject_data.subject_id,
start_time=stats_start_time,
title="GLM for subject %s" % subject_data.subject_id,
# additional ``kwargs`` for more informative report
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
drift_model=drift_model,
hrf_model=hrf_model,
paradigm=dict(("Run_%02i" % (run_id + 1), paradigms[run_id].__dict__)
for run_id in run_ids),
frametimes=dict(("Run_%02i" % (run_id + 1), frametimes_list[run_id])
for run_id in run_ids),
# fwhm=fwhm
)
ProgressReport().finish_dir(subject_data.output_dir)
print "\r\nStatistic report written to %s\r\n" % stats_report_filename
return contrasts, effects_maps, z_maps, mask_path
"""
Author: DOHMATOB Elvis Dopgima elvis[dot]dohmatob[at]inria[dot]fr
Synopsis: single_subject_pipeline.py demo
"""
from pypreprocess.datasets import fetch_spm_multimodal_fmri
from pypreprocess.purepython_preproc_utils import do_subject_preproc
# fetch data
sd = fetch_spm_multimodal_fmri()
sd.output_dir = "/tmp/sub001"
sd.func = [sd.func1, sd.func2]
# preproc data
do_subject_preproc(sd.__dict__,
concat=False,
coregister=True,
stc=True,
tsdiffana=True,
realign=True,
report=True,
reslice=True)
"""
Author: DOHMATOB Elvis Dopgima elvis[dot]dohmatob[at]inria[dot]fr
Synopsis: single_subject_pipeline.py demo
"""
from pypreprocess.datasets import fetch_spm_multimodal_fmri
from pypreprocess.purepython_preproc_utils import do_subject_preproc
# fetch data
sd = fetch_spm_multimodal_fmri()
sd.output_dir = "/tmp/sub001"
sd.func = [sd.func1, sd.func2]
# preproc data
do_subject_preproc(sd.__dict__, concat=False, coregister=True, stc=True,
tsdiffana=True, realign=True, report=True, reslice=True)
if __name__ == "__main__":
for subject in subjects:
subject_dir = os.path.join(work_dir, subject)
t1_dir = os.path.join(subject_dir, 't1')
fmri_dir = os.path.join(subject_dir, 'fmri')
anat_image = glob.glob(os.path.join(t1_dir, 'anat*.nii'))[0]
fmri_images = glob.glob(os.path.join(fmri_dir,
'visualcategs/visu*.nii'))
fmri_images += glob.glob(os.path.join(fmri_dir,
'audiosentence/audio*.nii'))
fmri_images += glob.glob(os.path.join(fmri_dir,
'localizer/loc*.nii'))
preproc_dict = {
'n_sessions': len(fmri_images),
'func': fmri_images,
'anat': anat_image,
'subject_id': subject,
'output_dir': fmri_dir
}
# do_subject_preproc(preproc_dict, concat=False, do_coreg=True,
# do_stc=False, do_cv_tc=True, do_realign=True,
# do_report=True)
do_subject_preproc(preproc_dict,
coregister=True,
cv_tc=0,
realign=True,
report=True,)
import os
from pypreprocess.datasets import fetch_spm_auditory_data
from pypreprocess.purepython_preproc_utils import do_subject_preproc
import nibabel
# fetch data
sd = fetch_spm_auditory_data(os.path.join(os.path.abspath('.'),
"spm_auditory"))
# preproc data
for flag in xrange(3):
for pre_concat in [True, False]:
# pack data into dict, the format understood by the pipeleine
subject_data = {
'n_sessions': 1, # number of sessions
'func': [sd.func], # functional (BOLD) images 1 item/session
'anat': sd.anat, # anatomical (structural) image
'subject_id': 'sub001',
'output_dir': os.path.abspath('spm_auditory_preproc')
}
if pre_concat:
subject_data['func'] = [nibabel.concat_images(sess_func,
check_affines=False)
for sess_func in subject_data['func']]
do_subject_preproc(subject_data,
stc=True,
fwhm=[8] * 3,
write_output_images=flag
)
def _preprocess_and_analysis_subject(subject_data,
do_normalize=False,
fwhm=0.,
slicer='z',
cut_coords=6,
threshold=3.,
cluster_th=15):
"""
Preprocesses the subject and then fits (mass-univariate) GLM thereupon.
"""
# sanitize run_ids:
# Sub14/BOLD/Run_02/fMR09029-0004-00010-000010-01.nii is garbage,
# for example
run_ids = range(9)
if subject_data['subject_id'] == "Sub14":
run_ids = [0] + range(2, 9)
subject_data['func'] = [subject_data['func'][0]
] + subject_data['func'][2:]
subject_data['session_id'] = [subject_data['session_id'][0]
] + subject_data['session_id'][2:]
# sanitize subject output dir
if not 'output_dir' in subject_data:
subject_data['output_dir'] = os.path.join(output_dir,
subject_data['subject_id'])
# preprocess the data
subject_data = do_subject_preproc(SubjectData(**subject_data),
do_realign=True,
do_coreg=True,
do_report=False,
do_cv_tc=False)
assert not subject_data.anat is None
# norm
if do_normalize:
subject_data = nipype_do_subject_preproc(
subject_data,
do_realign=False,
do_coreg=False,
do_segment=True,
do_normalize=True,
func_write_voxel_sizes=[3, 3, 3],
anat_write_voxel_sizes=[2, 2, 2],
fwhm=fwhm,
hardlink_output=False,
do_report=False)
# chronometry
stats_start_time = pretty_time()
# to-be merged lists, one item per run
paradigms = []
frametimes_list = []
design_matrices = [] # one
list_of_contrast_dicts = [] # one dict per run
n_scans = []
for run_id in run_ids:
_n_scans = len(subject_data.func[run_id])
n_scans.append(_n_scans)
# make paradigm
paradigm = make_paradigm(getattr(subject_data, 'timing')[run_id])
# make design matrix
tr = 2.
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
hfcut = 128.
frametimes = np.linspace(0, (_n_scans - 1) * tr, _n_scans)
design_matrix = make_dmtx(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
hfcut=hfcut,
add_regs=np.loadtxt(
getattr(subject_data, 'realignment_parameters')[run_id]),
add_reg_names=[
'Translation along x axis', 'Translation along yaxis',
'Translation along z axis', 'Rotation along x axis',
'Rotation along y axis', 'Rotation along z axis'
])
# import matplotlib.pyplot as plt
# design_matrix.show()
# plt.show()
paradigms.append(paradigm)
design_matrices.append(design_matrix)
frametimes_list.append(frametimes)
n_scans.append(_n_scans)
# specify contrasts
contrasts = {}
n_columns = len(design_matrix.names)
for i in xrange(paradigm.n_conditions):
contrasts['%s' %
design_matrix.names[2 * i]] = np.eye(n_columns)[2 * i]
# more interesting contrasts"""
contrasts['Famous-Unfamiliar'] = contrasts['Famous'] - contrasts[
'Unfamiliar']
contrasts['Unfamiliar-Famous'] = -contrasts['Famous-Unfamiliar']
contrasts[
'Famous-Scrambled'] = contrasts['Famous'] - contrasts['Scrambled']
contrasts['Scrambled-Famous'] = -contrasts['Famous-Scrambled']
contrasts['Unfamiliar-Scrambled'] = contrasts[
'Unfamiliar'] - contrasts['Scrambled']
contrasts['Scrambled-Unfamiliar'] = -contrasts['Unfamiliar-Scrambled']
list_of_contrast_dicts.append(contrasts)
# importat maps
z_maps = {}
effects_maps = {}
# fit GLM
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FMRILinearModel(
[nibabel.concat_images(sess_func) for sess_func in subject_data.func],
[design_matrix.matrix for design_matrix in design_matrices],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
print "... done.\r\n"
# save computed mask
mask_path = os.path.join(subject_data.output_dir, "mask.nii.gz")
print "Saving mask image to %s ..." % mask_path
nibabel.save(fmri_glm.mask, mask_path)
print "... done.\r\n"
# replicate contrasts across runs
contrasts = dict(
(cid, [contrasts[cid] for contrasts in list_of_contrast_dicts])
for cid, cval in contrasts.iteritems())
# compute effects
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
z_map, eff_map = fmri_glm.contrast(contrast_val,
con_id=contrast_id,
output_z=True,
output_stat=False,
output_effects=True,
output_variance=False)
# store stat maps to disk
for map_type, out_map in zip(['z', 'effects'], [z_map, eff_map]):
map_dir = os.path.join(subject_data.output_dir,
'%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s.nii.gz' % contrast_id)
print "\t\tWriting %s ..." % map_path
nibabel.save(out_map, map_path)
# collect zmaps for contrasts we're interested in
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# remove repeated contrasts
contrasts = dict((cid, cval[0]) for cid, cval in contrasts.iteritems())
# do stats report
stats_report_filename = os.path.join(
getattr(subject_data, 'reports_output_dir', subject_data.output_dir),
"report_stats.html")
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.mask,
threshold=threshold,
cluster_th=cluster_th,
slicer=slicer,
cut_coords=cut_coords,
anat=nibabel.load(subject_data.anat).get_data(),
anat_affine=nibabel.load(subject_data.anat).get_affine(),
design_matrices=design_matrices,
subject_id=subject_data.subject_id,
start_time=stats_start_time,
title="GLM for subject %s" % subject_data.subject_id,
# additional ``kwargs`` for more informative report
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
drift_model=drift_model,
hrf_model=hrf_model,
paradigm=dict(("Run_%02i" % (run_id + 1), paradigms[run_id].__dict__)
for run_id in run_ids),
frametimes=dict(("Run_%02i" % (run_id + 1), frametimes_list[run_id])
for run_id in run_ids),
# fwhm=fwhm
)
ProgressReport().finish_dir(subject_data.output_dir)
print "\r\nStatistic report written to %s\r\n" % stats_report_filename
return contrasts, effects_maps, z_maps, mask_path
:Author: DOHMATOB Elvis Dopgima
:Synopsis: single_subject_pipeline.py demo
"""
import os
from pypreprocess.datasets import fetch_spm_auditory_data
from pypreprocess.purepython_preproc_utils import do_subject_preproc
from pypreprocess._spike.pipeline_comparisons import execute_spm_auditory_glm
# fetch data
sd = fetch_spm_auditory_data(os.path.join(os.environ['HOME'],
"CODE/datasets/spm_auditory"))
# pack data into dict, the format understood by the pipeleine
subject_data = {'n_sessions': 1, # number of sessions
'func': [sd.func], # functional (BOLD) images 1 item/session
'anat': sd.anat, # anatomical (structural) image
'subject_id': 'sub001',
'output_dir': os.path.abspath('spm_auditory_preproc')
}
# preproc data
subject_data = do_subject_preproc(subject_data,
do_stc=True,
fwhm=[8] * 3
)
# run glm on data
execute_spm_auditory_glm(subject_data)