def main(**args):
path = "/mnt/DATA_4Tera/Dati_Sherlock/bids/"
derivatives = os.path.join(path, "derivatives", "afniproc")
print("mkdir -p {}".format(derivatives))
os.system("mkdir -p {}".format(derivatives))
subj_deriv = os.path.join(derivatives, 'sub-{subject}', "ses-{session}")
layout = BIDSLayout(path)
subjects = layout.get_subjects()
sessions = layout.get_sessions()
for session in sessions:
for subj in subjects:
deriv_dir = subj_deriv.format(session=session, subject=subj)
print("mkdir -p {}".format(deriv_dir))
os.system("mkdir -p {}".format(deriv_dir))
# Create anat and func
anat_dir = os.path.join(subj_deriv,
"{datatype}").format(session=session,
subject=subj,
datatype='anat')
func_dir = os.path.join(subj_deriv,
"{datatype}").format(session=session,
subject=subj,
datatype='func')
print("mkdir -p {}".format(anat_dir))
os.system("mkdir -p {}".format(anat_dir))
print("mkdir -p {}".format(func_dir))
os.system("mkdir -p {}".format(func_dir))
# Check and convert T1 to send Freesurfer segmentation
t1 = layout.get(subject=subj, session=session, suffix='T1w')[0]
entities = t1.get_entities()
pattern = os.path.join(
subj_deriv, "{datatype}",
"sub-{subject}[_ses-{session}][_desc-{desc}]_{suffix}.{extension}"
)
entities['desc'] = 'fsprep'
t1_fs = layout.build_path(entities, pattern, validate=False)
entities['extension'] = 'txt'
t1_log = layout.build_path(entities, pattern, validate=False)
command = "check_dset_for_fs.py -input %s -fix_all -fix_out_prefix %s -fix_out_vox_dim 1 -verb > %s"
command = command % (t1.path, t1_fs, t1_log)
print(command)
os.system(command)
check_fs(layout, subj, session, subj_deriv)
### T1 to MNI space coreg
entities = t1.get_entities()
entities['desc'] = 'fsprep'
entities['space'] = 'MNI152'
pattern = os.path.join(
subj_deriv, "{datatype}",
"sub-{subject}[_ses-{session}][_space-{space}][_desc-{desc}]_{suffix}.{extension}"
)
t1_mni = layout.build_path(entities, pattern, validate=False)
command = "@auto_tlrc -base MNI152_2009_template.nii.gz -pad_base 35 -prefix {prefix} -input {input}"
command = command.format(prefix=t1_mni, input=t1_fs)
print(command)
os.system(command)
runs = layout.get_runs()
ordered_bold = []
for run in runs:
fname = layout.get(session=session,
subject=subj,
run=run,
suffix='bold')
if len(fname) != 0:
ordered_bold.append(fname[0])
# Slice time correction - motion correction - align EPI to Anat to MNI
bold = layout.get(subject=subj,
session=session,
suffix='bold',
extension='nii.gz')
# 1D File
slice_timing = np.array(bold[0].get_metadata()['SliceTiming'])
slice_fname = os.path.join(path, "slice_timing.txt")
np.savetxt(slice_fname,
slice_timing,
delimiter=' ',
newline=' ',
fmt='%.5f')
epi = layout.get(subject=subj,
session=session,
suffix='bold',
extension='nii.gz',
run=1)[0].path
child_epi = " ".join([b.path for b in ordered_bold])
command = "align_epi_anat.py -anat {anat} -epi {epi} -child_epi {child_epi}"+\
" -epi_base 0 -tshift_opts -tpattern {tpattern} -epi2anat -giant_move"+\
" -tlrc_apar {tlrc_apar}"
command = command.format(anat=t1_fs,
epi=epi,
child_epi=child_epi,
tpattern=slice_fname,
tlrc_apar=t1_mni)
print(command)
os.system(command)
# Create mask
automask = " ".join(
[b.filename[:-7] + '_tlrc_al+tlrc.HEAD' for b in bold])
mean_mask_prefix = 'mean_mni.nii.gz'
command = '3dTstat -prefix {prefix} {input}'.format(
prefix=mean_mask_prefix, input=automask)
print(command)
os.system(command)
entities = bold[0].get_entities()
entities['suffix'] = 'mask'
entities['space'] = 'MNI152'
pattern = os.path.join(
subj_deriv, "{datatype}",
"sub-{subject}[_ses-{session}][_space-{space}][_desc-{desc}]_{suffix}.{extension}"
)
mask_prefix = layout.build_path(entities, pattern, validate=False)
command = '3dAutomask -prefix {prefix} {input}'.format(
input=mean_mask_prefix, prefix=mask_prefix)
print(command)
os.system(command)
# Clean files
removed = " ".join([b.filename[:-7] + '_al+orig.*' for b in bold])
command = "rm " + removed
print(command)
os.system(command)
command = "rm __tt_*.*"
print(command)
os.system(command)
command = "rm malldump.*"
print(command)
os.system(command)
# Put files in BIDS
header = [
'trans_x', 'trans_y', 'trans_z', 'rot_x', 'rot_y', 'rot_z'
]
motion_ordered = list()
for bold in ordered_bold:
motion_fname = bold.filename[:-7] + "_vr_motion.1D"
motion = np.genfromtxt(motion_fname)
motion_ordered.append(motion_fname)
entities = bold.get_entities()
entities['suffix'] = 'motion'
entities['desc'] = 'volreg'
entities['extension'] = 'tsv'
pattern = os.path.join(
subj_deriv, "{datatype}",
"sub-{subject}_ses-{session}_task-{task}_run-{run:02d}_desc-{desc}_{suffix}.{extension}"
)
motion_bids = pattern.format(**entities)
motion = motion[:, [3, 4, 5, 0, 1, 2]]
np.savetxt(motion_bids,
motion,
fmt="%f",
delimiter="\t",
header="\t".join(header))
print("rm " + motion_fname)
os.system("rm " + motion_fname)
for desc in ['mat', "reg_mat", "tlrc_mat"]:
affine_fname = bold.filename[:-7] + "_al_" + desc + ".aff12.1D"
entities = bold.get_entities()
entities['suffix'] = 'affine'
entities['desc'] = desc.replace("_", "")
entities['extension'] = 'tsv'
pattern = os.path.join(
subj_deriv, "{datatype}",
"sub-{subject}_ses-{session}_task-{task}_run-{run:02d}_desc-{desc}_{suffix}.{extension}"
)
affine_bids = pattern.format(**entities)
command = "mv {0} {1}".format(affine_fname, affine_bids)
print(command)
os.system(command)
afni_bold = bold.filename[:-7] + "_tlrc_al+tlrc"
entities = bold.get_entities()
entities['desc'] = "afniproc"
entities['extension'] = 'nii.gz'
entities['space'] = 'MNI152'
pattern = os.path.join(
subj_deriv, "{datatype}",
"sub-{subject}_ses-{session}_task-{task}_run-{run:02d}_space-{space}_desc-{desc}_{suffix}.{extension}"
)
afni_bids = pattern.format(**entities)
command = "3dcopy {0} {1}".format(afni_bold, afni_bids)
print(command)
os.system(command)
print("rm {}*".format(afni_bold))
os.system("rm {}*".format(afni_bold))
# Create confound regressors
# Motion
motion_files = list()
for run in runs:
f = layout.get(subject=subj,
session=session,
task=session,
run=run,
suffix='motion')
if len(f) != 0:
motion_files.append(f[0])
motion_df = [
pd.read_csv(m.path, delimiter="\t") for m in motion_files
]
motion_demean = [m - m.mean(0) for m in motion_df]
motion_demean = pd.concat(motion_demean)
entities = motion_files[0].get_entities()
entities['desc'] = 'demean'
pattern = os.path.join(
subj_deriv, "{datatype}",
"sub-{subject}_ses-{session}_desc-{desc}_{suffix}.{extension}")
demean_fname = pattern.format(**entities)
motion_demean.to_csv(demean_fname,
header=False,
index=False,
sep="\t")
motion_deriv = [m.diff() for m in motion_df]
motion_deriv = [m.fillna(0) for m in motion_deriv]
motion_deriv = [m - m.mean(0) for m in motion_deriv]
motion_deriv = pd.concat(motion_deriv)
entities['desc'] = 'deriv'
deriv_fname = pattern.format(**entities)
motion_deriv.to_csv(deriv_fname,
header=False,
index=False,
sep="\t")
tr_counts = [m.shape[0] for m in motion_df]
for j, t in enumerate(tr_counts):
command = "1dBport -nodata {ntr} 1 -band 0.01 999 -invert -nozero > bpass.1D".format(
ntr=t)
print(command)
os.system(command)
command = "1d_tool.py -infile bpass.1D -pad_into_many_runs {run:1d} {n_runs}"+\
" -set_run_lengths {tr_counts} -write bpass.{run:02d}.1D"
command = command.format(run=j + 1,
n_runs=str(len(tr_counts)),
tr_counts=" ".join(
[str(t) for t in tr_counts]))
print(command)
os.system(command)
entities['desc'] = 'bpass'
entities['suffix'] = 'timeseries'
entities['extension'] = '1D'
bpass_fname = pattern.format(**entities)
command = "1dcat bpass.*.1D > {}".format(bpass_fname)
print(command)
os.system(command)
command = "rm bpass*"
print(command)
os.system(command)
acquisition=list(filter(lambda x: "acq-" in x, labels))
if acquisition:
acquisitionValue=acquisition[0].split('-')[1]
entities['acquisition']=acquisitionValue
else:
entities.pop('acquisition', None)
run=list(filter(lambda x: "run-" in x, labels))
if run:
runValue=run[0].split('-')[1]
entities['run']=runValue
else:
entities.pop('run', None)
entities['extension']='nii.gz'
outputfile=os.path.join(niftidir, layout.build_path(entities))
if os.path.exists(sourcefile):
logtext (LOGFILE, "copying %s to %s" %(sourcefile, outputfile))
subprocess.check_output(['cp',sourcefile,outputfile])
else:
logtext (LOGFILE, "ERROR: %s cannot be found. Check bidsaction file logic." % sourcefile)
entities['extension']='json'
outputjson=os.path.join(niftidir, layout.build_path(entities))
if os.path.exists(sourcejson):
logtext (LOGFILE, "copying %s to %s" %(sourcejson, outputjson))
subprocess.check_output(['cp',sourcejson, outputjson])
else:
logtext (LOGFILE, "ERROR: %s cannot be found. Check bidsaction file logic." % sourcejson)
def main(**args):
outfiles = ['fitts', 'errts', 'stats', 'betas']
path = args['path']
pipeline = args['pipeline']
command = '3dDeconvolve -input {files} -jobs {n_jobs} -polort {polort} -float {confounds} {events_string} '+ \
' -mask {mask} -allzero_OK -fout -tout -x1D {design_matrix_txt} -xjpeg {design_matrix_jpg} -xsave '+\
'-fitts {fitts} -errts {errts} -bucket {stats} -cbucket {betas} -rout -gltsym "SYM: RESP+L -RESP+R" -glt_label 1 RespLvsRespR'
extra_event = process_extraevent_arg(args['extra_event'])
derivatives = os.path.join(path, "derivatives", pipeline)
print("mkdir -p {}".format(derivatives))
os.system("mkdir -p {}".format(derivatives))
derivatives_pattern = os.path.join(derivatives, 'sub-{subject}',
"ses-{session}")
pattern = os.path.join(
derivatives_pattern, "{datatype}",
"sub-{subject}[_ses-{session}][_space-{space}][_desc-{desc}]_{suffix}.{extension}"
)
layout = BIDSLayout(path, derivatives=True)
subjects = layout.get_subjects()
subjects.remove('lormat')
sessions = layout.get_sessions()
# TODO: Check if there are sessions
for session in sessions:
for subj in subjects:
deriv_dir = derivatives_pattern.format(session=session,
subject=subj)
print("mkdir -p {}".format(deriv_dir))
os.system("mkdir -p {}".format(deriv_dir))
# Create func
func_dir = os.path.join(derivatives_pattern,
"{datatype}").format(session=session,
subject=subj,
datatype='func')
print("mkdir -p {}".format(func_dir))
os.system("mkdir -p {}".format(func_dir))
# Main command
files = layout.get(subject=subj,
session=session,
task=session,
desc='afniproc',
extension='nii.gz')
entities = files[0].get_entities()
files = " ".join(f.path for f in files)
args['files'] = files
confounds = ''
for desc in ['bpass', 'demean']:
ort_files = layout.get(subject=subj,
session=session,
desc=desc)
confounds += '-ortvec {} {} '.format(ort_files[0].path, desc)
args['confounds'] = confounds
# Stimuli
stims = bids2afni_events(subj,
session,
layout,
pattern,
extra_event=extra_event)
write_afni(stims)
args['events_string'] = stims_times(stims)
# Mask
mask = layout.get(subject=subj,
session=session,
suffix='mask',
extension='nii.gz')
args['mask'] = mask[0].path
# Buckets
for desc in outfiles:
entities['desc'] = pipeline
entities['suffix'] = desc
args[desc] = layout.build_path(entities,
pattern,
validate=False)
for extension in ['jpg', 'txt']:
entities['suffix'] = 'dmatrix'
entities['extension'] = extension
args['design_matrix_' + extension] = layout.build_path(
entities, pattern, validate=False)
print(command.format(**args))
os.system(command.format(**args))
def convert(input_path, output_path, include_ses=False):
in_path = Path(input_path)
if not in_path.is_dir():
msg = f"{input_path} is not a valid directory."
raise ValueError(msg)
# Make output path
out_path = Path(output_path)
if not out_path.is_dir():
out_path.mkdir(parents=True)
# Generate dataset_description.json
data = dict(Name="hcp", BIDSVersion="1.4.0", DatasetType="raw")
with open(out_path / "dataset_description.json", "w") as f:
json.dump(data, f)
layout = BIDSLayout(out_path.absolute())
# Iterate through each subject folder
subject_folders = [x for x in in_path.iterdir() if x.is_dir()]
for subject_folder in subject_folders:
if not (subject_folder / "unprocessed/3T/").is_dir():
continue
# 6 digit sub id in str form
subject = subject_folder.name
modality_folders = [
x for x in (subject_folder / "unprocessed/3T/").iterdir()
if x.is_dir()
]
for modality_folder in modality_folders:
modality = modality_folder.name
# Make bids output folders
_mkdir(layout, subject, modality, include_ses)
if modality == "T1w_MPR1":
entities = dict(
subject=subject,
datatype=modality_dict[modality],
extension=".nii.gz",
suffix="T1w",
)
if include_ses:
entities["session"] = "1"
new_fname = layout.build_path(entities, pattern)
# Rename old files
old_fname = list(modality_folder.iterdir())[0]
old_fname.rename(new_fname)
elif modality == "Diffusion":
for fname in modality_folder.iterdir():
splits = fname.name.split(".")
extension = "." + splits[-1] # Get extension
if extension == ".gz":
extension = ".nii.gz"
splits = splits[0].split("_")
direction = splits[-1] # Direction. RL or LR
run = run_dict[splits[-2]] # Run number
entities = dict(
subject=subject,
datatype=modality_dict[modality],
direction=direction,
run=run,
extension=extension,
suffix="dwi",
)
if include_ses:
entities["session"] = "1"
new_fname = layout.build_path(entities, pattern)
Path(fname).rename(new_fname)
# Make json sidecar
if extension == ".nii.gz":
entities["extension"] = ".json"
if direction == "LR":
phase = "i-"
elif direction == "RL":
phase = "i"
# TotalReadoutTime = EffectiveEchoSpacing * (EPI factor - 1) (which is 144)
sidecar = dict(
EffectiveEchoSpacing=0.00078,
TotalReadoutTime=0.11154,
PhaseEncodingDirection=phase,
)
with open(layout.build_path(entities, pattern),
"w") as f:
json.dump(sidecar, f)
# Remove all folders
modality_folder.rmdir()
for folder in list(subject_folder.rglob("*"))[::-1]:
folder.rmdir()
subject_folder.rmdir()
if not input_path == output_path:
in_path.rmdir()
if acquisition:
acquisitionValue = acquisition[0].split('-')[1]
entities['acquisition'] = acquisitionValue
else:
entities.pop('acquisition', None)
run = list(filter(lambda x: "run-" in x, labels))
if run:
runValue = run[0].split('-')[1]
entities['run'] = runValue
else:
entities.pop('run', None)
entities['extension'] = 'nii.gz'
outputfile = os.path.join(niftidir,
layout.build_path(entities))
if os.path.exists(sourcefile):
logtext(
LOGFILE,
"copying %s to %s" % (sourcefile, outputfile))
subprocess.check_output(
['cp', sourcefile, outputfile])
else:
logtext(
LOGFILE,
"ERROR: %s cannot be found. Check bidsaction file logic."
% sourcefile)
entities['extension'] = 'json'
outputjson = os.path.join(niftidir,
layout.build_path(entities))
def main():
parser = ArgumentParser(
description="Defaces anatomical data in a BIDS dataset",
formatter_class=ArgumentDefaultsHelpFormatter,
)
g_required = parser.add_mutually_exclusive_group(required=True)
g_required.add_argument("--study",
action="store",
help="Nickname of the study to process")
g_required.add_argument(
"--bids-dir",
action="store",
metavar="DIR",
type=lambda x: _is_dir(x, parser),
help="The root directory of the BIDS dataset to process",
)
g_bids = parser.add_argument_group("Options for filtering BIDS queries")
g_bids.add_argument(
"-s",
"--suffix-id",
action="store",
nargs="+",
default=["T1w"],
help="Select a specific BIDS suffix to be processed",
)
g_bids.add_argument(
"--skip-bids-validation",
action="store_true",
default=False,
help="Assume the input dataset is BIDS compatible and skip validation",
)
g_bids.add_argument(
"--bids-database-dir",
action="store",
type=lambda x: _is_dir(x, parser),
help="Path to a PyBIDS database directory for faster indexing",
)
g_perfm = parser.add_argument_group("Options for logging and debugging")
g_perfm.add_argument("--quiet",
action="store_true",
default=False,
help="Minimal logging")
g_perfm.add_argument("--verbose",
action="store_true",
default=False,
help="Maximal logging")
g_perfm.add_argument("--dry-run",
action="store_true",
default=False,
help="Do nothing")
args = parser.parse_args()
if args.verbose:
logger.setLevel(logging.INFO)
if args.quiet:
logger.setLevel(logging.ERROR)
if args.study:
config = datman.config.config(study=args.study)
bids_dir = config.get_path("bids")
else:
bids_dir = args.bids_dir
layout = BIDSLayout(
bids_dir,
validate=args.skip_bids_validation,
database_path=args.bids_database_dir,
)
anat_list = layout.get(suffix=args.suffix_id,
extension=[".nii", ".nii.gz"])
keys_to_extract = [
"subject",
"session",
"acquisition",
"ceagent",
"reconstruction",
"run",
"suffix",
]
for anat in anat_list:
entities = {
key: anat.entities.get(key, None)
for key in keys_to_extract
}
if (entities["acquisition"] is not None
and "defaced" in entities["acquisition"]):
continue
if entities["acquisition"] is not None:
entities["acquisition"] = entities["acquisition"] + "defaced"
else:
entities["acquisition"] = "defaced"
output_file = Path(bids_dir, layout.build_path(entities))
if not output_file.exists():
if args.dry_run:
logger.info(
f"DRYRUN would have executed defacing on <{anat.path}> "
f"and output to <{output_file}>")
continue
try:
deface_image(infile=anat.path, outfile=str(output_file))
except Exception as e:
logger.error(f"Defacing failed to run on <{anat.path}> for "
f"reason {e}")
return
anat_metadata = anat.get_metadata()
anat_metadata["DefaceSoftware"] = "pydeface"
with open(str(output_file).replace(".nii.gz", ".json"), "w+") as f:
json.dump(anat_metadata, f, indent=4)
