def rex_bids_csv(folder_path, to_save, ftype):
"""[CSV generation for BIDS datasets]
[This function is used to generate a csv for BIDS datasets]
Arguments:
folder_path {[string]} -- [Takes the folder to see where to look for
the different modaliies]
to_save {[string]} -- [Takes the folder as a string to save the csv]
ftype {[string]} -- [Are you trying to save train, validation or test,
if file type is set to test, it does not look for
ground truths]
"""
if ftype == "test":
csv_file = open(os.path.join(to_save, ftype + ".csv"), "w+")
csv_file.write("ID,")
else:
csv_file = open(os.path.join(to_save, ftype + ".csv"), "w+")
csv_file.write("ID,gt_path,")
# load BIDS dataset into memory
layout = BIDSLayout(folder_path)
bids_df = layout.to_df()
bids_modality_df = {
"t1": bids_df[bids_df["suffix"] == "T1w"],
"t2": bids_df[bids_df["suffix"] == "T2w"],
"flair": bids_df[bids_df["suffix"] == "FLAIR"],
"t1ce": bids_df[bids_df["suffix"] == "T1CE"],
}
# check what modalities the dataset contains
modalities = []
for modality, df in bids_modality_df.items():
if not df.empty:
modalities.append(modality)
# write headers for those modalities
for modality in modalities[:-1]:
csv_file.write(modality + "_path,")
modality = modalities[-1]
csv_file.write(modality + "_path\n")
# write image paths for each subject
for sub in layout.get_subjects():
csv_file.write(sub)
csv_file.write(",")
if ftype != "test":
ground_truth = glob.glob(
os.path.join(folder_path, sub, "*mask.nii.gz"))[0]
csv_file.write(ground_truth)
csv_file.write(",")
for modality in modalities[:-1]:
img = bids_modality_df[modality][bids_df["subject"] ==
sub].path.values
csv_file.write(img[0])
csv_file.write(",")
modality = modalities[-1]
img = bids_modality_df[modality][bids_df["subject"] == sub].path.values
csv_file.write(img[0])
csv_file.write("\n")
csv_file.close()
def get_scan_duration(output_dir, modality="func", task="rest"):
layout = BIDSLayout(output_dir)
df = layout.to_df()
scans_df = df.query(
"datatype==@modality & task==@task & extension=='nii.gz'")
scan_durations = []
for file in scans_df.path:
scan_durations.append(layout.get_metadata(file)["ScanDurationSec"])
scans_df["scan_duration"] = scan_durations
scans_df.reset_index(drop=True, inplace=True)
return scans_df
def find_t1_in_bids(root_path, subject, session):
'''
use pybids to make the pybids object and get the T1w
'''
subject = str(subject.replace('sub-', ''))
session = str(session.replace('ses-', ''))
data = BIDSLayout(root_path)
df = data.to_df()
t1 = df.loc[(df['subject'] == subject) & (df['session'] == session) &
(df['suffix'] == 'T1w') & (df['extension'] == 'nii.gz')]
assert t1.shape[0] == 1, "Couldn't find proper T1w file in BIDS!"
p = Path(t1.iloc[0]['path']).resolve()
return p.parent, p.name
def run_conversion(raw_dir,
output_base_dir,
analysis_level,
info_out_dir,
participant_label,
session_label,
public_output,
use_new_ids,
ds_version,
info_list,
dataset_description,
new_id_lut_file=None,
bvecs_from_scanner_file=None,
tp6_raw_lut=None,
dry_run=False,
demo_file=None,
session_duration_min=120):
# privacy settings
private_str = "_PRIVATE" if not (public_output and use_new_ids) else ""
output_dir = Path(
output_base_dir) / f"LHAB_{ds_version}{private_str}" / "sourcedata"
metainfo_dir = Path(
output_base_dir) / f"LHAB_{ds_version}{private_str}" / "metainfo"
metainfo_dir.mkdir(exist_ok=True, parents=True)
output_dir.mkdir(parents=True, exist_ok=True)
info_out_dir = Path(info_out_dir) / "PRIVATE"
info_out_dir.mkdir(parents=True, exist_ok=True)
if analysis_level == "participant":
for old_subject_id in participant_label:
submit_single_subject(
old_subject_id,
session_label,
raw_dir,
output_dir,
info_list,
info_out_dir,
bvecs_from_scanner_file=bvecs_from_scanner_file,
public_output=public_output,
use_new_ids=use_new_ids,
new_id_lut_file=new_id_lut_file,
tp6_raw_lut=tp6_raw_lut,
dry_run=dry_run,
session_duration_min=session_duration_min)
print("\n\n\n\nDONE.\nConverted %d subjects." % len(participant_label))
print(participant_label)
elif analysis_level == "group":
ds_desc_file = output_dir / "dataset_description.json"
if ds_desc_file.is_file():
ds_desc_file.unlink()
dataset_description["DataSetVersion"] = ds_version
add_info_to_json(ds_desc_file, dataset_description, create_new=True)
# Demos
print("Exporting demos...")
pwd = getpass.getpass("Enter the Password for dob file:")
calc_demos(output_dir,
info_out_dir,
demo_file,
pwd,
new_id_lut_file=new_id_lut_file)
# check for duplicates
mappings = concat_tsvs(info_out_dir / "parrec_mapping_PRIVATE")
dups = mappings[mappings.duplicated(subset="from")]
assert len(dups) == 0, print("duplicates found", dups)
# concat notconverted files
unconv_df = concat_tsvs(info_out_dir / "unconverted_files")
unconv_df.to_csv(info_out_dir / "unconverted_files.tsv",
sep="\t",
index=False)
print("X" * 20 + "\nRuning BIDS validator")
os.system(f"bids-validator {str(output_dir)}")
print("\n Get BIDS layout")
layout = BIDSLayout(output_dir)
layout.to_df().to_csv(metainfo_dir / "layout.csv", index=False)
else:
raise RuntimeError(f"Analysis level unknown {analysis_level}")
def main(subject, sourcedata, derivatives, smoothed, n_jobs=5):
os.environ['SUBJECTS_DIR'] = op.join(derivatives, 'freesurfer')
source_layout = BIDSLayout(sourcedata, validate=False, derivatives=False)
fmriprep_layout = BIDSLayout(op.join(derivatives, 'fmriprep'),
validate=False)
if smoothed:
bold_layout = BIDSLayout(op.join(derivatives, 'smoothed'),
validate=False)
bold = bold_layout.get(subject=subject, extension='func.gii')
else:
bold = fmriprep_layout.get(subject=subject, extension='func.gii')
bold = sorted([e for e in bold if 'fsaverage6' in e.filename],
key=lambda x: x.run)
fmriprep_layout_df = fmriprep_layout.to_df()
fmriprep_layout_df = fmriprep_layout_df[~fmriprep_layout_df.subject.isnull(
)]
fmriprep_layout_df['subject'] = fmriprep_layout_df.subject.astype(int)
fmriprep_layout_df = fmriprep_layout_df[np.in1d(fmriprep_layout_df.suffix,
['regressors'])]
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.extension, ['tsv'])]
fmriprep_layout_df = fmriprep_layout_df.set_index(['subject', 'run'])
events_df = source_layout.to_df()
events_df = events_df[events_df.suffix == 'events']
events_df['subject'] = events_df['subject'].astype(int)
events_df = events_df.set_index(['subject', 'run'])
tr = source_layout.get_tr(bold[0].path)
if smoothed:
base_dir = op.join(derivatives, 'glm_stim1_surf_smoothed',
f'sub-{subject}', 'func')
else:
base_dir = op.join(derivatives, 'glm_stim1_surf', f'sub-{subject}',
'func')
if not op.exists(base_dir):
os.makedirs(base_dir)
for b in bold:
run = b.entities['run']
hemi = b.entities['suffix']
# print(run)
confounds_ = fmriprep_layout_df.loc[(subject, run), 'path'].iloc[0]
confounds_ = pd.read_csv(confounds_, sep='\t')
confounds_ = confounds_[to_include].fillna(method='bfill')
pca = PCA(n_components=7)
confounds_ -= confounds_.mean(0)
confounds_ /= confounds_.std(0)
confounds_pca = pca.fit_transform(confounds_[to_include])
events_ = events_df.loc[(subject, run), 'path']
events_ = pd.read_csv(events_, sep='\t')
events_['trial_type'] = events_['trial_type'].apply(
lambda x: 'stim2' if x.startswith('stim2') else x)
frametimes = np.arange(0, tr * len(confounds_), tr)
X = make_first_level_design_matrix(
frametimes,
events_,
add_regs=confounds_pca,
add_reg_names=[f'confound_pca.{i}' for i in range(1, 8)])
Y = surface.load_surf_data(b.path).T
Y = (Y / Y.mean(0) * 100)
Y -= Y.mean(0)
fit = run_glm(Y, X, noise_model='ols', n_jobs=n_jobs)
r = fit[1][0.0]
betas = pd.DataFrame(r.theta, index=X.columns)
stim1 = []
for stim in 5, 7, 10, 14, 20, 28:
stim1.append(betas.loc[f'stim1-{stim}'])
result = pd.concat(stim1, 1).T
print(result.shape)
pes = nb.gifti.GiftiImage(header=nb.load(b.path).header,
darrays=[
nb.gifti.GiftiDataArray(row)
for ix, row in result.iterrows()
])
fn_template = op.join(
base_dir,
'sub-{subject}_run-{run}_space-{space}_desc-stims1_hemi-{hemi}.pe.gii'
)
space = 'fsaverage6'
pes.to_filename(fn_template.format(**locals()))
transformer = SurfaceTransform(source_subject='fsaverage6',
target_subject='fsaverage',
hemi={
'L': 'lh',
'R': 'rh'
}[hemi])
transformer.inputs.source_file = pes.get_filename()
space = 'fsaverage'
transformer.inputs.out_file = fn_template.format(**locals())
# Disable on MAC OS X (SIP problem)
transformer.run()
def from_bids(cls,
source_dir,
subject=None,
session=None,
acquisition=None,
run=None,
inversion_efficiency=0.96):
""" Creates a MEMP2RAGE-object from a properly organized BIDS-folder.
The folder should be organized similar to this example:
sub-01/anat/:
# The first inversion time volumes
* sub-01_inv-1_part-mag_MPRAGE.nii
* sub-01_inv-1_part-phase_MPRAGE.nii
# The four echoes of the second inversion (magnitude)
* sub-01_inv-2_part-mag_echo-1_MPRAGE.nii
* sub-01_inv-2_part-mag_echo-2_MPRAGE.nii
* sub-01_inv-2_part-mag_echo-3_MPRAGE.nii
* sub-01_inv-2_part-mag_echo-4_MPRAGE.nii
# The four echoes of the second inversion (phase)
* sub-01_inv-2_part-phase_echo-1_MPRAGE.nii
* sub-01_inv-2_part-phase_echo-2_MPRAGE.nii
* sub-01_inv-2_part-phase_echo-3_MPRAGE.nii
* sub-01_inv-2_part-phase_echo-4_MPRAGE.nii
# The json describing the parameters of the first inversion pulse
* sub-01_inv-1_MPRAGE.json
# The json describing the parameters of the second inversion pulse
* sub-01_inv-2_echo-1_MPRAGE.json
* sub-01_inv-2_echo-2_MPRAGE.json
* sub-01_inv-2_echo-3_MPRAGE.json
* sub-01_inv-2_echo-4_MPRAGE.json
The JSON-files should contain all the necessary MP2RAGE sequence parameters
and should look something like this:
sub-01/anat/sub-01_inv-1_MPRAGE.json:
{
"InversionTime":0.67,
"FlipAngle":7,
"RepetitionTimeExcitation":0.0062,
"RepetitionTimePreparation":6.723,
"NumberShots":150,
"FieldStrength": 7
}
sub-01/anat/sub-01_inv-2_echo-1_MPRAGE.json:
{
"InversionTime":3.855,
"FlipAngle":6,
"RepetitionTimeExcitation":0.0320,
"RepetitionTimePreparation":6.723,
"NumberShots":150,
"EchoTime": 6.0
"FieldStrength": 7
}
sub-01/anat/sub-01_inv-2_echo-2_MPRAGE.json:
{
"InversionTime":3.855,
"FlipAngle":6,
"RepetitionTimeExcitation":0.0320,
"RepetitionTimePreparation":6.723,
"NumberShots":150,
"EchoTime": 14.5
"FieldStrength": 7
}
sub-01/anat/sub-01_inv-2_echo-3_MPRAGE.json:
{
"InversionTime":3.855,
"FlipAngle":6,
"RepetitionTimeExcitation":0.0320,
"RepetitionTimePreparation":6.723,
"NumberShots":150,
"EchoTime": 23
"FieldStrength": 7
}
sub-01/anat/sub-01_inv-2_echo-4_MPRAGE.json:
{
"InversionTime":3.855,
"FlipAngle":6,
"RepetitionTimeExcitation":0.0320,
"RepetitionTimePreparation":6.723,
"NumberShots":150,
"EchoTime": 31.5
"FieldStrength": 7
}
A MEMP2RAGE-object can now be created from the BIDS folder as follows:
Example:
>>> import pymp2rage
>>> mp2rage = pymp2rage.MEMP2RAGE.from_bids('/data/sourcedata/', '01')
Args:
source_dir (BIDS dir): directory containing all necessary files
subject (str): subject identifier
**kwargs: additional keywords that are forwarded to get-function of
BIDSLayout. For example `ses` could be used to select specific session.
"""
__dir__ = os.path.abspath(os.path.dirname(__file__))
layout = BIDSLayout(source_dir,
validate=False,
config=op.join(__dir__, 'bids', 'bep001.json'))
df = layout.to_df()
subject = str(subject) if subject is not None else subject
session = str(session) if session is not None else session
run = int(run) if run is not None else run
for var_str, var in zip(['subject', 'session', 'run', 'acquisition'],
[subject, session, run, acquisition]):
if var is not None:
df = df[df[var_str] == var]
df = df[np.in1d(df.extension, ['nii', 'nii.gz'])]
for key in ['echo', 'inv', 'fa']:
if key in df.columns:
df[key] = df[key].astype(float)
df = df.set_index(['suffix', 'inv', 'echo', 'part'])
df = df.loc[['MP2RAGE', 'TB1map']]
for ix, row in df.iterrows():
for key, value in layout.get_metadata(row.path).items():
if key in [
'EchoTime', 'InversionTime',
'RepetitionTimePreparation',
'RepetitionTimeExcitation', 'NumberShots',
'FieldStrength', 'FlipAngle'
]:
df.loc[ix, key] = value
if 'TB1map' in df.index:
if len(df.loc['TB1map']) == 1:
print('using {} as B1map'.format(
str(df.loc['TB1map'].iloc[0]['path'])))
b1map = df.loc['TB1map'].iloc[0]['path']
else:
print('FOUND MORE THAN ONE B1-MAP! Will not use B1-correction')
b1map = None
else:
b1map = None
inv1 = df.loc[('MP2RAGE', 1, slice(None), 'mag'), 'path'].iloc[0]
inv1ph = df.loc[('MP2RAGE', 1, slice(None), 'phase'), 'path'].iloc[0]
inv2 = df.loc[('MP2RAGE', 2, slice(None), 'mag'), 'path'].tolist()
inv2ph = df.loc[('MP2RAGE', 2, slice(None), 'phase'), 'path'].tolist()
echo_times = df.loc[('MP2RAGE', 2, slice(None), 'mag'),
'EchoTime'].values
MPRAGE_tr = df.loc[('MP2RAGE', 1, slice(None), 'mag'),
'RepetitionTimePreparation'].values[0]
invtimesAB = df.loc[('MP2RAGE', 1, slice(None), 'mag'),
'InversionTime'].values[0], df.loc[(
'MP2RAGE', 2, slice(None),
'mag'), 'InversionTime'].values[0],
nZslices = df.loc[('MP2RAGE', 1, slice(None), 'mag'),
'NumberShots'].values[0]
FLASH_tr = df.loc[('MP2RAGE', 1, slice(None), 'mag'),
'RepetitionTimeExcitation'].values[0], df.loc[(
'MP2RAGE', 2, slice(None),
'mag'), 'RepetitionTimeExcitation'].values[0]
B0 = df.loc[('MP2RAGE', 1, slice(None), 'mag'),
'FieldStrength'].values[0]
flipangleABdegree = df.loc[('MP2RAGE', 1, slice(None), 'mag'),
'FlipAngle'].values[0], df.loc[(
'MP2RAGE', 2, slice(None),
'mag'), 'FlipAngle'].values[0]
mp2rageme = cls(echo_times=echo_times,
MPRAGE_tr=MPRAGE_tr,
invtimesAB=invtimesAB,
flipangleABdegree=flipangleABdegree,
nZslices=nZslices,
FLASH_tr=FLASH_tr,
inversion_efficiency=inversion_efficiency,
B0=B0,
inv1=inv1,
inv1ph=inv1ph,
inv2=inv2,
inv2ph=inv2ph)
return mp2rageme
def main(subject, sourcedata, derivatives):
source_layout = BIDSLayout(sourcedata, validate=False, derivatives=False)
fmriprep_layout = BIDSLayout(op.join(derivatives, 'fmriprep'),
validate=False)
bold = fmriprep_layout.get(
subject=subject,
suffix='bold',
description='preproc',
extension='nii.gz',
)
bold = sorted([e for e in bold if 'MNI' in e.filename],
key=lambda x: x.run)
reg = re.compile('.*_space-(?P<space>.+)_desc.*')
fmriprep_layout_df = fmriprep_layout.to_df()
fmriprep_layout_df = fmriprep_layout_df[~fmriprep_layout_df.subject.isnull(
)]
fmriprep_layout_df['subject'] = fmriprep_layout_df.subject.astype(int)
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.suffix, ['bold', 'regressors', 'mask'])]
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.extension, ['nii.gz', 'tsv'])]
fmriprep_layout_df['space'] = fmriprep_layout_df.path.apply(
lambda path: reg.match(path).group(1) if reg.match(path) else None)
fmriprep_layout_df = fmriprep_layout_df.set_index(
['subject', 'run', 'suffix', 'space'])
events_df = source_layout.to_df()
events_df = events_df[events_df.suffix == 'events']
events_df['subject'] = events_df['subject'].astype(int)
events_df = events_df.set_index(['subject', 'run'])
tr = source_layout.get_tr(bold[0].path)
for b in bold:
run = b.entities['run']
print(run)
confounds_ = fmriprep_layout_df.loc[(subject, run, 'regressors'),
'path'].iloc[0]
confounds_ = pd.read_csv(confounds_, sep='\t')
confounds_ = confounds_[to_include].fillna(method='bfill')
events_ = events_df.loc[(subject, run), 'path']
events_ = pd.read_csv(events_, sep='\t')
events_['trial_type'] = events_['trial_type'].apply(
lambda x: 'stim2' if x.startswith('stim2') else x)
model = FirstLevelModel(tr,
drift_model=None,
n_jobs=5,
smoothing_fwhm=4.0)
pca = PCA(n_components=7)
confounds_ -= confounds_.mean(0)
confounds_ /= confounds_.std(0)
confounds_pca = pca.fit_transform(confounds_[to_include])
events_['onset'] += tr
model.fit(b.path, events_, confounds_pca)
base_dir = op.join(derivatives, 'glm_stim1', f'sub-{subject}', 'func')
if not op.exists(base_dir):
os.makedirs(base_dir)
# PE
ims = []
for stim in 5, 7, 10, 14, 20, 28:
im = model.compute_contrast(f'stim1-{stim}',
output_type='effect_size')
ims.append(im)
ims = image.concat_imgs(ims)
ims.to_filename(
op.join(base_dir,
f'sub-{subject}_run-{run}_desc-stims1_pe.nii.gz'))
# zmap
ims = []
for stim in 5, 7, 10, 14, 20, 28:
im = model.compute_contrast(f'stim1-{stim}', output_type='z_score')
ims.append(im)
ims = image.concat_imgs(ims)
ims.to_filename(
op.join(base_dir,
f'sub-{subject}_run-{run}_desc-stims1_zmap.nii.gz'))
# In[19]: layout.get(task='localizer', suffix='bold', scope='raw')[:10] # Notice that there are nifti and event files. We can get the filename for the first particant's functional run # In[140]: f = layout.get(task='localizer')[0].filename f # If you want a summary of all the files in your BIDSLayout, but don't want to have to iterate BIDSFile objects and extract their entities, you can get a nice bird's-eye view of your dataset using the `to_df()` method. # In[20]: layout.to_df() # ## Loading Data with Nibabel # Neuroimaging data is often stored in the format of nifti files `.nii` which can also be compressed using gzip `.nii.gz`. These files store both 3D and 4D data and also contain structured metadata in the image **header**. # # There is an very nice tool to access nifti data stored on your file system in python called [nibabel](http://nipy.org/nibabel/). If you don't already have nibabel installed on your computer it is easy via `pip`. First, tell the jupyter cell that you would like to access the unix system outside of the notebook and then install nibabel using pip `!pip install nibabel`. You only need to run this once (unless you would like to update the version). # # nibabel objects can be initialized by simply pointing to a nifti file even if it is compressed through gzip. First, we will import the nibabel module as `nib` (short and sweet so that we don't have to type so much when using the tool). I'm also including a path to where the data file is located so that I don't have to constantly type this. It is easy to change this on your own computer. # # We will be loading an anatomical image from subject S01 from the localizer [dataset](../content/Download_Data). See this [paper](https://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-8-91) for more information about this dataset. # # We will use pybids to grab subject S01's T1 image. # In[26]: import nibabel as nib
def main(subject,
sourcedata,
derivatives):
source_layout = BIDSLayout(sourcedata, validate=False, derivatives=False)
fmriprep_layout = BIDSLayout(
op.join(derivatives, 'fmriprep'), validate=False)
bold = fmriprep_layout.get(subject=subject,
suffix='bold',
description='preproc',
extension='nii.gz', )
bold = sorted([e for e in bold if 'MNI' in e.filename],
key=lambda x: x.run)
reg = re.compile('.*_space-(?P<space>.+)_desc.*')
fmriprep_layout_df = fmriprep_layout.to_df()
fmriprep_layout_df = fmriprep_layout_df[~fmriprep_layout_df.subject.isnull(
)]
fmriprep_layout_df['subject'] = fmriprep_layout_df.subject.astype(int)
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.suffix, ['bold', 'regressors', 'mask'])]
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.extension, ['nii.gz', 'tsv'])]
fmriprep_layout_df['space'] = fmriprep_layout_df.path.apply(
lambda path: reg.match(path).group(1) if reg.match(path) else None)
fmriprep_layout_df = fmriprep_layout_df.set_index(
['subject', 'run', 'suffix', 'space'])
events_df = source_layout.to_df()
events_df = events_df[events_df.suffix == 'events']
events_df['subject'] = events_df['subject'].astype(int)
events_df = events_df.set_index(['subject', 'run'])
tr = source_layout.get_tr(bold[0].path)
imgs = []
confounds = []
events = []
for b in bold:
run = b.entities['run']
confounds_ = fmriprep_layout_df.loc[(
subject, run, 'regressors'), 'path'].iloc[0]
confounds_ = pd.read_csv(confounds_, sep='\t')
events_ = events_df.loc[(subject, run), 'path']
events_ = pd.read_csv(events_, sep='\t')
events_['trial_type'] = events_['trial_type'].apply(
lambda x: 'stim2' if x.startswith('stim2') else x)
imgs.append(b.path)
confounds.append(confounds_[to_include].fillna(method='bfill'))
events.append(events_)
mask = fmriprep_layout.get(subject=subject,
run=1,
suffix='mask',
extension='nii.gz',
description='brain')
mask = [e for e in mask if 'MNI' in e.filename]
assert(len(mask) == 1)
mask = mask[0].path
model = FirstLevelModel(tr, drift_model=None, n_jobs=5, smoothing_fwhm=6.0)
model.fit(imgs, events, confounds, )
base_dir = op.join(derivatives, 'glm', f'sub-{subject}', 'func')
if not op.exists(base_dir):
os.makedirs(base_dir)
contrasts = [('left-right', 'leftright'), ('error', 'error'),
('stim1-10 + stim1-14 + stim1-20 + stim1-28 + stim1-5 + stim1-7', 'stim1'),
('stim2', 'stim2')]
for contrast, label in contrasts:
con = model.compute_contrast(contrast, output_type='z_score')
pe = model.compute_contrast(contrast, output_type='effect_size')
con.to_filename(op.join(base_dir, f'sub-{subject}_contrast-{label}_zmap.nii.gz'))
pe.to_filename(op.join(base_dir, f'sub-{subject}_contrast-{label}_pe.nii.gz'))
def main():
# Init
parser = get_parser()
args = parser.parse_args()
warnings.filterwarnings("ignore", category=FutureWarning)
if args.api_key:
client = flywheel.Client(args.api_key)
else:
client = flywheel.Client()
assert client, "Your Flywheel CLI credentials aren't set!"
if args.verbose:
logger.info("Scanning BIDS directory for data...")
local_bids = BIDSLayout(args.directory)
local_bids_df = local_bids.to_df()
local_t1s = (local_bids_df.loc[(local_bids_df['suffix'] == 'T1w')
& (local_bids_df['extension'] == 'nii.gz')])
assert local_t1s.shape[0] >= 1, logger.error(
"No T1w files found in dataset!")
if args.verbose:
logger.info("Found the following subjects:\n{}".format(' '.join(
local_t1s.subject.values)))
if args.verbose:
logger.info("Finding matching subjects on Flywheel...")
for i, row in local_t1s.iterrows():
print("\n")
row_processed = False
flywheel_bids = FlyBIDSLayout(args.project,
str('sub-' + row['subject']))
flywheel_bids_df = flywheel_bids.to_df()
flywheel_t1s = (flywheel_bids_df.loc[
(flywheel_bids_df['Filename'].str.contains('T1w'))
& (flywheel_bids_df['Filename'].str.contains('.nii.gz'))])
target_filename = Path(row['path']).name
mask = flywheel_t1s['Filename'].str.contains(target_filename)
file_exists = mask.any()
if file_exists:
if args.verbose:
logger.info(
"Matching file {} found on Flywheel. Refacing local T1w..."
.format(row['path']))
path_to_t1 = Path(row['path']).resolve().parent
input_filename = Path(row['path']).resolve().name
refaced_file, status = run_afni(str(path_to_t1),
str(input_filename),
args.rec,
dry_run=args.dry_run,
verbose=args.verbose)
if args.verbose:
logger.info("Replacing T1w with newly refaced version...")
row_processed = replace_t1w(client,
row,
flywheel_t1s[mask],
args.rec,
dry_run=args.dry_run,
verbose=args.verbose,
delete=args.delete)
if not row_processed:
logger.error("Subject {} not processed!".format(row['subject']))
Suppose we were interested in getting a list of tasks included in the dataset. layout.get_task() We can query all of the files associated with this task. layout.get(task='localizer', suffix='bold', scope='raw')[:10] Notice that there are nifti and event files. We can get the filename for the first particant's functional run f = layout.get(task='localizer')[0].filename f If you want a summary of all the files in your BIDSLayout, but don't want to have to iterate BIDSFile objects and extract their entities, you can get a nice bird's-eye view of your dataset using the `to_df()` method. layout.to_df() ## Loading Data with Nibabel Neuroimaging data is often stored in the format of nifti files `.nii` which can also be compressed using gzip `.nii.gz`. These files store both 3D and 4D data and also contain structured metadata in the image **header**. There is an very nice tool to access nifti data stored on your file system in python called [nibabel](http://nipy.org/nibabel/). If you don't already have nibabel installed on your computer it is easy via `pip`. First, tell the jupyter cell that you would like to access the unix system outside of the notebook and then install nibabel using pip `!pip install nibabel`. You only need to run this once (unless you would like to update the version). nibabel objects can be initialized by simply pointing to a nifti file even if it is compressed through gzip. First, we will import the nibabel module as `nib` (short and sweet so that we don't have to type so much when using the tool). I'm also including a path to where the data file is located so that I don't have to constantly type this. It is easy to change this on your own computer. We will be loading an anatomical image from subject S01 from the localizer [dataset](http://brainomics.cea.fr/localizer/). See this [paper](https://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-8-91) for more information about this dataset. We will use pybids to grab subject S01's T1 image. import nibabel as nib
def main(subject,
sourcedata,
derivatives):
source_layout = BIDSLayout(sourcedata, validate=False, derivatives=False)
fmriprep_layout = BIDSLayout(
op.join(derivatives, 'fmriprep'), validate=False)
bold = fmriprep_layout.get(subject=subject,
extension='func.gii')
bold = sorted([e for e in bold if 'fsaverage6' in e.filename],
key=lambda x: x.run)
reg = re.compile('.*_space-(?P<space>.+)_desc.*')
fmriprep_layout_df = fmriprep_layout.to_df()
fmriprep_layout_df = fmriprep_layout_df[~fmriprep_layout_df.subject.isnull(
)]
fmriprep_layout_df['subject'] = fmriprep_layout_df.subject.astype(int)
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.suffix, ['regressors'])]
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.extension, ['tsv'])]
fmriprep_layout_df = fmriprep_layout_df.set_index(
['subject', 'run'])
events_df = source_layout.to_df()
events_df = events_df[events_df.suffix == 'events']
events_df['subject'] = events_df['subject'].astype(int)
events_df = events_df.set_index(['subject', 'run'])
tr = source_layout.get_tr(bold[0].path)
base_dir = op.join(derivatives, 'glm_stim1_trialwise_surf', f'sub-{subject}', 'func')
if not op.exists(base_dir):
os.makedirs(base_dir)
for b in bold:
run = b.entities['run']
hemi = b.entities['suffix']
# print(run)
confounds_ = fmriprep_layout_df.loc[(
subject, run), 'path'].iloc[0]
confounds_ = pd.read_csv(confounds_, sep='\t')
confounds_ = confounds_[to_include].fillna(method='bfill')
events_ = events_df.loc[(subject, run), 'path']
events_ = pd.read_csv(events_, sep='\t')
events_['trial_type'] = events_['trial_type'].apply(
lambda x: 'stim2' if x.startswith('stim2') else x)
events_['onset'] += tr
# Split up over trials
stim1_events = events_[events_.trial_type.apply(lambda x: x.startswith('stim1'))]
def number_trials(d):
return pd.Series(['{}.{}'.format(e, i+1) for i, e in enumerate(d)],
index=d.index)
stim1_events['trial_type'] = stim1_events.groupby('trial_type').trial_type.apply(number_trials)
events_.loc[stim1_events.index, 'trial_type'] = stim1_events['trial_type']
frametimes = np.arange(0, tr*len(confounds_), tr)
pca = PCA(n_components=7)
confounds_ -= confounds_.mean(0)
confounds_ /= confounds_.std(0)
confounds_pca = pca.fit_transform(confounds_[to_include])
X = make_first_level_design_matrix(frametimes,
events_,
add_regs=confounds_pca.values)
Y = surface.load_surf_data(b.path).T
Y = (Y / Y.mean(0) * 100)
Y -= Y.mean(0)
fit = run_glm(Y, X, noise_model='ols')
r = fit[1][0.0]
betas = pd.DataFrame(r.theta, index=X.columns)
stim1 = []
for stim in 5, 7, 10, 14, 20, 28:
for trial in range(1, 7):
stim1.append(betas.loc[f'stim1-{stim}.{trial}'])
result = pd.concat(stim1, 1).T
pes = nb.gifti.GiftiImage(header=nb.load(b.path).header,
darrays=[nb.gifti.GiftiDataArray(result)])
pes.to_filename(
op.join(base_dir,
f'sub-{subject}_run-{run}_desc-stims1_hemi-{hemi}.pe.gii'))
