def main(sourcedata, derivatives, subject, session, tmp_dir):
sourcedata_layout = BIDSLayout(sourcedata)
sourcedata_df = sourcedata_layout.as_data_frame()
events = sourcedata_df[(sourcedata_df['type'] == 'events')
& (sourcedata_df['subject'] == subject) &
(sourcedata_df['session'] == session)]
derivatives_layout = BIDSLayout(os.path.join(derivatives, 'spynoza'))
derivatives_df = derivatives_layout.as_data_frame()
bold = derivatives_df[(derivatives_df['type'] == 'preproc')
& (derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)]
mask = derivatives_layout.get(subject=subject,
session=session,
type='mask',
return_type='file')[0]
mask = image.math_img('(im > .5).astype(int)', im=mask)
print(mask)
row = bold.iloc[0]
results_dir = os.path.join(derivatives, 'modelfitting_av', 'glm2',
'sub-{}'.format(row['subject']))
os.makedirs(results_dir, exist_ok=True)
av_bold_fn = os.path.join(
results_dir,
'sub-{}_ses-{}_bold_average.nii.gz'.format(row['subject'],
row['session']))
av_bold = average_over_runs(bold.path.tolist(), output_filename=av_bold_fn)
av_bold = image.math_img('(av_bold / av_bold.mean(-1)[..., np.newaxis])',
av_bold=av_bold)
av_bold.to_filename(av_bold_fn)
model = FirstLevelModel(t_r=4, mask=mask, drift_model=None)
paradigm = pd.read_table(events.iloc[0]['path'])
model.fit(av_bold, paradigm)
left_right = model.compute_contrast('eye_L - eye_R', output_type='z_score')
left_right.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_left_over_right_zmap.nii.gz'.format(
row['subject'],
row['session'],
)))
left_right = model.compute_contrast('eye_L - eye_R',
output_type='effect_size')
left_right.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_left_over_right_psc.nii.gz'.format(
row['subject'], row['session'])))
def get_T1w(fpath):
"""
Returns list of anatomical (T1w) files from ``fpath``
Parameters
----------
fpath : str
Filepath to BIDS dataset (or loaded BIDSLayout object)
Returns
-------
imgs : list-of-str
Filepaths of T1w images
"""
# exclude everything that isn't the subject data itself
data = BIDSLayout(op.abspath(fpath), exclude=EXCLUDE)
# TODO: it'd be great to do this without needing pandas...
t1s = data.as_data_frame(modality='anat', type='T1w', extensions='.nii.gz')
# try to get most "minimal" (i.e., fewest tags) T1w image for each subject
cols = [f for f in TAGS if f in t1s.columns]
if len(cols) > 0:
t1s = t1s.sort_values(cols, na_position='first').reset_index(drop=True)
imgs = t1s.groupby('subject').nth(0).get('path').tolist()
if len(imgs) == 0:
raise ValueError(f'There are no viable T1w images in {fpath}. '
'Check inputs and try again.')
return imgs
def prepare_bids_df(sourcedata_dir, subject_ids=None, session_ids=None):
layout = BIDSLayout(sourcedata_dir)
bids_df = layout.as_data_frame()
bids_df.dropna(subset=["subject"], inplace=True) # remove study level info
nii_df = bids_df[bids_df.path.str.endswith(NII_EXT)]
nii_df = nii_df.sort_values(
by=["subject", "session", "modality", "type", "run"])
# reduce to requested sessions
if subject_ids:
nii_df = nii_df[nii_df.subject.isin(subject_ids)]
if session_ids:
nii_df = nii_df[nii_df.session.isin(session_ids)]
return nii_df
def drp_seed_fc():
import numpy as np
from os import path
#from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
from samri.utilities import bids_substitution_iterator
from samri.analysis import fc
from samri.utilities import N_PROCS
N_PROCS = max(N_PROCS - 8, 2)
from bids.grabbids import BIDSLayout
from bids.grabbids import BIDSValidator
import os
base = '~/ni_data/ofM.dr/bids/preprocessing/generic/'
base = os.path.abspath(os.path.expanduser(base))
validate = BIDSValidator()
for x in os.walk(base):
print(x[0])
print(validate.is_bids(x[0]))
layout = BIDSLayout(base)
df = layout.as_data_frame()
df = df[df.type.isin(['cbv'])]
print(df)
#substitutions = bids_substitution_iterator(
# list(df['session'].unique()),
# all_subjects,
# ["CogB",],
# "~/ni_data/ofM.dr/",
# "composite",
# acquisitions=["EPI",],
# check_file_format='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz')
#substitutions = df.T.to_dict().values()[:2]
substitutions = df.T.to_dict().values()
print(substitutions)
fc_results = fc.seed_based(
substitutions,
"~/ni_data/templates/roi/DSURQEc_drp.nii.gz",
"/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
ts_file_template=
'~/ni_data/ofM.dr/bids/preprocessing/generic/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz',
save_results=
"~/ni_data/ofM.dr/bids/fc/DSURQEc_drp/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz",
n_procs=N_PROCS,
cachedir='/mnt/data/joblib')
def get_bids_df(bids_dir, scans_only=None, keep_defaced=False):
if isinstance(bids_dir, Path):
bids_dir = bids_dir.as_posix()
layout = BIDSLayout(bids_dir)
df_pybids = layout.as_data_frame()
if not keep_defaced:
df_pybids = df_pybids.query('~path.str.contains("defaced")')
if scans_only:
df_pybids = (df_pybids.loc[df_pybids.path.str.contains('nii.gz'), :].
query("~path.str.contains('.git')"))
df_pybids['json_path'] = \
(df_pybids.path.apply(
lambda x: Path(''.join([*x.split('.')[:-2], '.json']))))
return df_pybids
def drp_seed_fc():
import numpy as np
from os import path
#from labbookdb.report.tracking import treatment_group, append_external_identifiers
from samri.plotting.overview import multiplot_matrix, multipage_plot
from samri.utilities import bids_substitution_iterator
from samri.analysis import fc
from samri.utilities import N_PROCS
N_PROCS=max(N_PROCS-8, 2)
from bids.grabbids import BIDSLayout
from bids.grabbids import BIDSValidator
import os
base = '~/ni_data/ofM.dr/bids/preprocessing/generic/'
base = os.path.abspath(os.path.expanduser(base))
validate = BIDSValidator()
for x in os.walk(base):
print(x[0])
print(validate.is_bids(x[0]))
layout = BIDSLayout(base)
df = layout.as_data_frame()
df = df[df.type.isin(['cbv'])]
print(df)
#substitutions = bids_substitution_iterator(
# list(df['session'].unique()),
# all_subjects,
# ["CogB",],
# "~/ni_data/ofM.dr/",
# "composite",
# acquisitions=["EPI",],
# check_file_format='~/ni_data/ofM.dr/preprocessing/{preprocessing_dir}/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz')
#substitutions = df.T.to_dict().values()[:2]
substitutions = df.T.to_dict().values()
print(substitutions)
fc_results = fc.seed_based(substitutions, "~/ni_data/templates/roi/DSURQEc_drp.nii.gz", "/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
ts_file_template='~/ni_data/ofM.dr/bids/preprocessing/generic/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv.nii.gz',
save_results="~/ni_data/ofM.dr/bids/fc/DSURQEc_drp/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_zstat.nii.gz",
n_procs=N_PROCS,
cachedir='/mnt/data/joblib')
def check_fs_subjects(sourcedata_dir, fs_dir):
"checks that for every subject and session with T1w there is a finished cross and long fs subject"
from bids.grabbids import BIDSLayout
layout = BIDSLayout(sourcedata_dir)
df = layout.as_data_frame()
df = df[["subject", "session", "type"]].drop_duplicates().query("type=='T1w'")
df.reset_index(inplace=True, drop=True)
# fs
os.chdir(fs_dir)
df["cross"] = None
df["long"] = None
for r in df.itertuples(index=True):
print(r)
cross = True if glob("sub-{s}_ses-{ses}/scripts/recon-all.done".format(s=r.subject, ses=r.session)) else False
long = True if glob(
"sub-{s}_ses-{ses}.long.sub-*/scripts/recon-all.done".format(s=r.subject, ses=r.session)) else False
df.loc[r.Index, "cross"] = cross
df.loc[r.Index, "long"] = long
missing = df[df.cross == False]
raise_ex = False
if len(missing) > 0:
print("*** CROSS missing for {} entries".format(len(missing)))
print(missing)
raise_ex = True
missing = df[df.long == False]
if len(missing) > 0:
print("*** LONG missing for {} entries".format(len(missing)))
print(missing)
raise_ex = True
print("*** Found {} cross and {} long sessions from {} subjects".format(df.cross.sum(), df.long.sum(),
len(df.subject.unique())))
if raise_ex:
raise Exception("SOME FS SUBJECTS ARE MISSING")
else:
print("Everything looks good")
def bids_data_selection(
base,
structural_match,
functional_match,
subjects,
sessions,
verbose=False,
joint_conditions=True,
):
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
layout = BIDSLayout(base)
df = layout.as_data_frame()
# drop event files
df = df[df.type != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
df.loc[df.modality == 'func',
'task'] = df.path.str.partition('task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func',
'scan_type'] = 'task-' + df['task'] + '_acq-' + df['acq']
df.loc[df.modality == 'anat',
'scan_type'] = 'acq-' + df['acq'] + '_' + df['type']
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if (subjects):
df = filter_data(df, 'subject', subjects)
if (sessions):
df = filter_data(df, 'session', sessions)
return df
def main(sourcedata, derivatives, subject, session, tmp_dir):
sourcedata_layout = BIDSLayout(sourcedata)
sourcedata_df = sourcedata_layout.as_data_frame()
events = sourcedata_df[(sourcedata_df['type'] == 'events')
& (sourcedata_df['subject'] == subject) &
(sourcedata_df['session'] == session)]
derivatives_layout = BIDSLayout(
os.path.join(derivatives, 'spynoza_mc_mutualinfo'))
derivatives_df = derivatives_layout.as_data_frame()
bold = derivatives_df[(derivatives_df['type'] == 'preproc')
& (derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)]
confounds = derivatives_df[(derivatives_df['type'] == 'confounds')
& (derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)]
print(derivatives_df.type.unique())
mask = derivatives_layout.get(subject=subject,
session=session,
type='mask',
return_type='file')[0]
df = events.merge(bold,
on=['subject', 'session', 'run'],
suffixes=('_events', '_bold'))
confounds = confounds.rename(columns={'path': 'confounds'})
df = df.merge(confounds[['subject', 'session', 'run', 'confounds']])
models = []
for ix, row in df.iterrows():
results_dir = os.path.join(derivatives, 'modelfitting', 'glm4',
'sub-{}'.format(row['subject']))
if 'session' in row:
results_dir = os.path.join(results_dir,
'ses-{}'.format(row['session']))
os.makedirs(results_dir, exist_ok=True)
confounds = pd.read_table(row.confounds).fillna(method='bfill')
print('Fitting {}'.format(row['path_bold']))
model = FirstLevelModel(t_r=4, mask=mask)
paradigm = pd.read_table(row['path_events'])
model.fit(row['path_bold'], paradigm, confounds=confounds)
left_right = model.compute_contrast('eye_L - eye_R',
output_type='z_score')
left_right.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_left_over_right_zmap.nii.gz'.format(
row['subject'], row['session'], row['run'])))
left_right = model.compute_contrast('eye_L - eye_R',
output_type='effect_size')
left_right.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_left_over_right_psc.nii.gz'.format(
row['subject'], row['session'], row['run'])))
models.append(model)
second_level_model = SecondLevelModel(mask=mask)
second_level_model.fit(models)
left_right_group = second_level_model.compute_contrast(
first_level_contrast='eye_L - eye_R', output_type='z_score')
left_right_group.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_left_over_right_zmap.nii.gz'.format(
row['subject'], row['session'])))
left_right_group = second_level_model.compute_contrast(
first_level_contrast='eye_L - eye_R', output_type='effect_size')
left_right_group.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_left_over_right_effect_size.nii.gz'.format(
row['subject'], row['session'])))
left_right_group = second_level_model.compute_contrast(
first_level_contrast='eye_L - eye_R', output_type='z_score')
left_right_group.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_left_over_right_zmap.nii.gz'.format(
row['subject'], row['session'])))
def bids_autograb(bids_dir):
bids_dir = path.abspath(path.expanduser(bids_dir))
validate = BIDSValidator()
layout = BIDSLayout(bids_dir)
df = layout.as_data_frame()
return df
def main(sourcedata, derivatives, subject, session, tmp_dir):
sourcedata_layout = BIDSLayout(sourcedata)
sourcedata_df = sourcedata_layout.as_data_frame()
events = sourcedata_df[(sourcedata_df['type'] == 'events')
& (sourcedata_df['subject'] == subject) &
(sourcedata_df['session'] == session)]
derivatives_layout = BIDSLayout(os.path.join(derivatives, 'spynoza'))
derivatives_df = derivatives_layout.as_data_frame()
bold = derivatives_df[(derivatives_df['type'] == 'preproc')
& (derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)]
mask = derivatives_layout.get(subject=subject,
session=session,
type='mask',
return_type='file')[0]
mask = image.math_img('(im > .5).astype(int)', im=mask)
print(mask)
df = events.merge(bold,
on=['subject', 'session', 'run'],
suffixes=('_events', '_bold'))
models = []
for ix, row in df.iterrows():
results_dir = os.path.join(derivatives, 'modelfitting',
'sub-{}'.format(row['subject']))
if 'session' in row:
results_dir = os.path.join(results_dir,
'ses-{}'.format(row['session']))
os.makedirs(results_dir, exist_ok=True)
print('Fitting {}'.format(row['path_bold']))
model = FirstLevelModel(t_r=4, mask=mask)
paradigm = pd.read_table(row['path_events'])
paradigm_short = paradigm.copy()
paradigm_short['duration'] = 1
paradigm_short['trial_type'] = paradigm_short['trial_type'].map(
lambda x: '{}_instant'.format(x))
paradigm = pd.concat((paradigm, paradigm_short))
model.fit(row['path_bold'], paradigm)
left_right = model.compute_contrast('eye_L - eye_R',
output_type='z_score')
left_right.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_left_over_right_zmap.nii.gz'.format(
row['subject'], row['session'], row['run'])))
left_right = model.compute_contrast('eye_L - eye_R',
output_type='effect_size')
left_right.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_left_over_right_psc.nii.gz'.format(
row['subject'], row['session'], row['run'])))
eye_l_instant = model.compute_contrast('eye_L_instant',
output_type='z_score')
eye_l_instant.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_eye_l_instant_zmap.nii.gz'.format(
row['subject'], row['session'], row['run'])))
eye_l_instant = model.compute_contrast('eye_L_instant',
output_type='effect_size')
eye_l_instant.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_eye_l_instant_effect_size.nii.gz'.format(
row['subject'], row['session'], row['run'])))
eye_r_instant = model.compute_contrast('eye_R_instant',
output_type='z_score')
eye_r_instant.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_eye_r_instant_zmap.nii.gz'.format(
row['subject'], row['session'], row['run'])))
eye_r_instant = model.compute_contrast('eye_R_instant',
output_type='effect_size')
eye_r_instant.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_run-{}_eye_R_instant_effect_size.nii.gz'.format(
row['subject'], row['session'], row['run'])))
models.append(model)
second_level_model = SecondLevelModel(mask=mask)
second_level_model.fit(models)
left_right_group = second_level_model.compute_contrast(
first_level_contrast='eye_L - eye_R', output_type='z_score')
left_right_group.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_left_over_right_zmap.nii.gz'.format(
row['subject'], row['session'])))
left_right_group = second_level_model.compute_contrast(
first_level_contrast='eye_L - eye_R', output_type='effect_size')
left_right_group.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_left_over_right_effect_size.nii.gz'.format(
row['subject'], row['session'])))
"participant_id"]
participants_site["participant_id"] = participants_site[
"site"] + "x" + participants_site["orig_participant_id"]
participants = pd.concat((participants, participants_site))
else:
participants_site = pd.DataFrame({
"site": [site],
"participant_id":
"participants file missing"
})
participants_missing = pd.concat(
(participants_missing, participants_site))
# get file df
layout = BIDSLayout(site_dir)
df = layout.as_data_frame()
# copy_files = df[df.path.str.endswith(".nii.gz") | df.path.str.endswith("_sessions.tsv")]
nii_of_int = df[df.path.str.endswith(".nii.gz")
& df.modality.isin(["anat", "func"])]
session_files = df[df.path.str.endswith("_sessions.tsv")]
copy_files = pd.concat((nii_of_int, session_files))
def get_new_file(source_file, site_dir, out_dir, old_subject, site):
new_file = os.path.relpath(source_file, site_dir).replace(
old_subject, site + "x" + old_subject)
dest_file = os.path.join(out_dir, new_file)
dest_dir = os.path.dirname(dest_file)
mkdir(dest_dir)
return dest_file
def bids_data_selection(
base,
structural_match,
functional_match,
subjects,
sessions,
verbose=False,
joint_conditions=True,
):
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
layout = BIDSLayout(base)
df = layout.as_data_frame()
# Not crashing if the run field is not present
try:
# Run is for some reason recorded as float
df.loc[df['run'].notna(), 'run'] = df.loc[df['run'].notnull(),
'run'].apply(int).apply(str)
except KeyError:
pass
# drop event files
df = df[df.type != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
if 'func' in df.columns:
df.loc[df.modality == 'func', 'task'] = df.path.str.partition(
'task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func',
'scan_type'] = 'task-' + df['task'] + '_acq-' + df['acq']
if 'anat' in df.columns:
df.loc[df.modality == 'anat',
'scan_type'] = 'acq-' + df['acq'] + '_' + df['type']
# Unclear in current BIDS specification, we refer to BOLD/CBV as modalities and func/anat as types
df = df.rename(columns={'modality': 'type', 'type': 'modality'})
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if subjects:
df = filter_data(df, 'subject', subjects)
if sessions:
df = filter_data(df, 'session', sessions)
return df
def bids_data_selection(
base,
structural_match,
functional_match,
subjects,
sessions,
verbose=False,
joint_conditions=True,
):
"""
Creates a Pandas Dataframe descriptor from a BIDS datapath, optionally filtering out conditions.
Parameters
----------
base : str
path specifying the root directory of the BIDS data
structural_match : dict or bool
Dictionary specifying a whitelist of BIDS field identifiers.
False if no whitelist is specified.
functional_match : dict or bool
Dictionary specifying a whitelist of BIDS field identifiers.
False if no whitelist is specified.
subjects: list or bool
A list of subjects which may be present in the 'subjects' column of the created Pandas DataFrame, 'df'.
False if user does not want to filter DataFrame by subjects.
sessions: list or bool
A list of session names which may be present in the 'sessions' column of the created Pandas DataFrame, 'df'.
False if user does not want to filter DataFrame by sessions.
Returns
-------
df : pandas.DataFrame
A Pandas DataFrame with information corresponding to the whitelisted BIDS identifiers and optionally filtered by subjects and/or sessions.
"""
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
#layout = BIDSLayout(base, validate=False)
layout = BIDSLayout(base, validate=False, derivatives=True)
try:
df = layout.as_data_frame()
except AttributeError:
df = layout.to_df()
# Not crashing if the run field is not present
try:
# Run is for some reason recorded as float
df.loc[df['run'].notna(), 'run'] = df.loc[df['run'].notnull(),
'run'].apply(int).apply(str)
except KeyError:
pass
if verbose:
print(df)
print(df.columns)
# drop event files
# PyBIDS 0.6.5 and 0.10.2 compatibility
try:
df = df[df.type != 'events']
except AttributeError:
df = df[df.suffix != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
if 'func' in df.columns:
df.loc[df.modality == 'func', 'task'] = df.path.str.partition(
'task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func',
'scan_type'] = 'task-' + df['task'] + '_acq-' + df['acq']
if 'anat' in df.columns:
df.loc[df.modality == 'anat',
'scan_type'] = 'acq-' + df['acq'] + '_' + df['type']
# Unclear in current BIDS specification, we refer to BOLD/CBV as modalities and func/anat as types
# Can be removed after Pybids 0.10.2 migration
df = df.rename(columns={'modality': 'type', 'type': 'modality'})
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if subjects:
df = filter_data(df, 'subject', subjects)
if sessions:
df = filter_data(df, 'session', sessions)
return df
def bids_data_selection(base, structural_match, functional_match, subjects, sessions,
verbose=False,
joint_conditions=True,
):
validate = BIDSValidator()
if verbose:
for x in os.walk(base):
print(x[0])
if validate.is_bids(x[0]):
print("Is not BIDS-formatted.")
else:
print("Detected!")
layout = BIDSLayout(base)
df = layout.as_data_frame()
# Run is for some reason recorded as float
df.loc[df['run'].notna(),'run'] = df.loc[df['run'].notnull(),'run'].apply(int).apply(str)
#df['run'] = df['run'].astype(int)
# drop event files
df = df[df.type != 'events']
# rm .json
df = df.loc[df.path.str.contains('.nii')]
# generate scan types for later
df['scan_type'] = ""
#print(df.path.str.startswith('task', beg=0,end=len('task')))
beg = df.path.str.find('task-')
end = df.path.str.find('.')
#df.loc[df.modality == 'func', 'scan_type'] = 'acq-'+df['acq']+'_task-'+ df.path.str.partition('task-')[2].str.partition('.')[0]
#df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq']+'_' + df['type']
#TODO: fix task!=type
if 'func' in df.columns:
df.loc[df.modality == 'func', 'task'] = df.path.str.partition('task-')[2].str.partition('_')[0]
df.loc[df.modality == 'func', 'scan_type'] = 'task-' + df['task'] + '_acq-'+ df['acq']
if 'anat' in df.columns:
df.loc[df.modality == 'anat', 'scan_type'] = 'acq-'+df['acq'] +'_' + df['type']
#TODO: The following should be collapsed into one criterion category
if functional_match or structural_match:
res_df = pd.DataFrame()
if functional_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in functional_match.keys():
_df = _df.loc[_df[match].isin(functional_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, functional_match[match])
res_df = res_df.append(_df)
except:
pass
if structural_match:
_df = deepcopy(df)
try:
if joint_conditions:
for match in structural_match.keys():
_df = _df.loc[_df[match].isin(structural_match[match])]
res_df = res_df.append(_df)
else:
for match in structural_match.keys():
_df = filter_data(_df, match, structural_match[match])
res_df = res_df.append(_df)
except:
pass
df = res_df
if subjects:
df = filter_data(df, 'subject', subjects)
if sessions:
df = filter_data(df, 'session', sessions)
# Unclear in current BIDS specification, we refer to BOLD/CBV as modalities and func/anat as types
df = df.rename(columns={'modality': 'type', 'type': 'modality'})
return df
