def test_bru2bids():
bru2bids(data_dir,
inflated_size=False,
functional_match={"acquisition":["EPI"]},
structural_match={"acquisition":["TurboRARE"]},
out_base='/tmp/samri_testing/pytest/',
)
def test_bru2bids():
bru2bids(BRU_DIR,
inflated_size=False,
functional_match={"acquisition":["EPI"]},
structural_match={"acquisition":["TurboRARE"]},
out_base='/var/tmp/samri_testing/pytest/',
)
def test_bru2bids(tmp_path):
bru2bids(BRU_DIR,
inflated_size=False,
functional_match={"acquisition":["EPI"]},
structural_match={"acquisition":["TurboRARE"]},
out_base=tmp_path,
#keep_crashdump=True,
keep_work=True,
)
def produce_bids():
from labbookdb.report.selection import animal_id, parameterized
db_path = '~/syncdata/meta.db'
data_dir = '/usr/share/drlfom_data/'
scratch_dir = os.path.expanduser('~/.scratch')
base_dir = '{}/mlebe'.format(scratch_dir)
bru2bids(
data_dir,
inflated_size=False,
functional_match={
'acquisition': ['EPIlowcov'],
},
structural_match={
'acquisition': ['TurboRARElowcov'],
},
keep_crashdump=True,
out_base=base_dir,
)
# Add irregularity metadata
subjects_ldb = [
i[4:] for i in os.listdir('{}/mlebe/bids'.format(scratch_dir))
if i.startswith('sub')
]
subjects = [animal_id(db_path, 'ETH/AIC', i) for i in subjects_ldb]
subjects_info = parameterized(db_path,
'animals info',
animal_filter=subjects)
convert_column_names = {
'AnimalExternalIdentifier_identifier': 'subject',
'Animal_birth_date': 'birth_date',
'Animal_sex': 'sex',
}
subjects_info = subjects_info.loc[
subjects_info['AnimalExternalIdentifier_database'] == 'ETH/AIC',
convert_column_names.keys()]
subjects_info = subjects_info.rename(columns=convert_column_names)
subjects_info['age [d]'] = ''
irregularities = parameterized(db_path,
'animals measurements irregularities',
animal_filter=subjects)
bids_dir = '{}/bids'.format(base_dir)
for sub_dir in os.listdir(bids_dir):
sub_path = os.path.join(bids_dir, sub_dir)
if os.path.isdir(sub_path) and sub_dir[:4] == 'sub-':
sessions_file = os.path.join(sub_path,
'{}_sessions.tsv'.format(sub_dir))
if os.path.isfile(sessions_file):
sessions = pd.read_csv(sessions_file, sep='\t')
sessions['irregularities'] = ''
first_session_date = sessions['acq_time'].min()
first_session_date = datetime.strptime(first_session_date,
'%Y-%m-%dT%H:%M:%S')
age = first_session_date - subjects_info.loc[
subjects_info['subject'] == sub_dir[4:], 'birth_date']
age = age / np.timedelta64(1, 'D')
age = np.round(age)
subjects_info.loc[subjects_info['subject'] == sub_dir[4:],
'age [d]'] = age
for mydate in sessions['acq_time'].unique():
mydate_date = datetime.strptime(mydate,
'%Y-%m-%dT%H:%M:%S')
irregularity_list = irregularities.loc[
irregularities['Measurement_date'] == mydate_date,
'Irregularity_description'].tolist()
irregularity_list = '; '.join(irregularity_list)
sessions.loc[sessions['acq_time'] == mydate,
'irregularities'] = irregularity_list
sessions.to_csv(sessions_file, sep='\t', index=False)
subjects_info = subjects_info.drop('birth_date', 1)
subjects_info.to_csv('{}/participants.tsv'.format(bids_dir),
sep='\t',
index=False)
subjects_info.to_csv('../data/participants.tsv'.format(bids_dir),
sep='\t',
index=False)