# path to the atlases
# modl atlas from task-fMRI was used
ATLASES = [
'/storage/store/derivatives/OpenNeuro/modl/256/0.0001/maps.nii.gz',
fetch_atlas_basc_multiscale_2015().scale197
]
ATLASES_DESCR = ['modl256', 'basc197']
# path for the caching
CACHE_TIMESERIES = join(STORAGE_HOME, 'cache/timeseries')
if not os.path.exists(CACHE_TIMESERIES):
os.makedirs(CACHE_TIMESERIES)
MEMORY = Memory(CACHE_TIMESERIES)
N_JOBS = 10
dataset = load_camcan_rest(data_dir=CAMCAN_PREPROCESSED,
patients_excluded=CAMCAN_PATIENTS_EXCLUDED)
for atlas, atlas_descr in zip(ATLASES, ATLASES_DESCR):
time_series = Parallel(n_jobs=N_JOBS, verbose=1)(
delayed(extract_timeseries)
(func, atlas=atlas, confounds=confounds, memory=MEMORY, memory_level=0)
for func, confounds in zip(dataset.func, dataset.motion))
for ts, subject_id in zip(time_series, dataset.subject_id):
path_subject = os.path.join(CAMCAN_TIMESERIES, subject_id, atlas_descr)
if not os.path.exists(path_subject):
os.makedirs(path_subject)
filename = os.path.join(path_subject,
'%s_task-Rest_confounds.pkl' % subject_id)
joblib.dump(ts, filename)
import numpy as np
import pandas as pd
from camcan.datasets import load_camcan_rest
bb = load_camcan_rest()
out = list()
for ii, fname in enumerate(bb.motion):
data = np.genfromtxt(fname)
df = pd.DataFrame(data)
df['subject'] = bb.subject_id[ii][4:]
df['n_points'] = data.shape[0]
print(data.shape)
out.append(df)
out = pd.concat(out)
out.columns = ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'subject']
out.to_csv('./data/motion.csv')