import numpy as np
from loader import load_dynacomp, dict_to_list
from nilearn.input_data import NiftiMapsMasker
dataset = load_dynacomp(preprocessing_folder='pipeline_2',
prefix='resampled_wr')
# func1, func2
for idx, func in enumerate([dataset.func1, dataset.func2]):
# all the subjects
for i in range(len(dataset.subjects)):
tic = time.clock()
output_path, _ = os.path.split(func[i])
print dataset.subjects[i]
maps_img = dict_to_list(dataset.rois[i])
# add mask, smoothing, filtering and detrending
masker = NiftiMapsMasker(maps_img=maps_img,
mask_img=dataset.mask,
low_pass=.1,
high_pass=.01,
smoothing_fwhm=6.,
t_r=1.05,
detrend=True,
standardize=False,
resampling_target='data',
memory_level=0,
verbose=5)
output_path, _ = os.path.split(func[i])
# extract the signal to x
x = masker.fit_transform(func[i])
Created on Tue May 12 09:42:23 2015 @author: [email protected] """ import loader import numpy as np from nilearn.datasets import fetch_nyu_rest from nilearn.input_data import NiftiMapsMasker from sklearn.covariance import GraphLassoCV ############################################################################## # Dynacomp rs-fMRI ############################################################################## dyn_dataset = loader.load_dynacomp() roi_imgs = loader.dict_to_list(loader.load_dynacomp_rois()[0]) roi_names, roi_coords = loader.load_roi_names_and_coords( dyn_dataset.subjects[0]) ind = np.tril_indices(len(roi_names), k=-1) dyn_fc = [] for subject in dyn_dataset.subjects: dyn_fc.append( loader.load_dynacomp_fc(subject_id=subject, session='func1', metric='pc', msdl=False, preprocessing_folder='pipeline_1')[ind]) dyn_fc = np.asarray(dyn_fc) ##############################################################################
import os, time
import numpy as np
from loader import load_dynacomp, dict_to_list
from nilearn.input_data import NiftiMapsMasker
dataset = load_dynacomp(preprocessing_folder='pipeline_2',
prefix='resampled_wr')
# func1, func2
for idx, func in enumerate([dataset.func1, dataset.func2]):
# all the subjects
for i in range(len(dataset.subjects)):
tic = time.clock()
output_path, _ = os.path.split(func[i])
print dataset.subjects[i]
maps_img = dict_to_list(dataset.rois[i])
# add mask, smoothing, filtering and detrending
masker = NiftiMapsMasker(maps_img=maps_img,
mask_img=dataset.mask,
low_pass=.1,
high_pass=.01,
smoothing_fwhm=6.,
t_r=1.05,
detrend=True,
standardize=False,
resampling_target='data',
memory_level=0,
verbose=5)
output_path, _ = os.path.split(func[i])
# extract the signal to x
x = masker.fit_transform(func[i])
@author: [email protected] """ import loader import numpy as np from nilearn.datasets import fetch_nyu_rest from nilearn.input_data import NiftiMapsMasker from sklearn.covariance import GraphLassoCV ############################################################################## # Dynacomp rs-fMRI ############################################################################## dyn_dataset = loader.load_dynacomp() roi_imgs = loader.dict_to_list(loader.load_dynacomp_rois()[0]) roi_names, roi_coords = loader.load_roi_names_and_coords(dyn_dataset.subjects[0]) ind = np.tril_indices(len(roi_names), k=-1) dyn_fc = [] for subject in dyn_dataset.subjects: dyn_fc.append(loader.load_dynacomp_fc(subject_id=subject, session='func1', metric='pc', msdl=False, preprocessing_folder='pipeline_1')[ind]) dyn_fc = np.asarray(dyn_fc) ############################################################################## # NYU rs-fMRI ############################################################################## nyu_func = fetch_nyu_rest()['func']
