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']