def extract_rois_signals(preprocessing_folder ='pipeline_2', prefix= 'resampled_wr'): dataset = load_dynacomp(preprocessing_folder = preprocessing_folder,prefix = prefix) for idx, func in enumerate([dataset.func1, dataset.func2]): for i in range(len(dataset.subjects)): tic = time.clock() print func[i] output_path, _ = os.path.split(func[i]) print dataset.subjects[i] maps_img = dict_to_list(dataset.rois[i]) #add mask, smoothing, filter and detrending print 'Nifti' 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) #extract signal to x print 'masker' x = masker.fit_transform(func[i]) print x np.save(os.path.join(PATH_TO_SAVE_DATA,'output' + str(i+1) +'_rois_filter'),x) print time.clock() - tic return x
def extract_from_masker(dataset_like,dataset_mask, funci, timer =True): tic = time.clock() maps_img = dict_to_list(dataset_like) #add mask, smoothing, filter and detrending print 'Nifti' 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) #extract signal to x print 'masker' x = masker.fit_transform(funci) if timer: print time.clock() - tic return x
def extract_one_signal(dataset): for idx, func in enumerate([dataset.func1, dataset.func2]): for i in range(len(dataset.subjects)): tic = time.clock() #maps_img = dict_to_list(func) #add mask, smoothing, filter and detrending maps_img = dict_to_list(dataset.rois[i]) 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) #extract signal to x x = masker.fit_transform(func[i]) print "loading time : "+ str(time.clock() - tic) return x,maps_img