get_tfa_input_from_nifti.py 文件源码

def extract_data(nifti_file, mask_file, out_file, zscore, detrend, smoothing_fwmw):
    if mask_file is None:
        #whole brain, get coordinate info from nifti_file itself
        mask = nib.load(nifti_file.name)
    else:
        mask = nib.load(mask_file.name)
    affine = mask.get_affine()
    if mask_file is None:
        mask_data = mask.get_data()
        if mask_data.ndim == 4:
            #get mask in 3D
            img_data_type = mask.header.get_data_dtype()
            n_tr = mask_data.shape[3]
            mask_data = mask_data[:,:,:,n_tr//2].astype(bool)
            mask = nib.Nifti1Image(mask_data.astype(img_data_type), affine)
        else:
            mask_data = mask_data.astype(bool)
    else:
        mask_data = mask.get_data().astype(bool)

    #get voxel coordinates
    R = np.float64(np.argwhere(mask_data))

    #get scanner RAS coordinates based on voxel coordinates
    if affine is not []:
        R = (np.dot(affine[:3,:3], R.T) + affine[:3,3:4]).T

    #get ROI data, and run preprocessing
    nifti_masker = NiftiMasker(mask_img=mask, standardize=zscore, detrend=detrend, smoothing_fwhm=smoothing_fwmw)
    img = nib.load(nifti_file.name)
    all_images = np.float64(nifti_masker.fit_transform(img))
    data = all_images.T.copy()

    #save data
    subj_data = {'data': data, 'R': R}
    scipy.io.savemat(out_file.name, subj_data)