utils.py 文件源码

def data_compression(fmri_masked, mask_img, mask_np, output_size):
    """
    data : array_like
         A matrix of shape (`V`, `N`) with `V` voxels `N` timepoints
         The functional dataset that needs to be reduced
    mask : a numpy array of the mask
    output_size : integer
        The number of elements that the data should be reduced to

    """


## Transform nifti files to a data matrix with the NiftiMasker
    import time
    from nilearn import input_data

    datacompressiontime=time.time()
    nifti_masker = input_data.NiftiMasker(mask_img= mask_img, memory='nilearn_cache',
                                          mask_strategy='background', memory_level=1,
                                          standardize=False)

    ward=[]

# Perform Ward clustering
    from sklearn.feature_extraction import image
    shape = mask_np.shape
    connectivity = image.grid_to_graph(n_x=shape[0], n_y=shape[1],
                                       n_z=shape[2], mask=mask_np)


    from sklearn.cluster import FeatureAgglomeration
    start = time.time()
    ward = FeatureAgglomeration(n_clusters=output_size, connectivity=connectivity,
                            linkage='ward')
    ward.fit(fmri_masked)
    #print("Ward agglomeration compressing voxels into clusters: %.2fs" % (time.time() - start))


    labels = ward.labels_

    #print ('Extracting reduced Dimension Data')
    data_reduced = ward.transform(fmri_masked)
    fmri_masked=[]
    #print('Data compression took ', (time.time()- datacompressiontime), ' seconds')
    return {'data':data_reduced, 'labels':labels}