How to use the porespy.tools.extract_subsection function in porespy

def test_extract_subsection(self):
        sec = ps.tools.extract_subsection(self.blobs, [0.5])
        assert sp.all(sp.array(sp.shape(sec)) == 50)

def test_extract_subsection(self):
        sec = ps.tools.extract_subsection(self.blobs, [0.5])
        assert sp.all(sp.array(sp.shape(sec)) == 50)

if mode == 'mio':
        pw = int(sp.floor(dt.max()))
        impad = sp.pad(im, mode='symmetric', pad_width=pw)
        inletspad = sp.pad(inlets, mode='symmetric', pad_width=pw)
        inlets = sp.where(inletspad)
#        sizes = sp.unique(sp.around(sizes, decimals=0).astype(int))[-1::-1]
        imresults = sp.zeros(sp.shape(impad))
        for r in tqdm(sizes):
            imtemp = fftmorphology(impad, strel(r), mode='erosion')
            if access_limited:
                imtemp = trim_disconnected_blobs(imtemp, inlets)
            imtemp = fftmorphology(imtemp, strel(r), mode='dilation')
            if sp.any(imtemp):
                imresults[(imresults == 0)*imtemp] = r
        imresults = extract_subsection(imresults, shape=im.shape)
    elif mode == 'dt':
        inlets = sp.where(inlets)
        imresults = sp.zeros(sp.shape(im))
        for r in tqdm(sizes):
            imtemp = dt >= r
            if access_limited:
                imtemp = trim_disconnected_blobs(imtemp, inlets)
            if sp.any(imtemp):
                imtemp = spim.distance_transform_edt(~imtemp) < r
                imresults[(imresults == 0)*imtemp] = r
    elif mode == 'hybrid':
        inlets = sp.where(inlets)
        imresults = sp.zeros(sp.shape(im))
        for r in tqdm(sizes):
            imtemp = dt >= r
            if access_limited:

properties from skimage's ``regionprops``)

    Regions can be identified using a watershed algorithm, which can be a bit
    tricky to obtain desired results.  *PoreSpy* includes the SNOW algorithm,
    which may be helpful.

    """
    print('_'*60)
    print('Calculating regionprops')

    results = regionprops(im, coordinates='xy')
    for i in tqdm(range(len(results))):
        mask = results[i].image
        mask_padded = sp.pad(mask, pad_width=1, mode='constant')
        temp = spim.distance_transform_edt(mask_padded)
        dt = extract_subsection(temp, shape=mask.shape)
        # ---------------------------------------------------------------------
        # Slice indices
        results[i].slice = results[i]._slice
        # ---------------------------------------------------------------------
        # Volume of regions in voxels
        results[i].volume = results[i].area
        # ---------------------------------------------------------------------
        # Volume of bounding box, in voxels
        results[i].bbox_volume = sp.prod(mask.shape)
        # ---------------------------------------------------------------------
        # Create an image of the border
        results[i].border = dt == 1
        # ---------------------------------------------------------------------
        # Create an image of the maximal inscribed sphere
        r = dt.max()
        inv_dt = spim.distance_transform_edt(dt < r)

properties from skimage's ``regionprops``)

    Regions can be identified using a watershed algorithm, which can be a bit
    tricky to obtain desired results.  *PoreSpy* includes the SNOW algorithm,
    which may be helpful.

    """
    print('_'*60)
    print('Calculating regionprops')

    results = regionprops(im, coordinates='xy')
    for i in tqdm(range(len(results))):
        mask = results[i].image
        mask_padded = sp.pad(mask, pad_width=1, mode='constant')
        temp = spim.distance_transform_edt(mask_padded)
        dt = extract_subsection(temp, shape=mask.shape)
        # ---------------------------------------------------------------------
        # Slice indices
        results[i].slice = results[i]._slice
        # ---------------------------------------------------------------------
        # Volume of regions in voxels
        results[i].volume = results[i].area
        # ---------------------------------------------------------------------
        # Volume of bounding box, in voxels
        results[i].bbox_volume = sp.prod(mask.shape)
        # ---------------------------------------------------------------------
        # Create an image of the border
        results[i].border = dt == 1
        # ---------------------------------------------------------------------
        # Create an image of the maximal inscribed sphere
        r = dt.max()
        inv_dt = spim.distance_transform_edt(dt < r)