How to generate arbitrary high dimensional connectivity structures for scipy.ndimage.label

Question

I have some high dimensional boolean data, in this example an array with 4 dimensions, but this is arbitrary:

X.shape
 (3, 2, 66, 241)

I want to group the dataset into connected regions of True values, which can be done with scipy.ndimage.label, with the aid of a connectivity structure which says which points in the array should be considered to touch. The default 2-D structure is a cross:

[[0,1,0],
 [1,1,1],
 [0,1,0]]

Which can be easily extended to high dimensions if all those dimensions are connected. However I want to programmatically generate such a structure where I have a list of which dims are connected to which:

#We want to find connections across dims 2 and 3 across each slice of dims 0 and 1:
dim_connections=[[0],[1],[2,3]]

#Now we want two separate connected subspaces in our data:
dim_connections=[[0,1],[2,3]]

For individual cases I can work out with hard-thinking how to generate the correct structuring element, but I am struggling to work out the general rule! For clarity I want something like:

mystructure=construct_arbitrary_structure(ndim, dim_connections)
the_correct_result=scipy.ndimage.label(X,structure=my_structure)

Answer 1

This should work for you

def construct_arbitrary_structure(ndim, dim_connections):
    #Create structure array
    structure = np.zeros([3] * ndim, dtype=int)

    #Fill structure array
    for d in dim_connections:
        if len(d) > 1:
            # Set the connection between multiple dimensions
            for i in range(ndim):
                # Create a unit vector
                u = np.zeros(ndim, dtype=int)
                u[i] = 1

                # Create a mask by adding the connection between multiple dimensions
                M = np.zeros([3] * ndim, dtype=int)
                for j in d:
                    M += np.roll(u, j)
                structure += M
        else:
            # Set the connection for one dimension
            u = np.zeros(ndim, dtype=int)
            u[d[0]] = 1
            structure += u

    #Make sure it's symmetric
    for i in range(ndim):
        structure += np.roll(structure, 1, axis=i)

    return structure