Scipy CSGraph Wrappers

If you are working with graphs (representing neurons or similar objects) you might already be using routines implemented in scipy.csgraph - for example, to compute distances or extract connected components.

Assuming your graphs are rooted trees, you can use fastcore as drop-in replacement for some of these scipy functions:

>>> from scipy.sparse import csr_array
>>> from navis_fastcore.wrappers.csgraph import dijkstra
>>> graph = [
... [0, 1, 0, 0],
... [0, 0, 0, 1],
... [0, 0, 0, 3],
... [0, 0, 0, 0]
... ]
>>> graph = csr_array(graph)
>>> dist_matrix = dijkstra(csgraph=graph, directed=False)
>>> dist_matrix
array([[0., 1., 5., 2.],
       [1., 0., 4., 1.],
       [5., 4., 0., 3.],
       [2., 1., 3., 0.]], dtype=float32)

fastcore currently implements the following scipy.csgraph functions:

dijkstra
connected_components

Notes

Not all arguments are supported. For example, dijkstra currently does not support return_predecessors=True. See the docstrings for details!
By default, these functions will perform a check to make sure the input graph is actually directed and acyclic. These are generally fairly fast but if you are confident in your graphs and want to save the odd millisecond, you can set checks=False.