Utility functions

Assorted utility functions for the motifcluster module are in motifcluster.utils.

_a_b_one(a_mat, b_mat)

Compute a right-multiplication with the ones matrix.

Compute a * (b @ one_mat) where a, b, ones_mat are square matrices of the same size, and ones_mat contains all entries equal to one. The product * is an entry-wise (Hadamard) product, while @ represents matrix multiplication. This method is more efficient than the naive approach when a or b are sparse.

Parameters:a, b (matrix) – Square matrices of the same size. Returns:The sparse square matrix a * (b @ one_mat). Return type:sparse matrix

_a_one_b(a_mat, b_mat)

Compute a left-multiplication with the ones matrix.

Compute a * (one_mat @ b) where a, b, ones_mat are square matrices of the same size, and ones_mat contains all entries equal to one. The product * is an entry-wise (Hadamard) product, while @ represents matrix multiplication. This method is more efficient than the naive approach when a or b are sparse.

Parameters:a, b (matrix) – Square matrices of the same size. Returns:The sparse square matrix a * (one_mat @ b). Return type:sparse matrix

_drop0_killdiag(some_mat)

Set diagonal entries to zero and sparsify.

Set the diagonal entries of a matrix to zero and convert it to sparse form.

Parameters:some_mat (matrix) – A square matrix. Returns:sparse_mat – A sparse-form copy of some_mat with its diagonal entries set to zero. Return type:sparse matrix

_random_sparse_matrix(m, n, p, sample_weight_type='constant', w=1)

Build a random sparse matrix.

Build a sparse matrix of size m * n with non-zero probability p. Edge weights can be unweighted, constant-weighted or Poisson-weighted.

Parameters:

• m (int) – Size of first dimension of matrix.
• n (int) – Size of second dimension of matrix.
• p (float) – Probability that each entry is non-zero (before weighting).
• sample_weight_type (str) – Type of weighting scheme.
• w (float) – Weight parameter.

Returns:

A random sparse matrix.

Return type:

sparse matrix

get_largest_component(adj_mat, gr_method)

Get largest connected component.

Get the indices of the vertices in the largest connected component of a graph from its adjacency matrix.

Parameters:

• adj_mat (matrix) – An adjacency matrix of a graph.
• gr_method (str) – Format to use before building the graph. One of “sparse” or “dense”.

Returns:

verts_to_keep – A list of indices corresponding to the vertices in the largest connected component.

Return type:

list

Examples

>>> adj_mat = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0]).reshape((3, 3))
>>> get_largest_component(adj_mat)

get_motif_names()

Get common motif names.

Get the names of some common motifs as strings.

Returns:motif_names – A list of names (strings) of common motifs. Return type:list