bucky.model.adjmat

Utility class to manage the adjacency matrix regardless of if its dense or sparse

Module Contents

Classes

buckyAij

Class that handles the adjacency matrix for the model, generalizes between dense/sparse.

Functions

_csr_diag(mat, out=None, indptr_sorted=False)

Get the diagonal of a scipy/cupy CSR sparse matrix quickly

_csr_is_ind_sorted(mat)

Check if a cupy/scipy CSR sparse matrix has its indices sorted

_read_edge_mat(G, weight_attr='weight', sparse=True, a_min=0.0)

Read the adj matrix of a networkx graph and convert it to the cupy/scipy format.

class bucky.model.adjmat.buckyAij(G, sparse=True, a_min=0.0)[source]

Class that handles the adjacency matrix for the model, generalizes between dense/sparse.

property A(self)[source]

property refering to the dense/sparse matrix

property diag(self)[source]

property refering to the cache diagional of the matrix

normalize(self, mat, mat_diag, axis=0)[source]

Normalize A along a given axis and keep the cache A_diag in sync

perturb(self, var)[source]

Apply a normal perturbation to the matrix (and keep its diag in sync)

bucky.model.adjmat._csr_diag(mat, out=None, indptr_sorted=False)[source]

Get the diagonal of a scipy/cupy CSR sparse matrix quickly

bucky.model.adjmat._csr_is_ind_sorted(mat)[source]

Check if a cupy/scipy CSR sparse matrix has its indices sorted

bucky.model.adjmat._read_edge_mat(G, weight_attr='weight', sparse=True, a_min=0.0)[source]

Read the adj matrix of a networkx graph and convert it to the cupy/scipy format.