`bucky.model.adjmat`¶

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

Module Contents¶

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

`_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.

Deprecated since version 0.9.0: As of cupy==9.0.0a2 this functionality is provided by scipy.sparse.csr.csr_matrix.diagonal()

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.