You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
from __future__ import division, print_function, absolute_import
|
|
|
|
import numpy as np
|
|
from scipy.sparse import csr_matrix, isspmatrix, isspmatrix_csc
|
|
from ._tools import csgraph_to_dense, csgraph_from_dense,\
|
|
csgraph_masked_from_dense, csgraph_from_masked
|
|
|
|
DTYPE = np.float64
|
|
|
|
|
|
def validate_graph(csgraph, directed, dtype=DTYPE,
|
|
csr_output=True, dense_output=True,
|
|
copy_if_dense=False, copy_if_sparse=False,
|
|
null_value_in=0, null_value_out=np.inf,
|
|
infinity_null=True, nan_null=True):
|
|
"""Routine for validation and conversion of csgraph inputs"""
|
|
if not (csr_output or dense_output):
|
|
raise ValueError("Internal: dense or csr output must be true")
|
|
|
|
# if undirected and csc storage, then transposing in-place
|
|
# is quicker than later converting to csr.
|
|
if (not directed) and isspmatrix_csc(csgraph):
|
|
csgraph = csgraph.T
|
|
|
|
if isspmatrix(csgraph):
|
|
if csr_output:
|
|
csgraph = csr_matrix(csgraph, dtype=DTYPE, copy=copy_if_sparse)
|
|
else:
|
|
csgraph = csgraph_to_dense(csgraph, null_value=null_value_out)
|
|
elif np.ma.isMaskedArray(csgraph):
|
|
if dense_output:
|
|
mask = csgraph.mask
|
|
csgraph = np.array(csgraph.data, dtype=DTYPE, copy=copy_if_dense)
|
|
csgraph[mask] = null_value_out
|
|
else:
|
|
csgraph = csgraph_from_masked(csgraph)
|
|
else:
|
|
if dense_output:
|
|
csgraph = csgraph_masked_from_dense(csgraph,
|
|
copy=copy_if_dense,
|
|
null_value=null_value_in,
|
|
nan_null=nan_null,
|
|
infinity_null=infinity_null)
|
|
mask = csgraph.mask
|
|
csgraph = np.asarray(csgraph.data, dtype=DTYPE)
|
|
csgraph[mask] = null_value_out
|
|
else:
|
|
csgraph = csgraph_from_dense(csgraph, null_value=null_value_in,
|
|
infinity_null=infinity_null,
|
|
nan_null=nan_null)
|
|
|
|
if csgraph.ndim != 2:
|
|
raise ValueError("compressed-sparse graph must be two dimensional")
|
|
|
|
if csgraph.shape[0] != csgraph.shape[1]:
|
|
raise ValueError("compressed-sparse graph must be shape (N, N)")
|
|
|
|
return csgraph
|