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import numpy as np
from scipy.sparse import issparse
from scipy.sparse._sputils import convert_pydata_sparse_to_scipy
from scipy.sparse.csgraph._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")
accept_fv = [null_value_in]
if infinity_null:
accept_fv.append(np.inf)
if nan_null:
accept_fv.append(np.nan)
csgraph = convert_pydata_sparse_to_scipy(csgraph, accept_fv=accept_fv)
# if undirected and csc storage, then transposing in-place
# is quicker than later converting to csr.
if (not directed) and issparse(csgraph) and csgraph.format == "csc":
csgraph = csgraph.T
if issparse(csgraph):
if csr_output:
csgraph = csgraph.tocsr(copy=copy_if_sparse).astype(DTYPE, copy=False)
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 2-D")
if csgraph.shape[0] != csgraph.shape[1]:
raise ValueError("compressed-sparse graph must be shape (N, N)")
return csgraph
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