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- 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 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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