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import numpy as np |
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from scipy.sparse import issparse |
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from scipy.sparse._sputils import convert_pydata_sparse_to_scipy |
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from scipy.sparse.csgraph._tools import ( |
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csgraph_to_dense, csgraph_from_dense, |
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csgraph_masked_from_dense, csgraph_from_masked |
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) |
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DTYPE = np.float64 |
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def validate_graph(csgraph, directed, dtype=DTYPE, |
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csr_output=True, dense_output=True, |
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copy_if_dense=False, copy_if_sparse=False, |
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null_value_in=0, null_value_out=np.inf, |
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infinity_null=True, nan_null=True): |
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"""Routine for validation and conversion of csgraph inputs""" |
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if not (csr_output or dense_output): |
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raise ValueError("Internal: dense or csr output must be true") |
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accept_fv = [null_value_in] |
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if infinity_null: |
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accept_fv.append(np.inf) |
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if nan_null: |
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accept_fv.append(np.nan) |
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csgraph = convert_pydata_sparse_to_scipy(csgraph, accept_fv=accept_fv) |
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if (not directed) and issparse(csgraph) and csgraph.format == "csc": |
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csgraph = csgraph.T |
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if issparse(csgraph): |
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if csr_output: |
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csgraph = csgraph.tocsr(copy=copy_if_sparse).astype(DTYPE, copy=False) |
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else: |
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csgraph = csgraph_to_dense(csgraph, null_value=null_value_out) |
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elif np.ma.isMaskedArray(csgraph): |
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if dense_output: |
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mask = csgraph.mask |
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csgraph = np.array(csgraph.data, dtype=DTYPE, copy=copy_if_dense) |
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csgraph[mask] = null_value_out |
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else: |
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csgraph = csgraph_from_masked(csgraph) |
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else: |
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if dense_output: |
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csgraph = csgraph_masked_from_dense(csgraph, |
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copy=copy_if_dense, |
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null_value=null_value_in, |
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nan_null=nan_null, |
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infinity_null=infinity_null) |
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mask = csgraph.mask |
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csgraph = np.asarray(csgraph.data, dtype=DTYPE) |
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csgraph[mask] = null_value_out |
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else: |
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csgraph = csgraph_from_dense(csgraph, null_value=null_value_in, |
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infinity_null=infinity_null, |
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nan_null=nan_null) |
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if csgraph.ndim != 2: |
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raise ValueError("compressed-sparse graph must be 2-D") |
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if csgraph.shape[0] != csgraph.shape[1]: |
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raise ValueError("compressed-sparse graph must be shape (N, N)") |
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return csgraph |
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