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idx_grid
    csgraph_to_masked(csgraph)

    Convert a sparse graph representation to a masked array representation

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : csr_matrix, csc_matrix, or lil_matrix
        Sparse representation of a graph.

    Returns
    -------
    graph : MaskedArray
        The masked dense representation of the sparse graph.

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import csgraph_to_masked

    >>> graph = csr_matrix( [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ])
    >>> graph
    <4x4 sparse matrix of type '<class 'numpy.int64'>'
        with 4 stored elements in Compressed Sparse Row format>

    >>> csgraph_to_masked(graph)
    masked_array(
      data=[[ --, 1.0, 2.0,  --],
            [ --,  --,  --, 1.0],
            [ --,  --,  --, 3.0],
            [ --,  --,  --,  --]],
      mask=[[ True, False, False,  True],
            [ True,  True,  True, False],
            [ True,  True,  True, False],
            [ True,  True,  True,  True]],
      fill_value=1e+20)

    Ncsgraph should be a square matrixinfcsgraph_from_maskedcgraph should have two dimensionsITYPEastypesumcline_in_tracebackmaskpindnumpynanscipy.sparse.csgraph._toolsma` 
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_validationnpcsgraph_to_dense (line 222)getA1indptrCImportErrorcsgraph_masked_from_densecopy_if_densecsgraph_to_denseconstruct_dist_matrixtocsr__main__isnan
    csgraph_from_dense(graph, null_value=0, nan_null=True, infinity_null=True)

    Construct a CSR-format sparse graph from a dense matrix.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like
        Input graph.  Shape should be (n_nodes, n_nodes).
    null_value : float or None (optional)
        Value that denotes non-edges in the graph.  Default is zero.
    infinity_null : bool
        If True (default), then infinite entries (both positive and negative)
        are treated as null edges.
    nan_null : bool
        If True (default), then NaN entries are treated as non-edges

    Returns
    -------
    csgraph : csr_matrix
        Compressed sparse representation of graph,

    Examples
    --------
    >>> from scipy.sparse.csgraph import csgraph_from_dense

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]

    >>> csgraph_from_dense(graph)
    <4x4 sparse matrix of type '<class 'numpy.float64'>'
        with 4 stored elements in Compressed Sparse Row format>

    minimumcsr_outputasarray@ 
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scipy.sparsemasked_values_tools.pyxcsgraphcsgraph_from_dense (line 172)
    csgraph_masked_from_dense(graph, null_value=0, nan_null=True,
                              infinity_null=True, copy=True)

    Construct a masked array graph representation from a dense matrix.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like
        Input graph.  Shape should be (n_nodes, n_nodes).
    null_value : float or None (optional)
        Value that denotes non-edges in the graph.  Default is zero.
    infinity_null : bool
        If True (default), then infinite entries (both positive and negative)
        are treated as null edges.
    nan_null : bool
        If True (default), then NaN entries are treated as non-edges

    Returns
    -------
    csgraph : MaskedArray
        masked array representation of graph

    Examples
    --------
    >>> from scipy.sparse.csgraph import csgraph_masked_from_dense

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]

    >>> csgraph_masked_from_dense(graph)
    masked_array(
      data=[[--,  1,  2, --],
            [--, --, --,  1],
            [--, --, --,  3],
            [--, --, --, --]],
      mask=[[ True, False, False,  True],
            [ True,  True,  True, False],
            [ True,  True,  True, False],
            [ True,  True,  True,  True]],
      fill_value=0)

    ValueErrorshapefloat64rangeisspmatrix_lildist_matrixnnullarrayzerosisinfargsort__import__orderint32__name__masked_invalidbool
    reconstruct_path(csgraph, predecessors, directed=True)

    Construct a tree from a graph and a predecessor list.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix
        The N x N matrix representing the directed or undirected graph
        from which the predecessors are drawn.
    predecessors : array_like, one dimension
        The length-N array of indices of predecessors for the tree.  The
        index of the parent of node i is given by predecessors[i].
    directed : bool, optional
        If True (default), then operate on a directed graph: only move from
        point i to point j along paths csgraph[i, j].
        If False, then operate on an undirected graph: the algorithm can
        progress from point i to j along csgraph[i, j] or csgraph[j, i].

    Returns
    -------
    cstree : csr matrix
        The N x N directed compressed-sparse representation of the tree drawn
        from csgraph which is encoded by the predecessor list.

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import reconstruct_path

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 3)	1
      (2, 3)	3

    >>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32)

    >>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False)
    >>> cstree.todense()
    matrix([[0., 1., 2., 0.],
            [0., 0., 0., 1.],
            [0., 0., 0., 0.],
            [0., 0., 0., 0.]])

    null_value
    construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf)

    Construct distance matrix from a predecessor matrix

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like or sparse
        The N x N matrix representation of a directed or undirected graph.
        If dense, then non-edges are indicated by zeros or infinities.
    predecessors : array_like
        The N x N matrix of predecessors of each node (see Notes below).
    directed : bool, optional
        If True (default), then operate on a directed graph: only move from
        point i to point j along paths csgraph[i, j].
        If False, then operate on an undirected graph: the algorithm can
        progress from point i to j along csgraph[i, j] or csgraph[j, i].
    null_value : bool, optional
        value to use for distances between unconnected nodes.  Default is
        np.inf

    Returns
    -------
    dist_matrix : ndarray
        The N x N matrix of distances between nodes along the path specified
        by the predecessor matrix.  If no path exists, the distance is zero.

    Notes
    -----
    The predecessor matrix is of the form returned by
    `shortest_path`.  Row i of the predecessor matrix contains
    information on the shortest paths from point i: each entry
    predecessors[i, j] gives the index of the previous node in the path from
    point i to point j.  If no path exists between point i and j, then
    predecessors[i, j] = -9999

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import construct_dist_matrix

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 3)	1
      (2, 3)	3

    >>> pred = np.array([[-9999, 0, 0, 2],
    ...                  [1, -9999, 0, 1],
    ...                  [2, 0, -9999, 2],
    ...                  [1, 3, 3, -9999]], dtype=np.int32)

    >>> construct_dist_matrix(graph=graph, predecessors=pred, directed=False)
    array([[0., 1., 2., 5.],
           [1., 0., 3., 1.],
           [2., 3., 0., 3.],
           [2., 1., 3., 0.]])

    graph should be a square arraygraphdata__test__csgraph_from_masked (line 18)
Tools and utilities for working with compressed sparse graphs
copyDTYPEcsgraph_to_masked (line 338)onescsr_matrixindicesdata2construct_dist_matrix (line 500)searchsortedinfinity_nullgraph and predecessors must have the same shapeemptyarangecumsumdense_outputfillisspmatrixnan_nulldirectedisspmatrix_csrreconstruct_pathpredecessorsnumpy.core.multiarray failed to importcsgraph_from_densendimcsgraph_masked_from_dense (line 83)validate_graphmasked_arraynumpy.core.umath failed to import
    csgraph_to_dense(csgraph, null_value=0)

    Convert a sparse graph representation to a dense representation

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : csr_matrix, csc_matrix, or lil_matrix
        Sparse representation of a graph.
    null_value : float, optional
        The value used to indicate null edges in the dense representation.
        Default is 0.

    Returns
    -------
    graph : ndarray
        The dense representation of the sparse graph.

    Notes
    -----
    For normal sparse graph representations, calling csgraph_to_dense with
    null_value=0 produces an equivalent result to using dense format
    conversions in the main sparse package.  When the sparse representations
    have repeated values, however, the results will differ.  The tools in
    scipy.sparse will add repeating values to obtain a final value.  This
    function will select the minimum among repeating values to obtain a
    final value.  For example, here we'll create a two-node directed sparse
    graph with multiple edges from node 0 to node 1, of weights 2 and 3.
    This illustrates the difference in behavior:

    >>> from scipy.sparse import csr_matrix, csgraph
    >>> data = np.array([2, 3])
    >>> indices = np.array([1, 1])
    >>> indptr = np.array([0, 2, 2])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 5],
           [0, 0]])
    >>> csgraph.csgraph_to_dense(M)
    array([[0., 2.],
           [0., 0.]])

    The reason for this difference is to allow a compressed sparse graph to
    represent multiple edges between any two nodes.  As most sparse graph
    algorithms are concerned with the single lowest-cost edge between any
    two nodes, the default scipy.sparse behavior of summming multiple weights
    does not make sense in this context.

    The other reason for using this routine is to allow for graphs with
    zero-weight edges.  Let's look at the example of a two-node directed
    graph, connected by an edge of weight zero:

    >>> from scipy.sparse import csr_matrix, csgraph
    >>> data = np.array([0.0])
    >>> indices = np.array([1])
    >>> indptr = np.array([0, 1, 1])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 0],
           [0, 0]])
    >>> csgraph.csgraph_to_dense(M, np.inf)
    array([[inf,  0.],
           [inf, inf]])

    In the first case, the zero-weight edge gets lost in the dense
    representation.  In the second case, we can choose a different null value
    and see the true form of the graph.

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import csgraph_to_dense

    >>> graph = csr_matrix( [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ])
    >>> graph
    <4x4 sparse matrix of type '<class 'numpy.int64'>'
        with 4 stored elements in Compressed Sparse Row format>

    >>> csgraph_to_dense(graph)
    array([[0., 1., 2., 0.],
           [0., 0., 0., 1.],
           [0., 0., 0., 3.],
           [0., 0., 0., 0.]])

    csgraph_to_masked
    csgraph_from_masked(graph)

    Construct a CSR-format graph from a masked array.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : MaskedArray
        Input graph.  Shape should be (n_nodes, n_nodes).

    Returns
    -------
    csgraph : csr_matrix
        Compressed sparse representation of graph,

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.sparse.csgraph import csgraph_from_masked

    >>> graph_masked = np.ma.masked_array(data =[
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ],
    ... mask=[[ True, False, False,  True],
    ...       [ True,  True,  True, False],
    ...       [ True,  True,  True, False],
    ...       [ True,  True,  True,  True]],
    ... fill_value = 0)

    >>> csgraph_from_masked(graph_masked)
    <4x4 sparse matrix of type '<class 'numpy.float64'>'
        with 4 stored elements in Compressed Sparse Row format>

    todensecsgraph must be lil, csr, or csc formatdtypereconstruct_path (line 412)isspmatrix_csccompressedð?@ð¿H
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scipy\sparse\csgraph\_tools.c_tools.pyx__init__.pxdtype.pxd_toolsparameters.pxiDTYPE_tITYPE_tcsgraph_from_maskedcsgraph_masked_from_densecsgraph_from_densecsgraph_to_densecsgraph_to_maskedreconstruct_pathconstruct_dist_matrix
    reconstruct_path(csgraph, predecessors, directed=True)

    Construct a tree from a graph and a predecessor list.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : array_like or sparse matrix
        The N x N matrix representing the directed or undirected graph
        from which the predecessors are drawn.
    predecessors : array_like, one dimension
        The length-N array of indices of predecessors for the tree.  The
        index of the parent of node i is given by predecessors[i].
    directed : bool, optional
        If True (default), then operate on a directed graph: only move from
        point i to point j along paths csgraph[i, j].
        If False, then operate on an undirected graph: the algorithm can
        progress from point i to j along csgraph[i, j] or csgraph[j, i].

    Returns
    -------
    cstree : csr matrix
        The N x N directed compressed-sparse representation of the tree drawn
        from csgraph which is encoded by the predecessor list.

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import reconstruct_path

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 3)	1
      (2, 3)	3

    >>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32)

    >>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False)
    >>> cstree.todense()
    matrix([[0., 1., 2., 0.],
            [0., 0., 0., 1.],
            [0., 0., 0., 0.],
            [0., 0., 0., 0.]])

    
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    csgraph_to_masked(csgraph)

    Convert a sparse graph representation to a masked array representation

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : csr_matrix, csc_matrix, or lil_matrix
        Sparse representation of a graph.

    Returns
    -------
    graph : MaskedArray
        The masked dense representation of the sparse graph.

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import csgraph_to_masked

    >>> graph = csr_matrix( [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ])
    >>> graph
    <4x4 sparse matrix of type '<class 'numpy.int64'>'
        with 4 stored elements in Compressed Sparse Row format>

    >>> csgraph_to_masked(graph)
    masked_array(
      data=[[ --, 1.0, 2.0,  --],
            [ --,  --,  --, 1.0],
            [ --,  --,  --, 3.0],
            [ --,  --,  --,  --]],
      mask=[[ True, False, False,  True],
            [ True,  True,  True, False],
            [ True,  True,  True, False],
            [ True,  True,  True,  True]],
      fill_value=1e+20)

    
    csgraph_masked_from_dense(graph, null_value=0, nan_null=True,
                              infinity_null=True, copy=True)

    Construct a masked array graph representation from a dense matrix.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like
        Input graph.  Shape should be (n_nodes, n_nodes).
    null_value : float or None (optional)
        Value that denotes non-edges in the graph.  Default is zero.
    infinity_null : bool
        If True (default), then infinite entries (both positive and negative)
        are treated as null edges.
    nan_null : bool
        If True (default), then NaN entries are treated as non-edges

    Returns
    -------
    csgraph : MaskedArray
        masked array representation of graph

    Examples
    --------
    >>> from scipy.sparse.csgraph import csgraph_masked_from_dense

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]

    >>> csgraph_masked_from_dense(graph)
    masked_array(
      data=[[--,  1,  2, --],
            [--, --, --,  1],
            [--, --, --,  3],
            [--, --, --, --]],
      mask=[[ True, False, False,  True],
            [ True,  True,  True, False],
            [ True,  True,  True, False],
            [ True,  True,  True,  True]],
      fill_value=0)

    
    csgraph_from_dense(graph, null_value=0, nan_null=True, infinity_null=True)

    Construct a CSR-format sparse graph from a dense matrix.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like
        Input graph.  Shape should be (n_nodes, n_nodes).
    null_value : float or None (optional)
        Value that denotes non-edges in the graph.  Default is zero.
    infinity_null : bool
        If True (default), then infinite entries (both positive and negative)
        are treated as null edges.
    nan_null : bool
        If True (default), then NaN entries are treated as non-edges

    Returns
    -------
    csgraph : csr_matrix
        Compressed sparse representation of graph,

    Examples
    --------
    >>> from scipy.sparse.csgraph import csgraph_from_dense

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]

    >>> csgraph_from_dense(graph)
    <4x4 sparse matrix of type '<class 'numpy.float64'>'
        with 4 stored elements in Compressed Sparse Row format>

    
    csgraph_from_masked(graph)

    Construct a CSR-format graph from a masked array.

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : MaskedArray
        Input graph.  Shape should be (n_nodes, n_nodes).

    Returns
    -------
    csgraph : csr_matrix
        Compressed sparse representation of graph,

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.sparse.csgraph import csgraph_from_masked

    >>> graph_masked = np.ma.masked_array(data =[
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ],
    ... mask=[[ True, False, False,  True],
    ...       [ True,  True,  True, False],
    ...       [ True,  True,  True, False],
    ...       [ True,  True,  True,  True]],
    ... fill_value = 0)

    >>> csgraph_from_masked(graph_masked)
    <4x4 sparse matrix of type '<class 'numpy.float64'>'
        with 4 stored elements in Compressed Sparse Row format>

    
    csgraph_to_dense(csgraph, null_value=0)

    Convert a sparse graph representation to a dense representation

    .. versionadded:: 0.11.0

    Parameters
    ----------
    csgraph : csr_matrix, csc_matrix, or lil_matrix
        Sparse representation of a graph.
    null_value : float, optional
        The value used to indicate null edges in the dense representation.
        Default is 0.

    Returns
    -------
    graph : ndarray
        The dense representation of the sparse graph.

    Notes
    -----
    For normal sparse graph representations, calling csgraph_to_dense with
    null_value=0 produces an equivalent result to using dense format
    conversions in the main sparse package.  When the sparse representations
    have repeated values, however, the results will differ.  The tools in
    scipy.sparse will add repeating values to obtain a final value.  This
    function will select the minimum among repeating values to obtain a
    final value.  For example, here we'll create a two-node directed sparse
    graph with multiple edges from node 0 to node 1, of weights 2 and 3.
    This illustrates the difference in behavior:

    >>> from scipy.sparse import csr_matrix, csgraph
    >>> data = np.array([2, 3])
    >>> indices = np.array([1, 1])
    >>> indptr = np.array([0, 2, 2])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 5],
           [0, 0]])
    >>> csgraph.csgraph_to_dense(M)
    array([[0., 2.],
           [0., 0.]])

    The reason for this difference is to allow a compressed sparse graph to
    represent multiple edges between any two nodes.  As most sparse graph
    algorithms are concerned with the single lowest-cost edge between any
    two nodes, the default scipy.sparse behavior of summming multiple weights
    does not make sense in this context.

    The other reason for using this routine is to allow for graphs with
    zero-weight edges.  Let's look at the example of a two-node directed
    graph, connected by an edge of weight zero:

    >>> from scipy.sparse import csr_matrix, csgraph
    >>> data = np.array([0.0])
    >>> indices = np.array([1])
    >>> indptr = np.array([0, 1, 1])
    >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))
    >>> M.toarray()
    array([[0, 0],
           [0, 0]])
    >>> csgraph.csgraph_to_dense(M, np.inf)
    array([[inf,  0.],
           [inf, inf]])

    In the first case, the zero-weight edge gets lost in the dense
    representation.  In the second case, we can choose a different null value
    and see the true form of the graph.

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import csgraph_to_dense

    >>> graph = csr_matrix( [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ])
    >>> graph
    <4x4 sparse matrix of type '<class 'numpy.int64'>'
        with 4 stored elements in Compressed Sparse Row format>

    >>> csgraph_to_dense(graph)
    array([[0., 1., 2., 0.],
           [0., 0., 0., 1.],
           [0., 0., 0., 3.],
           [0., 0., 0., 0.]])

    
    construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf)

    Construct distance matrix from a predecessor matrix

    .. versionadded:: 0.11.0

    Parameters
    ----------
    graph : array_like or sparse
        The N x N matrix representation of a directed or undirected graph.
        If dense, then non-edges are indicated by zeros or infinities.
    predecessors : array_like
        The N x N matrix of predecessors of each node (see Notes below).
    directed : bool, optional
        If True (default), then operate on a directed graph: only move from
        point i to point j along paths csgraph[i, j].
        If False, then operate on an undirected graph: the algorithm can
        progress from point i to j along csgraph[i, j] or csgraph[j, i].
    null_value : bool, optional
        value to use for distances between unconnected nodes.  Default is
        np.inf

    Returns
    -------
    dist_matrix : ndarray
        The N x N matrix of distances between nodes along the path specified
        by the predecessor matrix.  If no path exists, the distance is zero.

    Notes
    -----
    The predecessor matrix is of the form returned by
    `shortest_path`.  Row i of the predecessor matrix contains
    information on the shortest paths from point i: each entry
    predecessors[i, j] gives the index of the previous node in the path from
    point i to point j.  If no path exists between point i and j, then
    predecessors[i, j] = -9999

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import construct_dist_matrix

    >>> graph = [
    ... [0, 1, 2, 0],
    ... [0, 0, 0, 1],
    ... [0, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 3)	1
      (2, 3)	3

    >>> pred = np.array([[-9999, 0, 0, 2],
    ...                  [1, -9999, 0, 1],
    ...                  [2, 0, -9999, 2],
    ...                  [1, 3, 3, -9999]], dtype=np.int32)

    >>> construct_dist_matrix(graph=graph, predecessors=pred, directed=False)
    array([[0., 1., 2., 5.],
           [1., 0., 3., 1.],
           [2., 3., 0., 3.],
           [2., 1., 3., 0.]])

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