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mplug_owl2/lib/python3.10/site-packages/networkx/linalg/attrmatrix.py

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+"""
+Functions for constructing matrix-like objects from graph attributes.
+"""
+
+import networkx as nx
+
+__all__ = ["attr_matrix", "attr_sparse_matrix"]
+
+
+def _node_value(G, node_attr):
+    """Returns a function that returns a value from G.nodes[u].
+
+    We return a function expecting a node as its sole argument. Then, in the
+    simplest scenario, the returned function will return G.nodes[u][node_attr].
+    However, we also handle the case when `node_attr` is None or when it is a
+    function itself.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph
+
+    node_attr : {None, str, callable}
+        Specification of how the value of the node attribute should be obtained
+        from the node attribute dictionary.
+
+    Returns
+    -------
+    value : function
+        A function expecting a node as its sole argument. The function will
+        returns a value from G.nodes[u] that depends on `edge_attr`.
+
+    """
+    if node_attr is None:
+
+        def value(u):
+            return u
+
+    elif not callable(node_attr):
+        # assume it is a key for the node attribute dictionary
+        def value(u):
+            return G.nodes[u][node_attr]
+
+    else:
+        # Advanced:  Allow users to specify something else.
+        #
+        # For example,
+        #     node_attr = lambda u: G.nodes[u].get('size', .5) * 3
+        #
+        value = node_attr
+
+    return value
+
+
+def _edge_value(G, edge_attr):
+    """Returns a function that returns a value from G[u][v].
+
+    Suppose there exists an edge between u and v.  Then we return a function
+    expecting u and v as arguments.  For Graph and DiGraph, G[u][v] is
+    the edge attribute dictionary, and the function (essentially) returns
+    G[u][v][edge_attr].  However, we also handle cases when `edge_attr` is None
+    and when it is a function itself. For MultiGraph and MultiDiGraph, G[u][v]
+    is a dictionary of all edges between u and v.  In this case, the returned
+    function sums the value of `edge_attr` for every edge between u and v.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    edge_attr : {None, str, callable}
+        Specification of how the value of the edge attribute should be obtained
+        from the edge attribute dictionary, G[u][v].  For multigraphs, G[u][v]
+        is a dictionary of all the edges between u and v.  This allows for
+        special treatment of multiedges.
+
+    Returns
+    -------
+    value : function
+        A function expecting two nodes as parameters. The nodes should
+        represent the from- and to- node of an edge. The function will
+        return a value from G[u][v] that depends on `edge_attr`.
+
+    """
+
+    if edge_attr is None:
+        # topological count of edges
+
+        if G.is_multigraph():
+
+            def value(u, v):
+                return len(G[u][v])
+
+        else:
+
+            def value(u, v):
+                return 1
+
+    elif not callable(edge_attr):
+        # assume it is a key for the edge attribute dictionary
+
+        if edge_attr == "weight":
+            # provide a default value
+            if G.is_multigraph():
+
+                def value(u, v):
+                    return sum(d.get(edge_attr, 1) for d in G[u][v].values())
+
+            else:
+
+                def value(u, v):
+                    return G[u][v].get(edge_attr, 1)
+
+        else:
+            # otherwise, the edge attribute MUST exist for each edge
+            if G.is_multigraph():
+
+                def value(u, v):
+                    return sum(d[edge_attr] for d in G[u][v].values())
+
+            else:
+
+                def value(u, v):
+                    return G[u][v][edge_attr]
+
+    else:
+        # Advanced:  Allow users to specify something else.
+        #
+        # Alternative default value:
+        #     edge_attr = lambda u,v: G[u][v].get('thickness', .5)
+        #
+        # Function on an attribute:
+        #     edge_attr = lambda u,v: abs(G[u][v]['weight'])
+        #
+        # Handle Multi(Di)Graphs differently:
+        #     edge_attr = lambda u,v: numpy.prod([d['size'] for d in G[u][v].values()])
+        #
+        # Ignore multiple edges
+        #     edge_attr = lambda u,v: 1 if len(G[u][v]) else 0
+        #
+        value = edge_attr
+
+    return value
+
+
+@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
+def attr_matrix(
+    G,
+    edge_attr=None,
+    node_attr=None,
+    normalized=False,
+    rc_order=None,
+    dtype=None,
+    order=None,
+):
+    """Returns the attribute matrix using attributes from `G` as a numpy array.
+
+    If only `G` is passed in, then the adjacency matrix is constructed.
+
+    Let A be a discrete set of values for the node attribute `node_attr`. Then
+    the elements of A represent the rows and columns of the constructed matrix.
+    Now, iterate through every edge e=(u,v) in `G` and consider the value
+    of the edge attribute `edge_attr`.  If ua and va are the values of the
+    node attribute `node_attr` for u and v, respectively, then the value of
+    the edge attribute is added to the matrix element at (ua, va).
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the attribute matrix.
+
+    edge_attr : str, optional
+        Each element of the matrix represents a running total of the
+        specified edge attribute for edges whose node attributes correspond
+        to the rows/cols of the matrix. The attribute must be present for
+        all edges in the graph. If no attribute is specified, then we
+        just count the number of edges whose node attributes correspond
+        to the matrix element.
+
+    node_attr : str, optional
+        Each row and column in the matrix represents a particular value
+        of the node attribute.  The attribute must be present for all nodes
+        in the graph. Note, the values of this attribute should be reliably
+        hashable. So, float values are not recommended. If no attribute is
+        specified, then the rows and columns will be the nodes of the graph.
+
+    normalized : bool, optional
+        If True, then each row is normalized by the summation of its values.
+
+    rc_order : list, optional
+        A list of the node attribute values. This list specifies the ordering
+        of rows and columns of the array. If no ordering is provided, then
+        the ordering will be random (and also, a return value).
+
+    Other Parameters
+    ----------------
+    dtype : NumPy data-type, optional
+        A valid NumPy dtype used to initialize the array. Keep in mind certain
+        dtypes can yield unexpected results if the array is to be normalized.
+        The parameter is passed to numpy.zeros(). If unspecified, the NumPy
+        default is used.
+
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. This parameter is passed to
+        numpy.zeros(). If unspecified, the NumPy default is used.
+
+    Returns
+    -------
+    M : 2D NumPy ndarray
+        The attribute matrix.
+
+    ordering : list
+        If `rc_order` was specified, then only the attribute matrix is returned.
+        However, if `rc_order` was None, then the ordering used to construct
+        the matrix is returned as well.
+
+    Examples
+    --------
+    Construct an adjacency matrix:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(0, 1, thickness=1, weight=3)
+    >>> G.add_edge(0, 2, thickness=2)
+    >>> G.add_edge(1, 2, thickness=3)
+    >>> nx.attr_matrix(G, rc_order=[0, 1, 2])
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]])
+
+    Alternatively, we can obtain the matrix describing edge thickness.
+
+    >>> nx.attr_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
+    array([[0., 1., 2.],
+           [1., 0., 3.],
+           [2., 3., 0.]])
+
+    We can also color the nodes and ask for the probability distribution over
+    all edges (u,v) describing:
+
+        Pr(v has color Y | u has color X)
+
+    >>> G.nodes[0]["color"] = "red"
+    >>> G.nodes[1]["color"] = "red"
+    >>> G.nodes[2]["color"] = "blue"
+    >>> rc = ["red", "blue"]
+    >>> nx.attr_matrix(G, node_attr="color", normalized=True, rc_order=rc)
+    array([[0.33333333, 0.66666667],
+           [1.        , 0.        ]])
+
+    For example, the above tells us that for all edges (u,v):
+
+        Pr( v is red  | u is red)  = 1/3
+        Pr( v is blue | u is red)  = 2/3
+
+        Pr( v is red  | u is blue) = 1
+        Pr( v is blue | u is blue) = 0
+
+    Finally, we can obtain the total weights listed by the node colors.
+
+    >>> nx.attr_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
+    array([[3., 2.],
+           [2., 0.]])
+
+    Thus, the total weight over all edges (u,v) with u and v having colors:
+
+        (red, red)   is 3   # the sole contribution is from edge (0,1)
+        (red, blue)  is 2   # contributions from edges (0,2) and (1,2)
+        (blue, red)  is 2   # same as (red, blue) since graph is undirected
+        (blue, blue) is 0   # there are no edges with blue endpoints
+
+    """
+    import numpy as np
+
+    edge_value = _edge_value(G, edge_attr)
+    node_value = _node_value(G, node_attr)
+
+    if rc_order is None:
+        ordering = list({node_value(n) for n in G})
+    else:
+        ordering = rc_order
+
+    N = len(ordering)
+    undirected = not G.is_directed()
+    index = dict(zip(ordering, range(N)))
+    M = np.zeros((N, N), dtype=dtype, order=order)
+
+    seen = set()
+    for u, nbrdict in G.adjacency():
+        for v in nbrdict:
+            # Obtain the node attribute values.
+            i, j = index[node_value(u)], index[node_value(v)]
+            if v not in seen:
+                M[i, j] += edge_value(u, v)
+                if undirected:
+                    M[j, i] = M[i, j]
+
+        if undirected:
+            seen.add(u)
+
+    if normalized:
+        M /= M.sum(axis=1).reshape((N, 1))
+
+    if rc_order is None:
+        return M, ordering
+    else:
+        return M
+
+
+@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
+def attr_sparse_matrix(
+    G, edge_attr=None, node_attr=None, normalized=False, rc_order=None, dtype=None
+):
+    """Returns a SciPy sparse array using attributes from G.
+
+    If only `G` is passed in, then the adjacency matrix is constructed.
+
+    Let A be a discrete set of values for the node attribute `node_attr`. Then
+    the elements of A represent the rows and columns of the constructed matrix.
+    Now, iterate through every edge e=(u,v) in `G` and consider the value
+    of the edge attribute `edge_attr`.  If ua and va are the values of the
+    node attribute `node_attr` for u and v, respectively, then the value of
+    the edge attribute is added to the matrix element at (ua, va).
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the NumPy matrix.
+
+    edge_attr : str, optional
+        Each element of the matrix represents a running total of the
+        specified edge attribute for edges whose node attributes correspond
+        to the rows/cols of the matrix. The attribute must be present for
+        all edges in the graph. If no attribute is specified, then we
+        just count the number of edges whose node attributes correspond
+        to the matrix element.
+
+    node_attr : str, optional
+        Each row and column in the matrix represents a particular value
+        of the node attribute.  The attribute must be present for all nodes
+        in the graph. Note, the values of this attribute should be reliably
+        hashable. So, float values are not recommended. If no attribute is
+        specified, then the rows and columns will be the nodes of the graph.
+
+    normalized : bool, optional
+        If True, then each row is normalized by the summation of its values.
+
+    rc_order : list, optional
+        A list of the node attribute values. This list specifies the ordering
+        of rows and columns of the array. If no ordering is provided, then
+        the ordering will be random (and also, a return value).
+
+    Other Parameters
+    ----------------
+    dtype : NumPy data-type, optional
+        A valid NumPy dtype used to initialize the array. Keep in mind certain
+        dtypes can yield unexpected results if the array is to be normalized.
+        The parameter is passed to numpy.zeros(). If unspecified, the NumPy
+        default is used.
+
+    Returns
+    -------
+    M : SciPy sparse array
+        The attribute matrix.
+
+    ordering : list
+        If `rc_order` was specified, then only the matrix is returned.
+        However, if `rc_order` was None, then the ordering used to construct
+        the matrix is returned as well.
+
+    Examples
+    --------
+    Construct an adjacency matrix:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(0, 1, thickness=1, weight=3)
+    >>> G.add_edge(0, 2, thickness=2)
+    >>> G.add_edge(1, 2, thickness=3)
+    >>> M = nx.attr_sparse_matrix(G, rc_order=[0, 1, 2])
+    >>> M.toarray()
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]])
+
+    Alternatively, we can obtain the matrix describing edge thickness.
+
+    >>> M = nx.attr_sparse_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
+    >>> M.toarray()
+    array([[0., 1., 2.],
+           [1., 0., 3.],
+           [2., 3., 0.]])
+
+    We can also color the nodes and ask for the probability distribution over
+    all edges (u,v) describing:
+
+        Pr(v has color Y | u has color X)
+
+    >>> G.nodes[0]["color"] = "red"
+    >>> G.nodes[1]["color"] = "red"
+    >>> G.nodes[2]["color"] = "blue"
+    >>> rc = ["red", "blue"]
+    >>> M = nx.attr_sparse_matrix(G, node_attr="color", normalized=True, rc_order=rc)
+    >>> M.toarray()
+    array([[0.33333333, 0.66666667],
+           [1.        , 0.        ]])
+
+    For example, the above tells us that for all edges (u,v):
+
+        Pr( v is red  | u is red)  = 1/3
+        Pr( v is blue | u is red)  = 2/3
+
+        Pr( v is red  | u is blue) = 1
+        Pr( v is blue | u is blue) = 0
+
+    Finally, we can obtain the total weights listed by the node colors.
+
+    >>> M = nx.attr_sparse_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
+    >>> M.toarray()
+    array([[3., 2.],
+           [2., 0.]])
+
+    Thus, the total weight over all edges (u,v) with u and v having colors:
+
+        (red, red)   is 3   # the sole contribution is from edge (0,1)
+        (red, blue)  is 2   # contributions from edges (0,2) and (1,2)
+        (blue, red)  is 2   # same as (red, blue) since graph is undirected
+        (blue, blue) is 0   # there are no edges with blue endpoints
+
+    """
+    import numpy as np
+    import scipy as sp
+
+    edge_value = _edge_value(G, edge_attr)
+    node_value = _node_value(G, node_attr)
+
+    if rc_order is None:
+        ordering = list({node_value(n) for n in G})
+    else:
+        ordering = rc_order
+
+    N = len(ordering)
+    undirected = not G.is_directed()
+    index = dict(zip(ordering, range(N)))
+    M = sp.sparse.lil_array((N, N), dtype=dtype)
+
+    seen = set()
+    for u, nbrdict in G.adjacency():
+        for v in nbrdict:
+            # Obtain the node attribute values.
+            i, j = index[node_value(u)], index[node_value(v)]
+            if v not in seen:
+                M[i, j] += edge_value(u, v)
+                if undirected:
+                    M[j, i] = M[i, j]
+
+        if undirected:
+            seen.add(u)
+
+    if normalized:
+        M *= 1 / M.sum(axis=1)[:, np.newaxis]  # in-place mult preserves sparse
+
+    if rc_order is None:
+        return M, ordering
+    else:
+        return M

mplug_owl2/lib/python3.10/site-packages/networkx/linalg/spectrum.py

@@ -0,0 +1,186 @@
+"""
+Eigenvalue spectrum of graphs.
+"""
+
+import networkx as nx
+
+__all__ = [
+    "laplacian_spectrum",
+    "adjacency_spectrum",
+    "modularity_spectrum",
+    "normalized_laplacian_spectrum",
+    "bethe_hessian_spectrum",
+]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def laplacian_spectrum(G, weight="weight"):
+    """Returns eigenvalues of the Laplacian of G
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    Notes
+    -----
+    For MultiGraph/MultiDiGraph, the edges weights are summed.
+    See :func:`~networkx.convert_matrix.to_numpy_array` for other options.
+
+    See Also
+    --------
+    laplacian_matrix
+
+    Examples
+    --------
+    The multiplicity of 0 as an eigenvalue of the laplacian matrix is equal
+    to the number of connected components of G.
+
+    >>> G = nx.Graph()  # Create a graph with 5 nodes and 3 connected components
+    >>> G.add_nodes_from(range(5))
+    >>> G.add_edges_from([(0, 2), (3, 4)])
+    >>> nx.laplacian_spectrum(G)
+    array([0., 0., 0., 2., 2.])
+
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvalsh(nx.laplacian_matrix(G, weight=weight).todense())
+
+
+@nx._dispatchable(edge_attrs="weight")
+def normalized_laplacian_spectrum(G, weight="weight"):
+    """Return eigenvalues of the normalized Laplacian of G
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    Notes
+    -----
+    For MultiGraph/MultiDiGraph, the edges weights are summed.
+    See to_numpy_array for other options.
+
+    See Also
+    --------
+    normalized_laplacian_matrix
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvalsh(
+        nx.normalized_laplacian_matrix(G, weight=weight).todense()
+    )
+
+
+@nx._dispatchable(edge_attrs="weight")
+def adjacency_spectrum(G, weight="weight"):
+    """Returns eigenvalues of the adjacency matrix of G.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    Notes
+    -----
+    For MultiGraph/MultiDiGraph, the edges weights are summed.
+    See to_numpy_array for other options.
+
+    See Also
+    --------
+    adjacency_matrix
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvals(nx.adjacency_matrix(G, weight=weight).todense())
+
+
+@nx._dispatchable
+def modularity_spectrum(G):
+    """Returns eigenvalues of the modularity matrix of G.
+
+    Parameters
+    ----------
+    G : Graph
+       A NetworkX Graph or DiGraph
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    See Also
+    --------
+    modularity_matrix
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, "Modularity and community structure in networks",
+       Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
+    """
+    import scipy as sp
+
+    if G.is_directed():
+        return sp.linalg.eigvals(nx.directed_modularity_matrix(G))
+    else:
+        return sp.linalg.eigvals(nx.modularity_matrix(G))
+
+
+@nx._dispatchable
+def bethe_hessian_spectrum(G, r=None):
+    """Returns eigenvalues of the Bethe Hessian matrix of G.
+
+    Parameters
+    ----------
+    G : Graph
+       A NetworkX Graph or DiGraph
+
+    r : float
+       Regularizer parameter
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    See Also
+    --------
+    bethe_hessian_matrix
+
+    References
+    ----------
+    .. [1] A. Saade, F. Krzakala and L. Zdeborová
+       "Spectral clustering of graphs with the bethe hessian",
+       Advances in Neural Information Processing Systems. 2014.
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvalsh(nx.bethe_hessian_matrix(G, r).todense())

mplug_owl2/lib/python3.10/site-packages/networkx/linalg/tests/test_algebraic_connectivity.py

@@ -0,0 +1,402 @@
+from math import sqrt
+
+import pytest
+
+np = pytest.importorskip("numpy")
+
+
+import networkx as nx
+
+methods = ("tracemin_pcg", "tracemin_lu", "lanczos", "lobpcg")
+
+
+def test_algebraic_connectivity_tracemin_chol():
+    """Test that "tracemin_chol" raises an exception."""
+    pytest.importorskip("scipy")
+    G = nx.barbell_graph(5, 4)
+    with pytest.raises(nx.NetworkXError):
+        nx.algebraic_connectivity(G, method="tracemin_chol")
+
+
+def test_fiedler_vector_tracemin_chol():
+    """Test that "tracemin_chol" raises an exception."""
+    pytest.importorskip("scipy")
+    G = nx.barbell_graph(5, 4)
+    with pytest.raises(nx.NetworkXError):
+        nx.fiedler_vector(G, method="tracemin_chol")
+
+
+def test_spectral_ordering_tracemin_chol():
+    """Test that "tracemin_chol" raises an exception."""
+    pytest.importorskip("scipy")
+    G = nx.barbell_graph(5, 4)
+    with pytest.raises(nx.NetworkXError):
+        nx.spectral_ordering(G, method="tracemin_chol")
+
+
+def test_fiedler_vector_tracemin_unknown():
+    """Test that "tracemin_unknown" raises an exception."""
+    pytest.importorskip("scipy")
+    G = nx.barbell_graph(5, 4)
+    L = nx.laplacian_matrix(G)
+    X = np.asarray(np.random.normal(size=(1, L.shape[0]))).T
+    with pytest.raises(nx.NetworkXError, match="Unknown linear system solver"):
+        nx.linalg.algebraicconnectivity._tracemin_fiedler(
+            L, X, normalized=False, tol=1e-8, method="tracemin_unknown"
+        )
+
+
+def test_spectral_bisection():
+    pytest.importorskip("scipy")
+    G = nx.barbell_graph(3, 0)
+    C = nx.spectral_bisection(G)
+    assert C == ({0, 1, 2}, {3, 4, 5})
+
+    mapping = dict(enumerate("badfec"))
+    G = nx.relabel_nodes(G, mapping)
+    C = nx.spectral_bisection(G)
+    assert C == (
+        {mapping[0], mapping[1], mapping[2]},
+        {mapping[3], mapping[4], mapping[5]},
+    )
+
+
+def check_eigenvector(A, l, x):
+    nx = np.linalg.norm(x)
+    # Check zeroness.
+    assert nx != pytest.approx(0, abs=1e-07)
+    y = A @ x
+    ny = np.linalg.norm(y)
+    # Check collinearity.
+    assert x @ y == pytest.approx(nx * ny, abs=1e-7)
+    # Check eigenvalue.
+    assert ny == pytest.approx(l * nx, abs=1e-7)
+
+
+class TestAlgebraicConnectivity:
+    @pytest.mark.parametrize("method", methods)
+    def test_directed(self, method):
+        G = nx.DiGraph()
+        pytest.raises(
+            nx.NetworkXNotImplemented, nx.algebraic_connectivity, G, method=method
+        )
+        pytest.raises(nx.NetworkXNotImplemented, nx.fiedler_vector, G, method=method)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_null_and_singleton(self, method):
+        G = nx.Graph()
+        pytest.raises(nx.NetworkXError, nx.algebraic_connectivity, G, method=method)
+        pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
+        G.add_edge(0, 0)
+        pytest.raises(nx.NetworkXError, nx.algebraic_connectivity, G, method=method)
+        pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_disconnected(self, method):
+        G = nx.Graph()
+        G.add_nodes_from(range(2))
+        assert nx.algebraic_connectivity(G) == 0
+        pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
+        G.add_edge(0, 1, weight=0)
+        assert nx.algebraic_connectivity(G) == 0
+        pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method=method)
+
+    def test_unrecognized_method(self):
+        pytest.importorskip("scipy")
+        G = nx.path_graph(4)
+        pytest.raises(nx.NetworkXError, nx.algebraic_connectivity, G, method="unknown")
+        pytest.raises(nx.NetworkXError, nx.fiedler_vector, G, method="unknown")
+
+    @pytest.mark.parametrize("method", methods)
+    def test_two_nodes(self, method):
+        pytest.importorskip("scipy")
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=1)
+        A = nx.laplacian_matrix(G)
+        assert nx.algebraic_connectivity(G, tol=1e-12, method=method) == pytest.approx(
+            2, abs=1e-7
+        )
+        x = nx.fiedler_vector(G, tol=1e-12, method=method)
+        check_eigenvector(A, 2, x)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_two_nodes_multigraph(self, method):
+        pytest.importorskip("scipy")
+        G = nx.MultiGraph()
+        G.add_edge(0, 0, spam=1e8)
+        G.add_edge(0, 1, spam=1)
+        G.add_edge(0, 1, spam=-2)
+        A = -3 * nx.laplacian_matrix(G, weight="spam")
+        assert nx.algebraic_connectivity(
+            G, weight="spam", tol=1e-12, method=method
+        ) == pytest.approx(6, abs=1e-7)
+        x = nx.fiedler_vector(G, weight="spam", tol=1e-12, method=method)
+        check_eigenvector(A, 6, x)
+
+    def test_abbreviation_of_method(self):
+        pytest.importorskip("scipy")
+        G = nx.path_graph(8)
+        A = nx.laplacian_matrix(G)
+        sigma = 2 - sqrt(2 + sqrt(2))
+        ac = nx.algebraic_connectivity(G, tol=1e-12, method="tracemin")
+        assert ac == pytest.approx(sigma, abs=1e-7)
+        x = nx.fiedler_vector(G, tol=1e-12, method="tracemin")
+        check_eigenvector(A, sigma, x)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_path(self, method):
+        pytest.importorskip("scipy")
+        G = nx.path_graph(8)
+        A = nx.laplacian_matrix(G)
+        sigma = 2 - sqrt(2 + sqrt(2))
+        ac = nx.algebraic_connectivity(G, tol=1e-12, method=method)
+        assert ac == pytest.approx(sigma, abs=1e-7)
+        x = nx.fiedler_vector(G, tol=1e-12, method=method)
+        check_eigenvector(A, sigma, x)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_problematic_graph_issue_2381(self, method):
+        pytest.importorskip("scipy")
+        G = nx.path_graph(4)
+        G.add_edges_from([(4, 2), (5, 1)])
+        A = nx.laplacian_matrix(G)
+        sigma = 0.438447187191
+        ac = nx.algebraic_connectivity(G, tol=1e-12, method=method)
+        assert ac == pytest.approx(sigma, abs=1e-7)
+        x = nx.fiedler_vector(G, tol=1e-12, method=method)
+        check_eigenvector(A, sigma, x)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_cycle(self, method):
+        pytest.importorskip("scipy")
+        G = nx.cycle_graph(8)
+        A = nx.laplacian_matrix(G)
+        sigma = 2 - sqrt(2)
+        ac = nx.algebraic_connectivity(G, tol=1e-12, method=method)
+        assert ac == pytest.approx(sigma, abs=1e-7)
+        x = nx.fiedler_vector(G, tol=1e-12, method=method)
+        check_eigenvector(A, sigma, x)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_seed_argument(self, method):
+        pytest.importorskip("scipy")
+        G = nx.cycle_graph(8)
+        A = nx.laplacian_matrix(G)
+        sigma = 2 - sqrt(2)
+        ac = nx.algebraic_connectivity(G, tol=1e-12, method=method, seed=1)
+        assert ac == pytest.approx(sigma, abs=1e-7)
+        x = nx.fiedler_vector(G, tol=1e-12, method=method, seed=1)
+        check_eigenvector(A, sigma, x)
+
+    @pytest.mark.parametrize(
+        ("normalized", "sigma", "laplacian_fn"),
+        (
+            (False, 0.2434017461399311, nx.laplacian_matrix),
+            (True, 0.08113391537997749, nx.normalized_laplacian_matrix),
+        ),
+    )
+    @pytest.mark.parametrize("method", methods)
+    def test_buckminsterfullerene(self, normalized, sigma, laplacian_fn, method):
+        pytest.importorskip("scipy")
+        G = nx.Graph(
+            [
+                (1, 10),
+                (1, 41),
+                (1, 59),
+                (2, 12),
+                (2, 42),
+                (2, 60),
+                (3, 6),
+                (3, 43),
+                (3, 57),
+                (4, 8),
+                (4, 44),
+                (4, 58),
+                (5, 13),
+                (5, 56),
+                (5, 57),
+                (6, 10),
+                (6, 31),
+                (7, 14),
+                (7, 56),
+                (7, 58),
+                (8, 12),
+                (8, 32),
+                (9, 23),
+                (9, 53),
+                (9, 59),
+                (10, 15),
+                (11, 24),
+                (11, 53),
+                (11, 60),
+                (12, 16),
+                (13, 14),
+                (13, 25),
+                (14, 26),
+                (15, 27),
+                (15, 49),
+                (16, 28),
+                (16, 50),
+                (17, 18),
+                (17, 19),
+                (17, 54),
+                (18, 20),
+                (18, 55),
+                (19, 23),
+                (19, 41),
+                (20, 24),
+                (20, 42),
+                (21, 31),
+                (21, 33),
+                (21, 57),
+                (22, 32),
+                (22, 34),
+                (22, 58),
+                (23, 24),
+                (25, 35),
+                (25, 43),
+                (26, 36),
+                (26, 44),
+                (27, 51),
+                (27, 59),
+                (28, 52),
+                (28, 60),
+                (29, 33),
+                (29, 34),
+                (29, 56),
+                (30, 51),
+                (30, 52),
+                (30, 53),
+                (31, 47),
+                (32, 48),
+                (33, 45),
+                (34, 46),
+                (35, 36),
+                (35, 37),
+                (36, 38),
+                (37, 39),
+                (37, 49),
+                (38, 40),
+                (38, 50),
+                (39, 40),
+                (39, 51),
+                (40, 52),
+                (41, 47),
+                (42, 48),
+                (43, 49),
+                (44, 50),
+                (45, 46),
+                (45, 54),
+                (46, 55),
+                (47, 54),
+                (48, 55),
+            ]
+        )
+        A = laplacian_fn(G)
+        try:
+            assert nx.algebraic_connectivity(
+                G, normalized=normalized, tol=1e-12, method=method
+            ) == pytest.approx(sigma, abs=1e-7)
+            x = nx.fiedler_vector(G, normalized=normalized, tol=1e-12, method=method)
+            check_eigenvector(A, sigma, x)
+        except nx.NetworkXError as err:
+            if err.args not in (
+                ("Cholesky solver unavailable.",),
+                ("LU solver unavailable.",),
+            ):
+                raise
+
+
+class TestSpectralOrdering:
+    _graphs = (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
+
+    @pytest.mark.parametrize("graph", _graphs)
+    def test_nullgraph(self, graph):
+        G = graph()
+        pytest.raises(nx.NetworkXError, nx.spectral_ordering, G)
+
+    @pytest.mark.parametrize("graph", _graphs)
+    def test_singleton(self, graph):
+        G = graph()
+        G.add_node("x")
+        assert nx.spectral_ordering(G) == ["x"]
+        G.add_edge("x", "x", weight=33)
+        G.add_edge("x", "x", weight=33)
+        assert nx.spectral_ordering(G) == ["x"]
+
+    def test_unrecognized_method(self):
+        G = nx.path_graph(4)
+        pytest.raises(nx.NetworkXError, nx.spectral_ordering, G, method="unknown")
+
+    @pytest.mark.parametrize("method", methods)
+    def test_three_nodes(self, method):
+        pytest.importorskip("scipy")
+        G = nx.Graph()
+        G.add_weighted_edges_from([(1, 2, 1), (1, 3, 2), (2, 3, 1)], weight="spam")
+        order = nx.spectral_ordering(G, weight="spam", method=method)
+        assert set(order) == set(G)
+        assert {1, 3} in (set(order[:-1]), set(order[1:]))
+
+    @pytest.mark.parametrize("method", methods)
+    def test_three_nodes_multigraph(self, method):
+        pytest.importorskip("scipy")
+        G = nx.MultiDiGraph()
+        G.add_weighted_edges_from([(1, 2, 1), (1, 3, 2), (2, 3, 1), (2, 3, 2)])
+        order = nx.spectral_ordering(G, method=method)
+        assert set(order) == set(G)
+        assert {2, 3} in (set(order[:-1]), set(order[1:]))
+
+    @pytest.mark.parametrize("method", methods)
+    def test_path(self, method):
+        pytest.importorskip("scipy")
+        path = list(range(10))
+        np.random.shuffle(path)
+        G = nx.Graph()
+        nx.add_path(G, path)
+        order = nx.spectral_ordering(G, method=method)
+        assert order in [path, list(reversed(path))]
+
+    @pytest.mark.parametrize("method", methods)
+    def test_seed_argument(self, method):
+        pytest.importorskip("scipy")
+        path = list(range(10))
+        np.random.shuffle(path)
+        G = nx.Graph()
+        nx.add_path(G, path)
+        order = nx.spectral_ordering(G, method=method, seed=1)
+        assert order in [path, list(reversed(path))]
+
+    @pytest.mark.parametrize("method", methods)
+    def test_disconnected(self, method):
+        pytest.importorskip("scipy")
+        G = nx.Graph()
+        nx.add_path(G, range(0, 10, 2))
+        nx.add_path(G, range(1, 10, 2))
+        order = nx.spectral_ordering(G, method=method)
+        assert set(order) == set(G)
+        seqs = [
+            list(range(0, 10, 2)),
+            list(range(8, -1, -2)),
+            list(range(1, 10, 2)),
+            list(range(9, -1, -2)),
+        ]
+        assert order[:5] in seqs
+        assert order[5:] in seqs
+
+    @pytest.mark.parametrize(
+        ("normalized", "expected_order"),
+        (
+            (False, [[1, 2, 0, 3, 4, 5, 6, 9, 7, 8], [8, 7, 9, 6, 5, 4, 3, 0, 2, 1]]),
+            (True, [[1, 2, 3, 0, 4, 5, 9, 6, 7, 8], [8, 7, 6, 9, 5, 4, 0, 3, 2, 1]]),
+        ),
+    )
+    @pytest.mark.parametrize("method", methods)
+    def test_cycle(self, normalized, expected_order, method):
+        pytest.importorskip("scipy")
+        path = list(range(10))
+        G = nx.Graph()
+        nx.add_path(G, path, weight=5)
+        G.add_edge(path[-1], path[0], weight=1)
+        A = nx.laplacian_matrix(G).todense()
+        order = nx.spectral_ordering(G, normalized=normalized, method=method)
+        assert order in expected_order

mplug_owl2/lib/python3.10/site-packages/networkx/linalg/tests/test_attrmatrix.py

@@ -0,0 +1,108 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+
+import networkx as nx
+
+
+def test_attr_matrix():
+    G = nx.Graph()
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 2, thickness=2)
+    G.add_edge(1, 2, thickness=3)
+
+    def node_attr(u):
+        return G.nodes[u].get("size", 0.5) * 3
+
+    def edge_attr(u, v):
+        return G[u][v].get("thickness", 0.5)
+
+    M = nx.attr_matrix(G, edge_attr=edge_attr, node_attr=node_attr)
+    np.testing.assert_equal(M[0], np.array([[6.0]]))
+    assert M[1] == [1.5]
+
+
+def test_attr_matrix_directed():
+    G = nx.DiGraph()
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 2, thickness=2)
+    G.add_edge(1, 2, thickness=3)
+    M = nx.attr_matrix(G, rc_order=[0, 1, 2])
+    # fmt: off
+    data = np.array(
+        [[0., 1., 1.],
+         [0., 0., 1.],
+         [0., 0., 0.]]
+    )
+    # fmt: on
+    np.testing.assert_equal(M, np.array(data))
+
+
+def test_attr_matrix_multigraph():
+    G = nx.MultiGraph()
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 2, thickness=2)
+    G.add_edge(1, 2, thickness=3)
+    M = nx.attr_matrix(G, rc_order=[0, 1, 2])
+    # fmt: off
+    data = np.array(
+        [[0., 3., 1.],
+         [3., 0., 1.],
+         [1., 1., 0.]]
+    )
+    # fmt: on
+    np.testing.assert_equal(M, np.array(data))
+    M = nx.attr_matrix(G, edge_attr="weight", rc_order=[0, 1, 2])
+    # fmt: off
+    data = np.array(
+        [[0., 9., 1.],
+         [9., 0., 1.],
+         [1., 1., 0.]]
+    )
+    # fmt: on
+    np.testing.assert_equal(M, np.array(data))
+    M = nx.attr_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
+    # fmt: off
+    data = np.array(
+        [[0., 3., 2.],
+         [3., 0., 3.],
+         [2., 3., 0.]]
+    )
+    # fmt: on
+    np.testing.assert_equal(M, np.array(data))
+
+
+def test_attr_sparse_matrix():
+    pytest.importorskip("scipy")
+    G = nx.Graph()
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 2, thickness=2)
+    G.add_edge(1, 2, thickness=3)
+    M = nx.attr_sparse_matrix(G)
+    mtx = M[0]
+    data = np.ones((3, 3), float)
+    np.fill_diagonal(data, 0)
+    np.testing.assert_equal(mtx.todense(), np.array(data))
+    assert M[1] == [0, 1, 2]
+
+
+def test_attr_sparse_matrix_directed():
+    pytest.importorskip("scipy")
+    G = nx.DiGraph()
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 1, thickness=1, weight=3)
+    G.add_edge(0, 2, thickness=2)
+    G.add_edge(1, 2, thickness=3)
+    M = nx.attr_sparse_matrix(G, rc_order=[0, 1, 2])
+    # fmt: off
+    data = np.array(
+        [[0., 1., 1.],
+         [0., 0., 1.],
+         [0., 0., 0.]]
+    )
+    # fmt: on
+    np.testing.assert_equal(M.todense(), np.array(data))

mplug_owl2/lib/python3.10/site-packages/networkx/linalg/tests/test_graphmatrix.py

@@ -0,0 +1,276 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.generators.degree_seq import havel_hakimi_graph
+
+
+def test_incidence_matrix_simple():
+    deg = [3, 2, 2, 1, 0]
+    G = havel_hakimi_graph(deg)
+    deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
+    MG = nx.random_clustered_graph(deg, seed=42)
+
+    I = nx.incidence_matrix(G, dtype=int).todense()
+    # fmt: off
+    expected = np.array(
+        [[1, 1, 1, 0],
+         [0, 1, 0, 1],
+         [1, 0, 0, 1],
+         [0, 0, 1, 0],
+         [0, 0, 0, 0]]
+    )
+    # fmt: on
+    np.testing.assert_equal(I, expected)
+
+    I = nx.incidence_matrix(MG, dtype=int).todense()
+    # fmt: off
+    expected = np.array(
+        [[1, 0, 0, 0, 0, 0, 0],
+         [1, 0, 0, 0, 0, 0, 0],
+         [0, 1, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0],
+         [0, 1, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 1, 1, 0],
+         [0, 0, 0, 0, 0, 1, 1],
+         [0, 0, 0, 0, 1, 0, 1]]
+    )
+    # fmt: on
+    np.testing.assert_equal(I, expected)
+
+    with pytest.raises(NetworkXError):
+        nx.incidence_matrix(G, nodelist=[0, 1])
+
+
+class TestGraphMatrix:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = havel_hakimi_graph(deg)
+        # fmt: off
+        cls.OI = np.array(
+            [[-1, -1, -1, 0],
+             [1, 0, 0, -1],
+             [0, 1, 0, 1],
+             [0, 0, 1, 0],
+             [0, 0, 0, 0]]
+        )
+        cls.A = np.array(
+            [[0, 1, 1, 1, 0],
+             [1, 0, 1, 0, 0],
+             [1, 1, 0, 0, 0],
+             [1, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0]]
+        )
+        # fmt: on
+        cls.WG = havel_hakimi_graph(deg)
+        cls.WG.add_edges_from(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
+        )
+        # fmt: off
+        cls.WA = np.array(
+            [[0, 0.5, 0.5, 0.5, 0],
+             [0.5, 0, 0.5, 0, 0],
+             [0.5, 0.5, 0, 0, 0],
+             [0.5, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0]]
+        )
+        # fmt: on
+        cls.MG = nx.MultiGraph(cls.G)
+        cls.MG2 = cls.MG.copy()
+        cls.MG2.add_edge(0, 1)
+        # fmt: off
+        cls.MG2A = np.array(
+            [[0, 2, 1, 1, 0],
+             [2, 0, 1, 0, 0],
+             [1, 1, 0, 0, 0],
+             [1, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0]]
+        )
+        cls.MGOI = np.array(
+            [[-1, -1, -1, -1, 0],
+             [1, 1, 0, 0, -1],
+             [0, 0, 1, 0, 1],
+             [0, 0, 0, 1, 0],
+             [0, 0, 0, 0, 0]]
+        )
+        # fmt: on
+        cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {"weight": 8})])
+        cls.no_edges_A = np.array([[0, 0], [0, 0]])
+
+    def test_incidence_matrix(self):
+        "Conversion to incidence matrix"
+        I = nx.incidence_matrix(
+            self.G,
+            nodelist=sorted(self.G),
+            edgelist=sorted(self.G.edges()),
+            oriented=True,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, self.OI)
+
+        I = nx.incidence_matrix(
+            self.G,
+            nodelist=sorted(self.G),
+            edgelist=sorted(self.G.edges()),
+            oriented=False,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, np.abs(self.OI))
+
+        I = nx.incidence_matrix(
+            self.MG,
+            nodelist=sorted(self.MG),
+            edgelist=sorted(self.MG.edges()),
+            oriented=True,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, self.OI)
+
+        I = nx.incidence_matrix(
+            self.MG,
+            nodelist=sorted(self.MG),
+            edgelist=sorted(self.MG.edges()),
+            oriented=False,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, np.abs(self.OI))
+
+        I = nx.incidence_matrix(
+            self.MG2,
+            nodelist=sorted(self.MG2),
+            edgelist=sorted(self.MG2.edges()),
+            oriented=True,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, self.MGOI)
+
+        I = nx.incidence_matrix(
+            self.MG2,
+            nodelist=sorted(self.MG),
+            edgelist=sorted(self.MG2.edges()),
+            oriented=False,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, np.abs(self.MGOI))
+
+        I = nx.incidence_matrix(self.G, dtype=np.uint8)
+        assert I.dtype == np.uint8
+
+    def test_weighted_incidence_matrix(self):
+        I = nx.incidence_matrix(
+            self.WG,
+            nodelist=sorted(self.WG),
+            edgelist=sorted(self.WG.edges()),
+            oriented=True,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, self.OI)
+
+        I = nx.incidence_matrix(
+            self.WG,
+            nodelist=sorted(self.WG),
+            edgelist=sorted(self.WG.edges()),
+            oriented=False,
+            dtype=int,
+        ).todense()
+        np.testing.assert_equal(I, np.abs(self.OI))
+
+        # np.testing.assert_equal(nx.incidence_matrix(self.WG,oriented=True,
+        #                                  weight='weight').todense(),0.5*self.OI)
+        # np.testing.assert_equal(nx.incidence_matrix(self.WG,weight='weight').todense(),
+        #              np.abs(0.5*self.OI))
+        # np.testing.assert_equal(nx.incidence_matrix(self.WG,oriented=True,weight='other').todense(),
+        #              0.3*self.OI)
+
+        I = nx.incidence_matrix(
+            self.WG,
+            nodelist=sorted(self.WG),
+            edgelist=sorted(self.WG.edges()),
+            oriented=True,
+            weight="weight",
+        ).todense()
+        np.testing.assert_equal(I, 0.5 * self.OI)
+
+        I = nx.incidence_matrix(
+            self.WG,
+            nodelist=sorted(self.WG),
+            edgelist=sorted(self.WG.edges()),
+            oriented=False,
+            weight="weight",
+        ).todense()
+        np.testing.assert_equal(I, np.abs(0.5 * self.OI))
+
+        I = nx.incidence_matrix(
+            self.WG,
+            nodelist=sorted(self.WG),
+            edgelist=sorted(self.WG.edges()),
+            oriented=True,
+            weight="other",
+        ).todense()
+        np.testing.assert_equal(I, 0.3 * self.OI)
+
+        # WMG=nx.MultiGraph(self.WG)
+        # WMG.add_edge(0,1,weight=0.5,other=0.3)
+        # np.testing.assert_equal(nx.incidence_matrix(WMG,weight='weight').todense(),
+        #              np.abs(0.5*self.MGOI))
+        # np.testing.assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True).todense(),
+        #              0.5*self.MGOI)
+        # np.testing.assert_equal(nx.incidence_matrix(WMG,weight='other',oriented=True).todense(),
+        #              0.3*self.MGOI)
+
+        WMG = nx.MultiGraph(self.WG)
+        WMG.add_edge(0, 1, weight=0.5, other=0.3)
+
+        I = nx.incidence_matrix(
+            WMG,
+            nodelist=sorted(WMG),
+            edgelist=sorted(WMG.edges(keys=True)),
+            oriented=True,
+            weight="weight",
+        ).todense()
+        np.testing.assert_equal(I, 0.5 * self.MGOI)
+
+        I = nx.incidence_matrix(
+            WMG,
+            nodelist=sorted(WMG),
+            edgelist=sorted(WMG.edges(keys=True)),
+            oriented=False,
+            weight="weight",
+        ).todense()
+        np.testing.assert_equal(I, np.abs(0.5 * self.MGOI))
+
+        I = nx.incidence_matrix(
+            WMG,
+            nodelist=sorted(WMG),
+            edgelist=sorted(WMG.edges(keys=True)),
+            oriented=True,
+            weight="other",
+        ).todense()
+        np.testing.assert_equal(I, 0.3 * self.MGOI)
+
+    def test_adjacency_matrix(self):
+        "Conversion to adjacency matrix"
+        np.testing.assert_equal(nx.adjacency_matrix(self.G).todense(), self.A)
+        np.testing.assert_equal(nx.adjacency_matrix(self.MG).todense(), self.A)
+        np.testing.assert_equal(nx.adjacency_matrix(self.MG2).todense(), self.MG2A)
+        np.testing.assert_equal(
+            nx.adjacency_matrix(self.G, nodelist=[0, 1]).todense(), self.A[:2, :2]
+        )
+        np.testing.assert_equal(nx.adjacency_matrix(self.WG).todense(), self.WA)
+        np.testing.assert_equal(
+            nx.adjacency_matrix(self.WG, weight=None).todense(), self.A
+        )
+        np.testing.assert_equal(
+            nx.adjacency_matrix(self.MG2, weight=None).todense(), self.MG2A
+        )
+        np.testing.assert_equal(
+            nx.adjacency_matrix(self.WG, weight="other").todense(), 0.6 * self.WA
+        )
+        np.testing.assert_equal(
+            nx.adjacency_matrix(self.no_edges_G, nodelist=[1, 3]).todense(),
+            self.no_edges_A,
+        )

mplug_owl2/lib/python3.10/site-packages/networkx/linalg/tests/test_laplacian.py

@@ -0,0 +1,336 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.generators.degree_seq import havel_hakimi_graph
+from networkx.generators.expanders import margulis_gabber_galil_graph
+
+
+class TestLaplacian:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = havel_hakimi_graph(deg)
+        cls.WG = nx.Graph(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
+        )
+        cls.WG.add_node(4)
+        cls.MG = nx.MultiGraph(cls.G)
+
+        # Graph with clsloops
+        cls.Gsl = cls.G.copy()
+        for node in cls.Gsl.nodes():
+            cls.Gsl.add_edge(node, node)
+
+        # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+        # "Google's PageRank and Beyond".
+        cls.DiG = nx.DiGraph()
+        cls.DiG.add_edges_from(
+            (
+                (1, 2),
+                (1, 3),
+                (3, 1),
+                (3, 2),
+                (3, 5),
+                (4, 5),
+                (4, 6),
+                (5, 4),
+                (5, 6),
+                (6, 4),
+            )
+        )
+        cls.DiMG = nx.MultiDiGraph(cls.DiG)
+        cls.DiWG = nx.DiGraph(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.DiG.edges()
+        )
+        cls.DiGsl = cls.DiG.copy()
+        for node in cls.DiGsl.nodes():
+            cls.DiGsl.add_edge(node, node)
+
+    def test_laplacian(self):
+        "Graph Laplacian"
+        # fmt: off
+        NL = np.array([[ 3, -1, -1, -1,  0],
+                       [-1,  2, -1,  0,  0],
+                       [-1, -1,  2,  0,  0],
+                       [-1,  0,  0,  1,  0],
+                       [ 0,  0,  0,  0,  0]])
+        # fmt: on
+        WL = 0.5 * NL
+        OL = 0.3 * NL
+        # fmt: off
+        DiNL = np.array([[ 2, -1, -1,  0,  0,  0],
+                         [ 0,  0,  0,  0,  0,  0],
+                         [-1, -1,  3, -1,  0,  0],
+                         [ 0,  0,  0,  2, -1, -1],
+                         [ 0,  0,  0, -1,  2, -1],
+                         [ 0,  0,  0,  0, -1,  1]])
+        # fmt: on
+        DiWL = 0.5 * DiNL
+        DiOL = 0.3 * DiNL
+        np.testing.assert_equal(nx.laplacian_matrix(self.G).todense(), NL)
+        np.testing.assert_equal(nx.laplacian_matrix(self.MG).todense(), NL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.G, nodelist=[0, 1]).todense(),
+            np.array([[1, -1], [-1, 1]]),
+        )
+        np.testing.assert_equal(nx.laplacian_matrix(self.WG).todense(), WL)
+        np.testing.assert_equal(nx.laplacian_matrix(self.WG, weight=None).todense(), NL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.WG, weight="other").todense(), OL
+        )
+
+        np.testing.assert_equal(nx.laplacian_matrix(self.DiG).todense(), DiNL)
+        np.testing.assert_equal(nx.laplacian_matrix(self.DiMG).todense(), DiNL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.DiG, nodelist=[1, 2]).todense(),
+            np.array([[1, -1], [0, 0]]),
+        )
+        np.testing.assert_equal(nx.laplacian_matrix(self.DiWG).todense(), DiWL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.DiWG, weight=None).todense(), DiNL
+        )
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.DiWG, weight="other").todense(), DiOL
+        )
+
+    def test_normalized_laplacian(self):
+        "Generalized Graph Laplacian"
+        # fmt: off
+        G = np.array([[ 1.   , -0.408, -0.408, -0.577,  0.],
+                      [-0.408,  1.   , -0.5  ,  0.   ,  0.],
+                      [-0.408, -0.5  ,  1.   ,  0.   ,  0.],
+                      [-0.577,  0.   ,  0.   ,  1.   ,  0.],
+                      [ 0.   ,  0.   ,  0.   ,  0.   ,  0.]])
+        GL = np.array([[ 1.   , -0.408, -0.408, -0.577,  0.   ],
+                       [-0.408,  1.   , -0.5  ,  0.   ,  0.   ],
+                       [-0.408, -0.5  ,  1.   ,  0.   ,  0.   ],
+                       [-0.577,  0.   ,  0.   ,  1.   ,  0.   ],
+                       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   ]])
+        Lsl = np.array([[ 0.75  , -0.2887, -0.2887, -0.3536,  0.    ],
+                        [-0.2887,  0.6667, -0.3333,  0.    ,  0.    ],
+                        [-0.2887, -0.3333,  0.6667,  0.    ,  0.    ],
+                        [-0.3536,  0.    ,  0.    ,  0.5   ,  0.    ],
+                        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ]])
+
+        DiG = np.array([[ 1.    ,  0.    , -0.4082,  0.    ,  0.    ,  0.    ],
+                        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
+                        [-0.4082,  0.    ,  1.    ,  0.    , -0.4082,  0.    ],
+                        [ 0.    ,  0.    ,  0.    ,  1.    , -0.5   , -0.7071],
+                        [ 0.    ,  0.    ,  0.    , -0.5   ,  1.    , -0.7071],
+                        [ 0.    ,  0.    ,  0.    , -0.7071,  0.     , 1.    ]])
+        DiGL = np.array([[ 1.    ,  0.    , -0.4082,  0.    ,  0.    ,  0.    ],
+                         [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
+                         [-0.4082,  0.    ,  1.    , -0.4082,  0.    ,  0.    ],
+                         [ 0.    ,  0.    ,  0.    ,  1.    , -0.5   , -0.7071],
+                         [ 0.    ,  0.    ,  0.    , -0.5   ,  1.    , -0.7071],
+                         [ 0.    ,  0.    ,  0.    ,  0.    , -0.7071,  1.    ]])
+        DiLsl = np.array([[ 0.6667, -0.5774, -0.2887,  0.    ,  0.    ,  0.    ],
+                          [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
+                          [-0.2887, -0.5   ,  0.75  , -0.2887,  0.    ,  0.    ],
+                          [ 0.    ,  0.    ,  0.    ,  0.6667, -0.3333, -0.4082],
+                          [ 0.    ,  0.    ,  0.    , -0.3333,  0.6667, -0.4082],
+                          [ 0.    ,  0.    ,  0.    ,  0.    , -0.4082,  0.5   ]])
+        # fmt: on
+
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.G, nodelist=range(5)).todense(),
+            G,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.G).todense(), GL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.MG).todense(), GL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.WG).todense(), GL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.WG, weight="other").todense(),
+            GL,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.Gsl).todense(), Lsl, decimal=3
+        )
+
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(
+                self.DiG,
+                nodelist=range(1, 1 + 6),
+            ).todense(),
+            DiG,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiG).todense(), DiGL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiMG).todense(), DiGL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiWG).todense(), DiGL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiWG, weight="other").todense(),
+            DiGL,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiGsl).todense(), DiLsl, decimal=3
+        )
+
+
+def test_directed_laplacian():
+    "Directed Laplacian"
+    # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+    # "Google's PageRank and Beyond". The graph contains dangling nodes, so
+    # the pagerank random walk is selected by directed_laplacian
+    G = nx.DiGraph()
+    G.add_edges_from(
+        (
+            (1, 2),
+            (1, 3),
+            (3, 1),
+            (3, 2),
+            (3, 5),
+            (4, 5),
+            (4, 6),
+            (5, 4),
+            (5, 6),
+            (6, 4),
+        )
+    )
+    # fmt: off
+    GL = np.array([[ 0.9833, -0.2941, -0.3882, -0.0291, -0.0231, -0.0261],
+                   [-0.2941,  0.8333, -0.2339, -0.0536, -0.0589, -0.0554],
+                   [-0.3882, -0.2339,  0.9833, -0.0278, -0.0896, -0.0251],
+                   [-0.0291, -0.0536, -0.0278,  0.9833, -0.4878, -0.6675],
+                   [-0.0231, -0.0589, -0.0896, -0.4878,  0.9833, -0.2078],
+                   [-0.0261, -0.0554, -0.0251, -0.6675, -0.2078,  0.9833]])
+    # fmt: on
+    L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # Make the graph strongly connected, so we can use a random and lazy walk
+    G.add_edges_from(((2, 5), (6, 1)))
+    # fmt: off
+    GL = np.array([[ 1.    , -0.3062, -0.4714,  0.    ,  0.    , -0.3227],
+                   [-0.3062,  1.    , -0.1443,  0.    , -0.3162,  0.    ],
+                   [-0.4714, -0.1443,  1.    ,  0.    , -0.0913,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  1.    , -0.5   , -0.5   ],
+                   [ 0.    , -0.3162, -0.0913, -0.5   ,  1.    , -0.25  ],
+                   [-0.3227,  0.    ,  0.    , -0.5   , -0.25  ,  1.    ]])
+    # fmt: on
+    L = nx.directed_laplacian_matrix(
+        G, alpha=0.9, nodelist=sorted(G), walk_type="random"
+    )
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # fmt: off
+    GL = np.array([[ 0.5   , -0.1531, -0.2357,  0.    ,  0.    , -0.1614],
+                   [-0.1531,  0.5   , -0.0722,  0.    , -0.1581,  0.    ],
+                   [-0.2357, -0.0722,  0.5   ,  0.    , -0.0456,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  0.5   , -0.25  , -0.25  ],
+                   [ 0.    , -0.1581, -0.0456, -0.25  ,  0.5   , -0.125 ],
+                   [-0.1614,  0.    ,  0.    , -0.25  , -0.125 ,  0.5   ]])  
+    # fmt: on
+    L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G), walk_type="lazy")
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # Make a strongly connected periodic graph
+    G = nx.DiGraph()
+    G.add_edges_from(((1, 2), (2, 4), (4, 1), (1, 3), (3, 4)))
+    # fmt: off
+    GL = np.array([[ 0.5  , -0.176, -0.176, -0.25 ],
+                   [-0.176,  0.5  ,  0.   , -0.176],
+                   [-0.176,  0.   ,  0.5  , -0.176],
+                   [-0.25 , -0.176, -0.176,  0.5  ]])
+    # fmt: on
+    L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+
+def test_directed_combinatorial_laplacian():
+    "Directed combinatorial Laplacian"
+    # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+    # "Google's PageRank and Beyond". The graph contains dangling nodes, so
+    # the pagerank random walk is selected by directed_laplacian
+    G = nx.DiGraph()
+    G.add_edges_from(
+        (
+            (1, 2),
+            (1, 3),
+            (3, 1),
+            (3, 2),
+            (3, 5),
+            (4, 5),
+            (4, 6),
+            (5, 4),
+            (5, 6),
+            (6, 4),
+        )
+    )
+    # fmt: off
+    GL = np.array([[ 0.0366, -0.0132, -0.0153, -0.0034, -0.0020, -0.0027],
+                   [-0.0132,  0.0450, -0.0111, -0.0076, -0.0062, -0.0069],
+                   [-0.0153, -0.0111,  0.0408, -0.0035, -0.0083, -0.0027],
+                   [-0.0034, -0.0076, -0.0035,  0.3688, -0.1356, -0.2187],
+                   [-0.0020, -0.0062, -0.0083, -0.1356,  0.2026, -0.0505],
+                   [-0.0027, -0.0069, -0.0027, -0.2187, -0.0505,  0.2815]])
+    # fmt: on
+
+    L = nx.directed_combinatorial_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # Make the graph strongly connected, so we can use a random and lazy walk
+    G.add_edges_from(((2, 5), (6, 1)))
+
+    # fmt: off
+    GL = np.array([[ 0.1395, -0.0349, -0.0465,  0.    ,  0.    , -0.0581],
+                   [-0.0349,  0.093 , -0.0116,  0.    , -0.0465,  0.    ],
+                   [-0.0465, -0.0116,  0.0698,  0.    , -0.0116,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  0.2326, -0.1163, -0.1163],
+                   [ 0.    , -0.0465, -0.0116, -0.1163,  0.2326, -0.0581],
+                   [-0.0581,  0.    ,  0.    , -0.1163, -0.0581,  0.2326]])
+    # fmt: on
+
+    L = nx.directed_combinatorial_laplacian_matrix(
+        G, alpha=0.9, nodelist=sorted(G), walk_type="random"
+    )
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # fmt: off
+    GL = np.array([[ 0.0698, -0.0174, -0.0233,  0.    ,  0.    , -0.0291],
+                   [-0.0174,  0.0465, -0.0058,  0.    , -0.0233,  0.    ],
+                   [-0.0233, -0.0058,  0.0349,  0.    , -0.0058,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  0.1163, -0.0581, -0.0581],
+                   [ 0.    , -0.0233, -0.0058, -0.0581,  0.1163, -0.0291],
+                   [-0.0291,  0.    ,  0.    , -0.0581, -0.0291,  0.1163]])
+    # fmt: on
+
+    L = nx.directed_combinatorial_laplacian_matrix(
+        G, alpha=0.9, nodelist=sorted(G), walk_type="lazy"
+    )
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    E = nx.DiGraph(margulis_gabber_galil_graph(2))
+    L = nx.directed_combinatorial_laplacian_matrix(E)
+    # fmt: off
+    expected = np.array(
+        [[ 0.16666667, -0.08333333, -0.08333333,  0.        ],
+         [-0.08333333,  0.16666667,  0.        , -0.08333333],
+         [-0.08333333,  0.        ,  0.16666667, -0.08333333],
+         [ 0.        , -0.08333333, -0.08333333,  0.16666667]]
+    )
+    # fmt: on
+    np.testing.assert_almost_equal(L, expected, decimal=6)
+
+    with pytest.raises(nx.NetworkXError):
+        nx.directed_combinatorial_laplacian_matrix(G, walk_type="pagerank", alpha=100)
+    with pytest.raises(nx.NetworkXError):
+        nx.directed_combinatorial_laplacian_matrix(G, walk_type="silly")

mplug_owl2/lib/python3.10/site-packages/networkx/linalg/tests/test_modularity.py

@@ -0,0 +1,87 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.generators.degree_seq import havel_hakimi_graph
+
+
+class TestModularity:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = havel_hakimi_graph(deg)
+        # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+        # "Google's PageRank and Beyond". (Used for test_directed_laplacian)
+        cls.DG = nx.DiGraph()
+        cls.DG.add_edges_from(
+            (
+                (1, 2),
+                (1, 3),
+                (3, 1),
+                (3, 2),
+                (3, 5),
+                (4, 5),
+                (4, 6),
+                (5, 4),
+                (5, 6),
+                (6, 4),
+            )
+        )
+
+    def test_modularity(self):
+        "Modularity matrix"
+        # fmt: off
+        B = np.array([[-1.125,  0.25,  0.25,  0.625,  0.],
+                      [0.25, -0.5,  0.5, -0.25,  0.],
+                      [0.25,  0.5, -0.5, -0.25,  0.],
+                      [0.625, -0.25, -0.25, -0.125,  0.],
+                      [0.,  0.,  0.,  0.,  0.]])
+        # fmt: on
+
+        permutation = [4, 0, 1, 2, 3]
+        np.testing.assert_equal(nx.modularity_matrix(self.G), B)
+        np.testing.assert_equal(
+            nx.modularity_matrix(self.G, nodelist=permutation),
+            B[np.ix_(permutation, permutation)],
+        )
+
+    def test_modularity_weight(self):
+        "Modularity matrix with weights"
+        # fmt: off
+        B = np.array([[-1.125,  0.25,  0.25,  0.625,  0.],
+                      [0.25, -0.5,  0.5, -0.25,  0.],
+                      [0.25,  0.5, -0.5, -0.25,  0.],
+                      [0.625, -0.25, -0.25, -0.125,  0.],
+                      [0.,  0.,  0.,  0.,  0.]])
+        # fmt: on
+
+        G_weighted = self.G.copy()
+        for n1, n2 in G_weighted.edges():
+            G_weighted.edges[n1, n2]["weight"] = 0.5
+        # The following test would fail in networkx 1.1
+        np.testing.assert_equal(nx.modularity_matrix(G_weighted), B)
+        # The following test that the modularity matrix get rescaled accordingly
+        np.testing.assert_equal(
+            nx.modularity_matrix(G_weighted, weight="weight"), 0.5 * B
+        )
+
+    def test_directed_modularity(self):
+        "Directed Modularity matrix"
+        # fmt: off
+        B = np.array([[-0.2,  0.6,  0.8, -0.4, -0.4, -0.4],
+                      [0.,  0.,  0.,  0.,  0.,  0.],
+                      [0.7,  0.4, -0.3, -0.6,  0.4, -0.6],
+                      [-0.2, -0.4, -0.2, -0.4,  0.6,  0.6],
+                      [-0.2, -0.4, -0.2,  0.6, -0.4,  0.6],
+                      [-0.1, -0.2, -0.1,  0.8, -0.2, -0.2]])
+        # fmt: on
+        node_permutation = [5, 1, 2, 3, 4, 6]
+        idx_permutation = [4, 0, 1, 2, 3, 5]
+        mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG))
+        np.testing.assert_equal(mm, B)
+        np.testing.assert_equal(
+            nx.directed_modularity_matrix(self.DG, nodelist=node_permutation),
+            B[np.ix_(idx_permutation, idx_permutation)],
+        )

mplug_owl2/lib/python3.10/site-packages/networkx/linalg/tests/test_spectrum.py

@@ -0,0 +1,71 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.generators.degree_seq import havel_hakimi_graph
+
+
+class TestSpectrum:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = havel_hakimi_graph(deg)
+        cls.P = nx.path_graph(3)
+        cls.WG = nx.Graph(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
+        )
+        cls.WG.add_node(4)
+        cls.DG = nx.DiGraph()
+        nx.add_path(cls.DG, [0, 1, 2])
+
+    def test_laplacian_spectrum(self):
+        "Laplacian eigenvalues"
+        evals = np.array([0, 0, 1, 3, 4])
+        e = sorted(nx.laplacian_spectrum(self.G))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.laplacian_spectrum(self.WG, weight=None))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.laplacian_spectrum(self.WG))
+        np.testing.assert_almost_equal(e, 0.5 * evals)
+        e = sorted(nx.laplacian_spectrum(self.WG, weight="other"))
+        np.testing.assert_almost_equal(e, 0.3 * evals)
+
+    def test_normalized_laplacian_spectrum(self):
+        "Normalized Laplacian eigenvalues"
+        evals = np.array([0, 0, 0.7712864461218, 1.5, 1.7287135538781])
+        e = sorted(nx.normalized_laplacian_spectrum(self.G))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight=None))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight="other"))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_adjacency_spectrum(self):
+        "Adjacency eigenvalues"
+        evals = np.array([-np.sqrt(2), 0, np.sqrt(2)])
+        e = sorted(nx.adjacency_spectrum(self.P))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_modularity_spectrum(self):
+        "Modularity eigenvalues"
+        evals = np.array([-1.5, 0.0, 0.0])
+        e = sorted(nx.modularity_spectrum(self.P))
+        np.testing.assert_almost_equal(e, evals)
+        # Directed modularity eigenvalues
+        evals = np.array([-0.5, 0.0, 0.0])
+        e = sorted(nx.modularity_spectrum(self.DG))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_bethe_hessian_spectrum(self):
+        "Bethe Hessian eigenvalues"
+        evals = np.array([0.5 * (9 - np.sqrt(33)), 4, 0.5 * (9 + np.sqrt(33))])
+        e = sorted(nx.bethe_hessian_spectrum(self.P, r=2))
+        np.testing.assert_almost_equal(e, evals)
+        # Collapses back to Laplacian:
+        e1 = sorted(nx.bethe_hessian_spectrum(self.P, r=1))
+        e2 = sorted(nx.laplacian_spectrum(self.P))
+        np.testing.assert_almost_equal(e1, e2)

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/graphml.py

@@ -0,0 +1,1053 @@
+"""
+*******
+GraphML
+*******
+Read and write graphs in GraphML format.
+
+.. warning::
+
+    This parser uses the standard xml library present in Python, which is
+    insecure - see :external+python:mod:`xml` for additional information.
+    Only parse GraphML files you trust.
+
+This implementation does not support mixed graphs (directed and unidirected
+edges together), hyperedges, nested graphs, or ports.
+
+"GraphML is a comprehensive and easy-to-use file format for graphs. It
+consists of a language core to describe the structural properties of a
+graph and a flexible extension mechanism to add application-specific
+data. Its main features include support of
+
+    * directed, undirected, and mixed graphs,
+    * hypergraphs,
+    * hierarchical graphs,
+    * graphical representations,
+    * references to external data,
+    * application-specific attribute data, and
+    * light-weight parsers.
+
+Unlike many other file formats for graphs, GraphML does not use a
+custom syntax. Instead, it is based on XML and hence ideally suited as
+a common denominator for all kinds of services generating, archiving,
+or processing graphs."
+
+http://graphml.graphdrawing.org/
+
+Format
+------
+GraphML is an XML format.  See
+http://graphml.graphdrawing.org/specification.html for the specification and
+http://graphml.graphdrawing.org/primer/graphml-primer.html
+for examples.
+"""
+
+import warnings
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = [
+    "write_graphml",
+    "read_graphml",
+    "generate_graphml",
+    "write_graphml_xml",
+    "write_graphml_lxml",
+    "parse_graphml",
+    "GraphMLWriter",
+    "GraphMLReader",
+]
+
+
+@open_file(1, mode="wb")
+def write_graphml_xml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml(G, "test.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    writer.dump(path)
+
+
+@open_file(1, mode="wb")
+def write_graphml_lxml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    This function uses the LXML framework and should be faster than
+    the version using the xml library.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml_lxml(G, "fourpath.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    try:
+        import lxml.etree as lxmletree
+    except ImportError:
+        return write_graphml_xml(
+            G,
+            path,
+            encoding,
+            prettyprint,
+            infer_numeric_types,
+            named_key_ids,
+            edge_id_from_attribute,
+        )
+
+    writer = GraphMLWriterLxml(
+        path,
+        graph=G,
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.dump()
+
+
+def generate_graphml(
+    G,
+    encoding="utf-8",
+    prettyprint=True,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Generate GraphML lines for G
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> for line in nx.generate_graphml(G):  # doctest: +SKIP
+    ...     print(line)
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    yield from str(writer).splitlines()
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_graphml(path, node_type=str, edge_key_type=int, force_multigraph=False):
+    """Read graph in GraphML format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If parallel edges are present or `force_multigraph=True` then
+        a MultiGraph or MultiDiGraph is returned. Otherwise a Graph/DiGraph.
+        The returned graph is directed if the file indicates it should be.
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    Files with the yEd "yfiles" extension can be read. The type of the node's
+    shape is preserved in the `shape_type` node attribute.
+
+    yEd compressed files ("file.graphmlz" extension) can be read by renaming
+    the file to "file.graphml.gz".
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(path=path))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = b'<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        path.seek(0)
+        old_bytes = path.read()
+        new_bytes = old_bytes.replace(b"<graphml>", header)
+        glist = list(reader(string=new_bytes))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_graphml(
+    graphml_string, node_type=str, edge_key_type=int, force_multigraph=False
+):
+    """Read graph in GraphML format from string.
+
+    Parameters
+    ----------
+    graphml_string : string
+       String containing graphml information
+       (e.g., contents of a graphml file).
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If no parallel edges are found a Graph or DiGraph is returned.
+        Otherwise a MultiGraph or MultiDiGraph is returned.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> H = nx.parse_graphml(s)
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(string=graphml_string))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = '<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        new_string = graphml_string.replace("<graphml>", header)
+        glist = list(reader(string=new_string))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+class GraphML:
+    NS_GRAPHML = "http://graphml.graphdrawing.org/xmlns"
+    NS_XSI = "http://www.w3.org/2001/XMLSchema-instance"
+    # xmlns:y="http://www.yworks.com/xml/graphml"
+    NS_Y = "http://www.yworks.com/xml/graphml"
+    SCHEMALOCATION = " ".join(
+        [
+            "http://graphml.graphdrawing.org/xmlns",
+            "http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd",
+        ]
+    )
+
+    def construct_types(self):
+        types = [
+            (int, "integer"),  # for Gephi GraphML bug
+            (str, "yfiles"),
+            (str, "string"),
+            (int, "int"),
+            (int, "long"),
+            (float, "float"),
+            (float, "double"),
+            (bool, "boolean"),
+        ]
+
+        # These additions to types allow writing numpy types
+        try:
+            import numpy as np
+        except:
+            pass
+        else:
+            # prepend so that python types are created upon read (last entry wins)
+            types = [
+                (np.float64, "float"),
+                (np.float32, "float"),
+                (np.float16, "float"),
+                (np.int_, "int"),
+                (np.int8, "int"),
+                (np.int16, "int"),
+                (np.int32, "int"),
+                (np.int64, "int"),
+                (np.uint8, "int"),
+                (np.uint16, "int"),
+                (np.uint32, "int"),
+                (np.uint64, "int"),
+                (np.int_, "int"),
+                (np.intc, "int"),
+                (np.intp, "int"),
+            ] + types
+
+        self.xml_type = dict(types)
+        self.python_type = dict(reversed(a) for a in types)
+
+    # This page says that data types in GraphML follow Java(TM).
+    #  http://graphml.graphdrawing.org/primer/graphml-primer.html#AttributesDefinition
+    # true and false are the only boolean literals:
+    #  http://en.wikibooks.org/wiki/Java_Programming/Literals#Boolean_Literals
+    convert_bool = {
+        # We use data.lower() in actual use.
+        "true": True,
+        "false": False,
+        # Include integer strings for convenience.
+        "0": False,
+        0: False,
+        "1": True,
+        1: True,
+    }
+
+    def get_xml_type(self, key):
+        """Wrapper around the xml_type dict that raises a more informative
+        exception message when a user attempts to use data of a type not
+        supported by GraphML."""
+        try:
+            return self.xml_type[key]
+        except KeyError as err:
+            raise TypeError(
+                f"GraphML does not support type {key} as data values."
+            ) from err
+
+
+class GraphMLWriter(GraphML):
+    def __init__(
+        self,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        from xml.etree.ElementTree import Element
+
+        self.myElement = Element
+
+        self.infer_numeric_types = infer_numeric_types
+        self.prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.encoding = encoding
+        self.xml = self.myElement(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self.keys = {}
+        self.attributes = defaultdict(list)
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def __str__(self):
+        from xml.etree.ElementTree import tostring
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        s = tostring(self.xml).decode(self.encoding)
+        return s
+
+    def attr_type(self, name, scope, value):
+        """Infer the attribute type of data named name. Currently this only
+        supports inference of numeric types.
+
+        If self.infer_numeric_types is false, type is used. Otherwise, pick the
+        most general of types found across all values with name and scope. This
+        means edges with data named 'weight' are treated separately from nodes
+        with data named 'weight'.
+        """
+        if self.infer_numeric_types:
+            types = self.attribute_types[(name, scope)]
+
+            if len(types) > 1:
+                types = {self.get_xml_type(t) for t in types}
+                if "string" in types:
+                    return str
+                elif "float" in types or "double" in types:
+                    return float
+                else:
+                    return int
+            else:
+                return list(types)[0]
+        else:
+            return type(value)
+
+    def get_key(self, name, attr_type, scope, default):
+        keys_key = (name, attr_type, scope)
+        try:
+            return self.keys[keys_key]
+        except KeyError:
+            if self.named_key_ids:
+                new_id = name
+            else:
+                new_id = f"d{len(list(self.keys))}"
+
+            self.keys[keys_key] = new_id
+            key_kwargs = {
+                "id": new_id,
+                "for": scope,
+                "attr.name": name,
+                "attr.type": attr_type,
+            }
+            key_element = self.myElement("key", **key_kwargs)
+            # add subelement for data default value if present
+            if default is not None:
+                default_element = self.myElement("default")
+                default_element.text = str(default)
+                key_element.append(default_element)
+            self.xml.insert(0, key_element)
+        return new_id
+
+    def add_data(self, name, element_type, value, scope="all", default=None):
+        """
+        Make a data element for an edge or a node. Keep a log of the
+        type in the keys table.
+        """
+        if element_type not in self.xml_type:
+            raise nx.NetworkXError(
+                f"GraphML writer does not support {element_type} as data values."
+            )
+        keyid = self.get_key(name, self.get_xml_type(element_type), scope, default)
+        data_element = self.myElement("data", key=keyid)
+        data_element.text = str(value)
+        return data_element
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data to edges or nodes, and stores type information
+        to be added later. See add_graph_element.
+        """
+        for k, v in data.items():
+            self.attribute_types[(str(k), scope)].add(type(v))
+            self.attributes[xml_obj].append([k, v, scope, default.get(k)])
+
+    def add_nodes(self, G, graph_element):
+        default = G.graph.get("node_default", {})
+        for node, data in G.nodes(data=True):
+            node_element = self.myElement("node", id=str(node))
+            self.add_attributes("node", node_element, data, default)
+            graph_element.append(node_element)
+
+    def add_edges(self, G, graph_element):
+        if G.is_multigraph():
+            for u, v, key, data in G.edges(data=True, keys=True):
+                edge_element = self.myElement(
+                    "edge",
+                    source=str(u),
+                    target=str(v),
+                    id=str(data.get(self.edge_id_from_attribute))
+                    if self.edge_id_from_attribute
+                    and self.edge_id_from_attribute in data
+                    else str(key),
+                )
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+        else:
+            for u, v, data in G.edges(data=True):
+                if self.edge_id_from_attribute and self.edge_id_from_attribute in data:
+                    # select attribute to be edge id
+                    edge_element = self.myElement(
+                        "edge",
+                        source=str(u),
+                        target=str(v),
+                        id=str(data.get(self.edge_id_from_attribute)),
+                    )
+                else:
+                    # default: no edge id
+                    edge_element = self.myElement("edge", source=str(u), target=str(v))
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self.myElement("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self.myElement(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+        default = {}
+        data = {
+            k: v
+            for (k, v) in G.graph.items()
+            if k not in ["node_default", "edge_default"]
+        }
+        self.add_attributes("graph", graph_element, data, default)
+        self.add_nodes(G, graph_element)
+        self.add_edges(G, graph_element)
+
+        # self.attributes contains a mapping from XML Objects to a list of
+        # data that needs to be added to them.
+        # We postpone processing in order to do type inference/generalization.
+        # See self.attr_type
+        for xml_obj, data in self.attributes.items():
+            for k, v, scope, default in data:
+                xml_obj.append(
+                    self.add_data(
+                        str(k), self.attr_type(k, scope, v), str(v), scope, default
+                    )
+                )
+        self.xml.append(graph_element)
+
+    def add_graphs(self, graph_list):
+        """Add many graphs to this GraphML document."""
+        for G in graph_list:
+            self.add_graph_element(G)
+
+    def dump(self, stream):
+        from xml.etree.ElementTree import ElementTree
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        document = ElementTree(self.xml)
+        document.write(stream, encoding=self.encoding, xml_declaration=True)
+
+    def indent(self, elem, level=0):
+        # in-place prettyprint formatter
+        i = "\n" + level * "  "
+        if len(elem):
+            if not elem.text or not elem.text.strip():
+                elem.text = i + "  "
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+            for elem in elem:
+                self.indent(elem, level + 1)
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+        else:
+            if level and (not elem.tail or not elem.tail.strip()):
+                elem.tail = i
+
+
+class IncrementalElement:
+    """Wrapper for _IncrementalWriter providing an Element like interface.
+
+    This wrapper does not intend to be a complete implementation but rather to
+    deal with those calls used in GraphMLWriter.
+    """
+
+    def __init__(self, xml, prettyprint):
+        self.xml = xml
+        self.prettyprint = prettyprint
+
+    def append(self, element):
+        self.xml.write(element, pretty_print=self.prettyprint)
+
+
+class GraphMLWriterLxml(GraphMLWriter):
+    def __init__(
+        self,
+        path,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        import lxml.etree as lxmletree
+
+        self.myElement = lxmletree.Element
+
+        self._encoding = encoding
+        self._prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.infer_numeric_types = infer_numeric_types
+
+        self._xml_base = lxmletree.xmlfile(path, encoding=encoding)
+        self._xml = self._xml_base.__enter__()
+        self._xml.write_declaration()
+
+        # We need to have a xml variable that support insertion. This call is
+        # used for adding the keys to the document.
+        # We will store those keys in a plain list, and then after the graph
+        # element is closed we will add them to the main graphml element.
+        self.xml = []
+        self._keys = self.xml
+        self._graphml = self._xml.element(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self._graphml.__enter__()
+        self.keys = {}
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self._xml.element("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self._xml.element(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+
+        # gather attributes types for the whole graph
+        # to find the most general numeric format needed.
+        # Then pass through attributes to create key_id for each.
+        graphdata = {
+            k: v
+            for k, v in G.graph.items()
+            if k not in ("node_default", "edge_default")
+        }
+        node_default = G.graph.get("node_default", {})
+        edge_default = G.graph.get("edge_default", {})
+        # Graph attributes
+        for k, v in graphdata.items():
+            self.attribute_types[(str(k), "graph")].add(type(v))
+        for k, v in graphdata.items():
+            element_type = self.get_xml_type(self.attr_type(k, "graph", v))
+            self.get_key(str(k), element_type, "graph", None)
+        # Nodes and data
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                self.attribute_types[(str(k), "node")].add(type(v))
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                T = self.get_xml_type(self.attr_type(k, "node", v))
+                self.get_key(str(k), T, "node", node_default.get(k))
+        # Edges and data
+        if G.is_multigraph():
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+        else:
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+
+        # Now add attribute keys to the xml file
+        for key in self.xml:
+            self._xml.write(key, pretty_print=self._prettyprint)
+
+        # The incremental_writer writes each node/edge as it is created
+        incremental_writer = IncrementalElement(self._xml, self._prettyprint)
+        with graph_element:
+            self.add_attributes("graph", incremental_writer, graphdata, {})
+            self.add_nodes(G, incremental_writer)  # adds attributes too
+            self.add_edges(G, incremental_writer)  # adds attributes too
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data."""
+        for k, v in data.items():
+            data_element = self.add_data(
+                str(k), self.attr_type(str(k), scope, v), str(v), scope, default.get(k)
+            )
+            xml_obj.append(data_element)
+
+    def __str__(self):
+        return object.__str__(self)
+
+    def dump(self, stream=None):
+        self._graphml.__exit__(None, None, None)
+        self._xml_base.__exit__(None, None, None)
+
+
+# default is lxml is present.
+write_graphml = write_graphml_lxml
+
+
+class GraphMLReader(GraphML):
+    """Read a GraphML document.  Produces NetworkX graph objects."""
+
+    def __init__(self, node_type=str, edge_key_type=int, force_multigraph=False):
+        self.construct_types()
+        self.node_type = node_type
+        self.edge_key_type = edge_key_type
+        self.multigraph = force_multigraph  # If False, test for multiedges
+        self.edge_ids = {}  # dict mapping (u,v) tuples to edge id attributes
+
+    def __call__(self, path=None, string=None):
+        from xml.etree.ElementTree import ElementTree, fromstring
+
+        if path is not None:
+            self.xml = ElementTree(file=path)
+        elif string is not None:
+            self.xml = fromstring(string)
+        else:
+            raise ValueError("Must specify either 'path' or 'string' as kwarg")
+        (keys, defaults) = self.find_graphml_keys(self.xml)
+        for g in self.xml.findall(f"{{{self.NS_GRAPHML}}}graph"):
+            yield self.make_graph(g, keys, defaults)
+
+    def make_graph(self, graph_xml, graphml_keys, defaults, G=None):
+        # set default graph type
+        edgedefault = graph_xml.get("edgedefault", None)
+        if G is None:
+            if edgedefault == "directed":
+                G = nx.MultiDiGraph()
+            else:
+                G = nx.MultiGraph()
+        # set defaults for graph attributes
+        G.graph["node_default"] = {}
+        G.graph["edge_default"] = {}
+        for key_id, value in defaults.items():
+            key_for = graphml_keys[key_id]["for"]
+            name = graphml_keys[key_id]["name"]
+            python_type = graphml_keys[key_id]["type"]
+            if key_for == "node":
+                G.graph["node_default"].update({name: python_type(value)})
+            if key_for == "edge":
+                G.graph["edge_default"].update({name: python_type(value)})
+        # hyperedges are not supported
+        hyperedge = graph_xml.find(f"{{{self.NS_GRAPHML}}}hyperedge")
+        if hyperedge is not None:
+            raise nx.NetworkXError("GraphML reader doesn't support hyperedges")
+        # add nodes
+        for node_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}node"):
+            self.add_node(G, node_xml, graphml_keys, defaults)
+        # add edges
+        for edge_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}edge"):
+            self.add_edge(G, edge_xml, graphml_keys)
+        # add graph data
+        data = self.decode_data_elements(graphml_keys, graph_xml)
+        G.graph.update(data)
+
+        # switch to Graph or DiGraph if no parallel edges were found
+        if self.multigraph:
+            return G
+
+        G = nx.DiGraph(G) if G.is_directed() else nx.Graph(G)
+        # add explicit edge "id" from file as attribute in NX graph.
+        nx.set_edge_attributes(G, values=self.edge_ids, name="id")
+        return G
+
+    def add_node(self, G, node_xml, graphml_keys, defaults):
+        """Add a node to the graph."""
+        # warn on finding unsupported ports tag
+        ports = node_xml.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+        # find the node by id and cast it to the appropriate type
+        node_id = self.node_type(node_xml.get("id"))
+        # get data/attributes for node
+        data = self.decode_data_elements(graphml_keys, node_xml)
+        G.add_node(node_id, **data)
+        # get child nodes
+        if node_xml.attrib.get("yfiles.foldertype") == "group":
+            graph_xml = node_xml.find(f"{{{self.NS_GRAPHML}}}graph")
+            self.make_graph(graph_xml, graphml_keys, defaults, G)
+
+    def add_edge(self, G, edge_element, graphml_keys):
+        """Add an edge to the graph."""
+        # warn on finding unsupported ports tag
+        ports = edge_element.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+
+        # raise error if we find mixed directed and undirected edges
+        directed = edge_element.get("directed")
+        if G.is_directed() and directed == "false":
+            msg = "directed=false edge found in directed graph."
+            raise nx.NetworkXError(msg)
+        if (not G.is_directed()) and directed == "true":
+            msg = "directed=true edge found in undirected graph."
+            raise nx.NetworkXError(msg)
+
+        source = self.node_type(edge_element.get("source"))
+        target = self.node_type(edge_element.get("target"))
+        data = self.decode_data_elements(graphml_keys, edge_element)
+        # GraphML stores edge ids as an attribute
+        # NetworkX uses them as keys in multigraphs too if no key
+        # attribute is specified
+        edge_id = edge_element.get("id")
+        if edge_id:
+            # self.edge_ids is used by `make_graph` method for non-multigraphs
+            self.edge_ids[source, target] = edge_id
+            try:
+                edge_id = self.edge_key_type(edge_id)
+            except ValueError:  # Could not convert.
+                pass
+        else:
+            edge_id = data.get("key")
+
+        if G.has_edge(source, target):
+            # mark this as a multigraph
+            self.multigraph = True
+
+        # Use add_edges_from to avoid error with add_edge when `'key' in data`
+        # Note there is only one edge here...
+        G.add_edges_from([(source, target, edge_id, data)])
+
+    def decode_data_elements(self, graphml_keys, obj_xml):
+        """Use the key information to decode the data XML if present."""
+        data = {}
+        for data_element in obj_xml.findall(f"{{{self.NS_GRAPHML}}}data"):
+            key = data_element.get("key")
+            try:
+                data_name = graphml_keys[key]["name"]
+                data_type = graphml_keys[key]["type"]
+            except KeyError as err:
+                raise nx.NetworkXError(f"Bad GraphML data: no key {key}") from err
+            text = data_element.text
+            # assume anything with subelements is a yfiles extension
+            if text is not None and len(list(data_element)) == 0:
+                if data_type == bool:
+                    # Ignore cases.
+                    # http://docs.oracle.com/javase/6/docs/api/java/lang/
+                    # Boolean.html#parseBoolean%28java.lang.String%29
+                    data[data_name] = self.convert_bool[text.lower()]
+                else:
+                    data[data_name] = data_type(text)
+            elif len(list(data_element)) > 0:
+                # Assume yfiles as subelements, try to extract node_label
+                node_label = None
+                # set GenericNode's configuration as shape type
+                gn = data_element.find(f"{{{self.NS_Y}}}GenericNode")
+                if gn is not None:
+                    data["shape_type"] = gn.get("configuration")
+                for node_type in ["GenericNode", "ShapeNode", "SVGNode", "ImageNode"]:
+                    pref = f"{{{self.NS_Y}}}{node_type}/{{{self.NS_Y}}}"
+                    geometry = data_element.find(f"{pref}Geometry")
+                    if geometry is not None:
+                        data["x"] = geometry.get("x")
+                        data["y"] = geometry.get("y")
+                    if node_label is None:
+                        node_label = data_element.find(f"{pref}NodeLabel")
+                    shape = data_element.find(f"{pref}Shape")
+                    if shape is not None:
+                        data["shape_type"] = shape.get("type")
+                if node_label is not None:
+                    data["label"] = node_label.text
+
+                # check all the different types of edges available in yEd.
+                for edge_type in [
+                    "PolyLineEdge",
+                    "SplineEdge",
+                    "QuadCurveEdge",
+                    "BezierEdge",
+                    "ArcEdge",
+                ]:
+                    pref = f"{{{self.NS_Y}}}{edge_type}/{{{self.NS_Y}}}"
+                    edge_label = data_element.find(f"{pref}EdgeLabel")
+                    if edge_label is not None:
+                        break
+                if edge_label is not None:
+                    data["label"] = edge_label.text
+            elif text is None:
+                data[data_name] = ""
+        return data
+
+    def find_graphml_keys(self, graph_element):
+        """Extracts all the keys and key defaults from the xml."""
+        graphml_keys = {}
+        graphml_key_defaults = {}
+        for k in graph_element.findall(f"{{{self.NS_GRAPHML}}}key"):
+            attr_id = k.get("id")
+            attr_type = k.get("attr.type")
+            attr_name = k.get("attr.name")
+            yfiles_type = k.get("yfiles.type")
+            if yfiles_type is not None:
+                attr_name = yfiles_type
+                attr_type = "yfiles"
+            if attr_type is None:
+                attr_type = "string"
+                warnings.warn(f"No key type for id {attr_id}. Using string")
+            if attr_name is None:
+                raise nx.NetworkXError(f"Unknown key for id {attr_id}.")
+            graphml_keys[attr_id] = {
+                "name": attr_name,
+                "type": self.python_type[attr_type],
+                "for": k.get("for"),
+            }
+            # check for "default" sub-element of key element
+            default = k.find(f"{{{self.NS_GRAPHML}}}default")
+            if default is not None:
+                # Handle default values identically to data element values
+                python_type = graphml_keys[attr_id]["type"]
+                if python_type == bool:
+                    graphml_key_defaults[attr_id] = self.convert_bool[
+                        default.text.lower()
+                    ]
+                else:
+                    graphml_key_defaults[attr_id] = python_type(default.text)
+        return graphml_keys, graphml_key_defaults

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/leda.py

@@ -0,0 +1,108 @@
+"""
+Read graphs in LEDA format.
+
+LEDA is a C++ class library for efficient data types and algorithms.
+
+Format
+------
+See http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+
+"""
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+
+__all__ = ["read_leda", "parse_leda"]
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import open_file
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_leda(path, encoding="UTF-8"):
+    """Read graph in LEDA format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to read.  Filenames ending in .gz or .bz2  will be
+       uncompressed.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    Examples
+    --------
+    G=nx.read_leda('file.leda')
+
+    References
+    ----------
+    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+    """
+    lines = (line.decode(encoding) for line in path)
+    G = parse_leda(lines)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_leda(lines):
+    """Read graph in LEDA format from string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable
+       Data in LEDA format.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    Examples
+    --------
+    G=nx.parse_leda(string)
+
+    References
+    ----------
+    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+    """
+    if isinstance(lines, str):
+        lines = iter(lines.split("\n"))
+    lines = iter(
+        [
+            line.rstrip("\n")
+            for line in lines
+            if not (line.startswith(("#", "\n")) or line == "")
+        ]
+    )
+    for i in range(3):
+        next(lines)
+    # Graph
+    du = int(next(lines))  # -1=directed, -2=undirected
+    if du == -1:
+        G = nx.DiGraph()
+    else:
+        G = nx.Graph()
+
+    # Nodes
+    n = int(next(lines))  # number of nodes
+    node = {}
+    for i in range(1, n + 1):  # LEDA counts from 1 to n
+        symbol = next(lines).rstrip().strip("|{}|  ")
+        if symbol == "":
+            symbol = str(i)  # use int if no label - could be trouble
+        node[i] = symbol
+
+    G.add_nodes_from([s for i, s in node.items()])
+
+    # Edges
+    m = int(next(lines))  # number of edges
+    for i in range(m):
+        try:
+            s, t, reversal, label = next(lines).split()
+        except BaseException as err:
+            raise NetworkXError(f"Too few fields in LEDA.GRAPH edge {i+1}") from err
+        # BEWARE: no handling of reversal edges
+        G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
+    return G

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/pajek.py

@@ -0,0 +1,286 @@
+"""
+*****
+Pajek
+*****
+Read graphs in Pajek format.
+
+This implementation handles directed and undirected graphs including
+those with self loops and parallel edges.
+
+Format
+------
+See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+for format information.
+
+"""
+
+import warnings
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["read_pajek", "parse_pajek", "generate_pajek", "write_pajek"]
+
+
+def generate_pajek(G):
+    """Generate lines in Pajek graph format.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    if G.name == "":
+        name = "NetworkX"
+    else:
+        name = G.name
+    # Apparently many Pajek format readers can't process this line
+    # So we'll leave it out for now.
+    # yield '*network %s'%name
+
+    # write nodes with attributes
+    yield f"*vertices {G.order()}"
+    nodes = list(G)
+    # make dictionary mapping nodes to integers
+    nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
+    for n in nodes:
+        # copy node attributes and pop mandatory attributes
+        # to avoid duplication.
+        na = G.nodes.get(n, {}).copy()
+        x = na.pop("x", 0.0)
+        y = na.pop("y", 0.0)
+        try:
+            id = int(na.pop("id", nodenumber[n]))
+        except ValueError as err:
+            err.args += (
+                (
+                    "Pajek format requires 'id' to be an int()."
+                    " Refer to the 'Relabeling nodes' section."
+                ),
+            )
+            raise
+        nodenumber[n] = id
+        shape = na.pop("shape", "ellipse")
+        s = " ".join(map(make_qstr, (id, n, x, y, shape)))
+        # only optional attributes are left in na.
+        for k, v in na.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Node attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+    # write edges with attributes
+    if G.is_directed():
+        yield "*arcs"
+    else:
+        yield "*edges"
+    for u, v, edgedata in G.edges(data=True):
+        d = edgedata.copy()
+        value = d.pop("weight", 1.0)  # use 1 as default edge value
+        s = " ".join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
+        for k, v in d.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Edge attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+
+@open_file(1, mode="wb")
+def write_pajek(G, path, encoding="UTF-8"):
+    """Write graph in Pajek format to path.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.net")
+
+    Warnings
+    --------
+    Optional node attributes and edge attributes must be non-empty strings.
+    Otherwise it will not be written into the file. You will need to
+    convert those attributes to strings if you want to keep them.
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    for line in generate_pajek(G):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_pajek(path, encoding="UTF-8"):
+    """Read graph in Pajek format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be uncompressed.
+
+    Returns
+    -------
+    G : NetworkX MultiGraph or MultiDiGraph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.net")
+    >>> G = nx.read_pajek("test.net")
+
+    To create a Graph instead of a MultiGraph use
+
+    >>> G1 = nx.Graph(G)
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_pajek(lines)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_pajek(lines):
+    """Parse Pajek format graph from string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable
+       Data in Pajek format.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    See Also
+    --------
+    read_pajek
+
+    """
+    import shlex
+
+    # multigraph=False
+    if isinstance(lines, str):
+        lines = iter(lines.split("\n"))
+    lines = iter([line.rstrip("\n") for line in lines])
+    G = nx.MultiDiGraph()  # are multiedges allowed in Pajek? assume yes
+    labels = []  # in the order of the file, needed for matrix
+    while lines:
+        try:
+            l = next(lines)
+        except:  # EOF
+            break
+        if l.lower().startswith("*network"):
+            try:
+                label, name = l.split(None, 1)
+            except ValueError:
+                # Line was not of the form:  *network NAME
+                pass
+            else:
+                G.graph["name"] = name
+        elif l.lower().startswith("*vertices"):
+            nodelabels = {}
+            l, nnodes = l.split()
+            for i in range(int(nnodes)):
+                l = next(lines)
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+                id, label = splitline[0:2]
+                labels.append(label)
+                G.add_node(label)
+                nodelabels[id] = label
+                G.nodes[label]["id"] = id
+                try:
+                    x, y, shape = splitline[2:5]
+                    G.nodes[label].update(
+                        {"x": float(x), "y": float(y), "shape": shape}
+                    )
+                except:
+                    pass
+                extra_attr = zip(splitline[5::2], splitline[6::2])
+                G.nodes[label].update(extra_attr)
+        elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
+            if l.lower().startswith("*edge"):
+                # switch from multidigraph to multigraph
+                G = nx.MultiGraph(G)
+            if l.lower().startswith("*arcs"):
+                # switch to directed with multiple arcs for each existing edge
+                G = G.to_directed()
+            for l in lines:
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+
+                if len(splitline) < 2:
+                    continue
+                ui, vi = splitline[0:2]
+                u = nodelabels.get(ui, ui)
+                v = nodelabels.get(vi, vi)
+                # parse the data attached to this edge and put in a dictionary
+                edge_data = {}
+                try:
+                    # there should always be a single value on the edge?
+                    w = splitline[2:3]
+                    edge_data.update({"weight": float(w[0])})
+                except:
+                    pass
+                    # if there isn't, just assign a 1
+                #                    edge_data.update({'value':1})
+                extra_attr = zip(splitline[3::2], splitline[4::2])
+                edge_data.update(extra_attr)
+                # if G.has_edge(u,v):
+                #     multigraph=True
+                G.add_edge(u, v, **edge_data)
+        elif l.lower().startswith("*matrix"):
+            G = nx.DiGraph(G)
+            adj_list = (
+                (labels[row], labels[col], {"weight": int(data)})
+                for (row, line) in enumerate(lines)
+                for (col, data) in enumerate(line.split())
+                if int(data) != 0
+            )
+            G.add_edges_from(adj_list)
+
+    return G
+
+
+def make_qstr(t):
+    """Returns the string representation of t.
+    Add outer double-quotes if the string has a space.
+    """
+    if not isinstance(t, str):
+        t = str(t)
+    if " " in t:
+        t = f'"{t}"'
+    return t

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/sparse6.py

@@ -0,0 +1,377 @@
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at https://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+"""Functions for reading and writing graphs in the *sparse6* format.
+
+The *sparse6* file format is a space-efficient format for large sparse
+graphs. For small graphs or large dense graphs, use the *graph6* file
+format.
+
+For more information, see the `sparse6`_ homepage.
+
+.. _sparse6: https://users.cecs.anu.edu.au/~bdm/data/formats.html
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.readwrite.graph6 import data_to_n, n_to_data
+from networkx.utils import not_implemented_for, open_file
+
+__all__ = ["from_sparse6_bytes", "read_sparse6", "to_sparse6_bytes", "write_sparse6"]
+
+
+def _generate_sparse6_bytes(G, nodes, header):
+    """Yield bytes in the sparse6 encoding of a graph.
+
+    `G` is an undirected simple graph. `nodes` is the list of nodes for
+    which the node-induced subgraph will be encoded; if `nodes` is the
+    list of all nodes in the graph, the entire graph will be
+    encoded. `header` is a Boolean that specifies whether to generate
+    the header ``b'>>sparse6<<'`` before the remaining data.
+
+    This function generates `bytes` objects in the following order:
+
+    1. the header (if requested),
+    2. the encoding of the number of nodes,
+    3. each character, one-at-a-time, in the encoding of the requested
+       node-induced subgraph,
+    4. a newline character.
+
+    This function raises :exc:`ValueError` if the graph is too large for
+    the graph6 format (that is, greater than ``2 ** 36`` nodes).
+
+    """
+    n = len(G)
+    if n >= 2**36:
+        raise ValueError(
+            "sparse6 is only defined if number of nodes is less than 2 ** 36"
+        )
+    if header:
+        yield b">>sparse6<<"
+    yield b":"
+    for d in n_to_data(n):
+        yield str.encode(chr(d + 63))
+
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def enc(x):
+        """Big endian k-bit encoding of x"""
+        return [1 if (x & 1 << (k - 1 - i)) else 0 for i in range(k)]
+
+    edges = sorted((max(u, v), min(u, v)) for u, v in G.edges())
+    bits = []
+    curv = 0
+    for v, u in edges:
+        if v == curv:  # current vertex edge
+            bits.append(0)
+            bits.extend(enc(u))
+        elif v == curv + 1:  # next vertex edge
+            curv += 1
+            bits.append(1)
+            bits.extend(enc(u))
+        else:  # skip to vertex v and then add edge to u
+            curv = v
+            bits.append(1)
+            bits.extend(enc(v))
+            bits.append(0)
+            bits.extend(enc(u))
+    if k < 6 and n == (1 << k) and ((-len(bits)) % 6) >= k and curv < (n - 1):
+        # Padding special case: small k, n=2^k,
+        # more than k bits of padding needed,
+        # current vertex is not (n-1) --
+        # appending 1111... would add a loop on (n-1)
+        bits.append(0)
+        bits.extend([1] * ((-len(bits)) % 6))
+    else:
+        bits.extend([1] * ((-len(bits)) % 6))
+
+    data = [
+        (bits[i + 0] << 5)
+        + (bits[i + 1] << 4)
+        + (bits[i + 2] << 3)
+        + (bits[i + 3] << 2)
+        + (bits[i + 4] << 1)
+        + (bits[i + 5] << 0)
+        for i in range(0, len(bits), 6)
+    ]
+
+    for d in data:
+        yield str.encode(chr(d + 63))
+    yield b"\n"
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_sparse6_bytes(string):
+    """Read an undirected graph in sparse6 format from string.
+
+    Parameters
+    ----------
+    string : string
+       Data in sparse6 format
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    >>> G = nx.from_sparse6_bytes(b":A_")
+    >>> sorted(G.edges())
+    [(0, 1), (0, 1), (0, 1)]
+
+    See Also
+    --------
+    read_sparse6, write_sparse6
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if string.startswith(b">>sparse6<<"):
+        string = string[11:]
+    if not string.startswith(b":"):
+        raise NetworkXError("Expected leading colon in sparse6")
+
+    chars = [c - 63 for c in string[1:]]
+    n, data = data_to_n(chars)
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def parseData():
+        """Returns stream of pairs b[i], x[i] for sparse6 format."""
+        chunks = iter(data)
+        d = None  # partial data word
+        dLen = 0  # how many unparsed bits are left in d
+
+        while 1:
+            if dLen < 1:
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+            dLen -= 1
+            b = (d >> dLen) & 1  # grab top remaining bit
+
+            x = d & ((1 << dLen) - 1)  # partially built up value of x
+            xLen = dLen  # how many bits included so far in x
+            while xLen < k:  # now grab full chunks until we have enough
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+                x = (x << 6) + d
+                xLen += 6
+            x = x >> (xLen - k)  # shift back the extra bits
+            dLen = xLen - k
+            yield b, x
+
+    v = 0
+
+    G = nx.MultiGraph()
+    G.add_nodes_from(range(n))
+
+    multigraph = False
+    for b, x in parseData():
+        if b == 1:
+            v += 1
+        # padding with ones can cause overlarge number here
+        if x >= n or v >= n:
+            break
+        elif x > v:
+            v = x
+        else:
+            if G.has_edge(x, v):
+                multigraph = True
+            G.add_edge(x, v)
+    if not multigraph:
+        G = nx.Graph(G)
+    return G
+
+
+def to_sparse6_bytes(G, nodes=None, header=True):
+    """Convert an undirected graph to bytes in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>sparse6<<' bytes to head of data.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the sparse6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    >>> nx.to_sparse6_bytes(nx.path_graph(2))
+    b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    to_sparse6_bytes, read_sparse6, write_sparse6_bytes
+
+    Notes
+    -----
+    The returned bytes end with a newline character.
+
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    return b"".join(_generate_sparse6_bytes(G, nodes, header))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_sparse6(path):
+    """Read an undirected graph in sparse6 format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to write.
+
+    Returns
+    -------
+    G : Graph/Multigraph or list of Graphs/MultiGraphs
+       If the file contains multiple lines then a list of graphs is returned
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    You can read a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f.name)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    You can also read a sparse6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    glist = []
+    for line in path:
+        line = line.strip()
+        if not len(line):
+            continue
+        glist.append(from_sparse6_bytes(line))
+    if len(glist) == 1:
+        return glist[0]
+    else:
+        return glist
+
+
+@not_implemented_for("directed")
+@open_file(1, mode="wb")
+def write_sparse6(G, path, nodes=None, header=True):
+    """Write graph G to given path in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    path : file or string
+       File or filename to write
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by G.nodes() is used.
+
+    header: bool
+       If True add '>>sparse6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed
+
+    Examples
+    --------
+    You can write a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f.name)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    You can also write a sparse6 file by giving an open file-like object::
+
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    Notes
+    -----
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    for b in _generate_sparse6_bytes(G, nodes, header):
+        path.write(b)

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_adjlist.py

@@ -0,0 +1,262 @@
+"""
+Unit tests for adjlist.
+"""
+
+import io
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+
+class TestAdjlist:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+        cls.XG = nx.MultiGraph()
+        cls.XG.add_weighted_edges_from([(1, 2, 5), (1, 2, 5), (1, 2, 1), (3, 3, 42)])
+        cls.XDG = nx.MultiDiGraph(cls.XG)
+
+    def test_read_multiline_adjlist_1(self):
+        # Unit test for https://networkx.lanl.gov/trac/ticket/252
+        s = b"""# comment line
+1 2
+# comment line
+2
+3
+"""
+        bytesIO = io.BytesIO(s)
+        G = nx.read_multiline_adjlist(bytesIO)
+        adj = {"1": {"3": {}, "2": {}}, "3": {"1": {}}, "2": {"1": {}}}
+        assert graphs_equal(G, nx.Graph(adj))
+
+    def test_unicode(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname)
+        assert graphs_equal(G, H)
+
+    def test_latin1_err(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "adjlist.txt"
+        with pytest.raises(UnicodeEncodeError):
+            nx.write_multiline_adjlist(G, fname, encoding="latin-1")
+
+    def test_latin1(self, tmp_path):
+        G = nx.Graph()
+        name1 = "Bj" + chr(246) + "rk"
+        name2 = chr(220) + "ber"
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname, encoding="latin-1")
+        H = nx.read_multiline_adjlist(fname, encoding="latin-1")
+        assert graphs_equal(G, H)
+
+    def test_parse_adjlist(self):
+        lines = ["1 2 5", "2 3 4", "3 5", "4", "5"]
+        nx.parse_adjlist(lines, nodetype=int)  # smoke test
+        with pytest.raises(TypeError):
+            nx.parse_adjlist(lines, nodetype="int")
+        lines = ["1 2 5", "2 b", "c"]
+        with pytest.raises(TypeError):
+            nx.parse_adjlist(lines, nodetype=int)
+
+    def test_adjlist_graph(self, tmp_path):
+        G = self.G
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname)
+        H2 = nx.read_adjlist(fname)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_digraph(self, tmp_path):
+        G = self.DG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, create_using=nx.DiGraph())
+        H2 = nx.read_adjlist(fname, create_using=nx.DiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_integers(self, tmp_path):
+        fname = tmp_path / "adjlist.txt"
+        G = nx.convert_node_labels_to_integers(self.G)
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, nodetype=int)
+        H2 = nx.read_adjlist(fname, nodetype=int)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_multigraph(self, tmp_path):
+        G = self.XG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiGraph())
+        H2 = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_multidigraph(self, tmp_path):
+        G = self.XDG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        H2 = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_delimiter(self):
+        fh = io.BytesIO()
+        G = nx.path_graph(3)
+        nx.write_adjlist(G, fh, delimiter=":")
+        fh.seek(0)
+        H = nx.read_adjlist(fh, nodetype=int, delimiter=":")
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+
+class TestMultilineAdjlist:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+        cls.DG.remove_edge("b", "a")
+        cls.DG.remove_edge("b", "c")
+        cls.XG = nx.MultiGraph()
+        cls.XG.add_weighted_edges_from([(1, 2, 5), (1, 2, 5), (1, 2, 1), (3, 3, 42)])
+        cls.XDG = nx.MultiDiGraph(cls.XG)
+
+    def test_parse_multiline_adjlist(self):
+        lines = [
+            "1 2",
+            "b {'weight':3, 'name': 'Frodo'}",
+            "c {}",
+            "d 1",
+            "e {'weight':6, 'name': 'Saruman'}",
+        ]
+        nx.parse_multiline_adjlist(iter(lines))  # smoke test
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+        nx.parse_multiline_adjlist(iter(lines), edgetype=str)  # smoke test
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+        lines = ["1 a"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines))
+        lines = ["a 2"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+        lines = ["1 2"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines))
+        lines = ["1 2", "2 {}"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines))
+
+    def test_multiline_adjlist_graph(self, tmp_path):
+        G = self.G
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname)
+        H2 = nx.read_multiline_adjlist(fname)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_digraph(self, tmp_path):
+        G = self.DG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname, create_using=nx.DiGraph())
+        H2 = nx.read_multiline_adjlist(fname, create_using=nx.DiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_integers(self, tmp_path):
+        fname = tmp_path / "adjlist.txt"
+        G = nx.convert_node_labels_to_integers(self.G)
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname, nodetype=int)
+        H2 = nx.read_multiline_adjlist(fname, nodetype=int)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_multigraph(self, tmp_path):
+        G = self.XG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname, nodetype=int, create_using=nx.MultiGraph())
+        H2 = nx.read_multiline_adjlist(
+            fname, nodetype=int, create_using=nx.MultiGraph()
+        )
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_multidigraph(self, tmp_path):
+        G = self.XDG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(
+            fname, nodetype=int, create_using=nx.MultiDiGraph()
+        )
+        H2 = nx.read_multiline_adjlist(
+            fname, nodetype=int, create_using=nx.MultiDiGraph()
+        )
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_delimiter(self):
+        fh = io.BytesIO()
+        G = nx.path_graph(3)
+        nx.write_multiline_adjlist(G, fh, delimiter=":")
+        fh.seek(0)
+        H = nx.read_multiline_adjlist(fh, nodetype=int, delimiter=":")
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+
+@pytest.mark.parametrize(
+    ("lines", "delim"),
+    (
+        (["1 2 5", "2 3 4", "3 5", "4", "5"], None),  # No extra whitespace
+        (["1\t2\t5", "2\t3\t4", "3\t5", "4", "5"], "\t"),  # tab-delimited
+        (
+            ["1\t2\t5", "2\t3\t4", "3\t5\t", "4\t", "5"],
+            "\t",
+        ),  # tab-delimited, extra delims
+        (
+            ["1\t2\t5", "2\t3\t4", "3\t5\t\t\n", "4\t", "5"],
+            "\t",
+        ),  # extra delim+newlines
+    ),
+)
+def test_adjlist_rstrip_parsing(lines, delim):
+    """Regression test related to gh-7465"""
+    expected = nx.Graph([(1, 2), (1, 5), (2, 3), (2, 4), (3, 5)])
+    nx.utils.graphs_equal(nx.parse_adjlist(lines, delimiter=delim), expected)

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_edgelist.py

@@ -0,0 +1,314 @@
+"""
+Unit tests for edgelists.
+"""
+
+import io
+import textwrap
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+edges_no_data = textwrap.dedent(
+    """
+    # comment line
+    1 2
+    # comment line
+    2 3
+    """
+)
+
+
+edges_with_values = textwrap.dedent(
+    """
+    # comment line
+    1 2 2.0
+    # comment line
+    2 3 3.0
+    """
+)
+
+
+edges_with_weight = textwrap.dedent(
+    """
+    # comment line
+    1 2 {'weight':2.0}
+    # comment line
+    2 3 {'weight':3.0}
+    """
+)
+
+
+edges_with_multiple_attrs = textwrap.dedent(
+    """
+    # comment line
+    1 2 {'weight':2.0, 'color':'green'}
+    # comment line
+    2 3 {'weight':3.0, 'color':'red'}
+    """
+)
+
+
+edges_with_multiple_attrs_csv = textwrap.dedent(
+    """
+    # comment line
+    1, 2, {'weight':2.0, 'color':'green'}
+    # comment line
+    2, 3, {'weight':3.0, 'color':'red'}
+    """
+)
+
+
+_expected_edges_weights = [(1, 2, {"weight": 2.0}), (2, 3, {"weight": 3.0})]
+_expected_edges_multiattr = [
+    (1, 2, {"weight": 2.0, "color": "green"}),
+    (2, 3, {"weight": 3.0, "color": "red"}),
+]
+
+
+@pytest.mark.parametrize(
+    ("data", "extra_kwargs"),
+    (
+        (edges_no_data, {}),
+        (edges_with_values, {}),
+        (edges_with_weight, {}),
+        (edges_with_multiple_attrs, {}),
+        (edges_with_multiple_attrs_csv, {"delimiter": ","}),
+    ),
+)
+def test_read_edgelist_no_data(data, extra_kwargs):
+    bytesIO = io.BytesIO(data.encode("utf-8"))
+    G = nx.read_edgelist(bytesIO, nodetype=int, data=False, **extra_kwargs)
+    assert edges_equal(G.edges(), [(1, 2), (2, 3)])
+
+
+def test_read_weighted_edgelist():
+    bytesIO = io.BytesIO(edges_with_values.encode("utf-8"))
+    G = nx.read_weighted_edgelist(bytesIO, nodetype=int)
+    assert edges_equal(G.edges(data=True), _expected_edges_weights)
+
+
+@pytest.mark.parametrize(
+    ("data", "extra_kwargs", "expected"),
+    (
+        (edges_with_weight, {}, _expected_edges_weights),
+        (edges_with_multiple_attrs, {}, _expected_edges_multiattr),
+        (edges_with_multiple_attrs_csv, {"delimiter": ","}, _expected_edges_multiattr),
+    ),
+)
+def test_read_edgelist_with_data(data, extra_kwargs, expected):
+    bytesIO = io.BytesIO(data.encode("utf-8"))
+    G = nx.read_edgelist(bytesIO, nodetype=int, **extra_kwargs)
+    assert edges_equal(G.edges(data=True), expected)
+
+
+@pytest.fixture
+def example_graph():
+    G = nx.Graph()
+    G.add_weighted_edges_from([(1, 2, 3.0), (2, 3, 27.0), (3, 4, 3.0)])
+    return G
+
+
+def test_parse_edgelist_no_data(example_graph):
+    G = example_graph
+    H = nx.parse_edgelist(["1 2", "2 3", "3 4"], nodetype=int)
+    assert nodes_equal(G.nodes, H.nodes)
+    assert edges_equal(G.edges, H.edges)
+
+
+def test_parse_edgelist_with_data_dict(example_graph):
+    G = example_graph
+    H = nx.parse_edgelist(
+        ["1 2 {'weight': 3}", "2 3 {'weight': 27}", "3 4 {'weight': 3.0}"], nodetype=int
+    )
+    assert nodes_equal(G.nodes, H.nodes)
+    assert edges_equal(G.edges(data=True), H.edges(data=True))
+
+
+def test_parse_edgelist_with_data_list(example_graph):
+    G = example_graph
+    H = nx.parse_edgelist(
+        ["1 2 3", "2 3 27", "3 4 3.0"], nodetype=int, data=(("weight", float),)
+    )
+    assert nodes_equal(G.nodes, H.nodes)
+    assert edges_equal(G.edges(data=True), H.edges(data=True))
+
+
+def test_parse_edgelist():
+    # ignore lines with less than 2 nodes
+    lines = ["1;2", "2 3", "3 4"]
+    G = nx.parse_edgelist(lines, nodetype=int)
+    assert list(G.edges()) == [(2, 3), (3, 4)]
+    # unknown nodetype
+    with pytest.raises(TypeError, match="Failed to convert nodes"):
+        lines = ["1 2", "2 3", "3 4"]
+        nx.parse_edgelist(lines, nodetype="nope")
+    # lines have invalid edge format
+    with pytest.raises(TypeError, match="Failed to convert edge data"):
+        lines = ["1 2 3", "2 3", "3 4"]
+        nx.parse_edgelist(lines, nodetype=int)
+    # edge data and data_keys not the same length
+    with pytest.raises(IndexError, match="not the same length"):
+        lines = ["1 2 3", "2 3 27", "3 4 3.0"]
+        nx.parse_edgelist(
+            lines, nodetype=int, data=(("weight", float), ("capacity", int))
+        )
+    # edge data can't be converted to edge type
+    with pytest.raises(TypeError, match="Failed to convert"):
+        lines = ["1 2 't1'", "2 3 't3'", "3 4 't3'"]
+        nx.parse_edgelist(lines, nodetype=int, data=(("weight", float),))
+
+
+def test_comments_None():
+    edgelist = ["node#1 node#2", "node#2 node#3"]
+    # comments=None supported to ignore all comment characters
+    G = nx.parse_edgelist(edgelist, comments=None)
+    H = nx.Graph([e.split(" ") for e in edgelist])
+    assert edges_equal(G.edges, H.edges)
+
+
+class TestEdgelist:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+        cls.XG = nx.MultiGraph()
+        cls.XG.add_weighted_edges_from([(1, 2, 5), (1, 2, 5), (1, 2, 1), (3, 3, 42)])
+        cls.XDG = nx.MultiDiGraph(cls.XG)
+
+    def test_write_edgelist_1(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edges_from([(1, 2), (2, 3)])
+        nx.write_edgelist(G, fh, data=False)
+        fh.seek(0)
+        assert fh.read() == b"1 2\n2 3\n"
+
+    def test_write_edgelist_2(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edges_from([(1, 2), (2, 3)])
+        nx.write_edgelist(G, fh, data=True)
+        fh.seek(0)
+        assert fh.read() == b"1 2 {}\n2 3 {}\n"
+
+    def test_write_edgelist_3(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2.0)
+        G.add_edge(2, 3, weight=3.0)
+        nx.write_edgelist(G, fh, data=True)
+        fh.seek(0)
+        assert fh.read() == b"1 2 {'weight': 2.0}\n2 3 {'weight': 3.0}\n"
+
+    def test_write_edgelist_4(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2.0)
+        G.add_edge(2, 3, weight=3.0)
+        nx.write_edgelist(G, fh, data=[("weight")])
+        fh.seek(0)
+        assert fh.read() == b"1 2 2.0\n2 3 3.0\n"
+
+    def test_unicode(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname)
+        assert graphs_equal(G, H)
+
+    def test_latin1_issue(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "el.txt"
+        with pytest.raises(UnicodeEncodeError):
+            nx.write_edgelist(G, fname, encoding="latin-1")
+
+    def test_latin1(self, tmp_path):
+        G = nx.Graph()
+        name1 = "Bj" + chr(246) + "rk"
+        name2 = chr(220) + "ber"
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "el.txt"
+
+        nx.write_edgelist(G, fname, encoding="latin-1")
+        H = nx.read_edgelist(fname, encoding="latin-1")
+        assert graphs_equal(G, H)
+
+    def test_edgelist_graph(self, tmp_path):
+        G = self.G
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname)
+        H2 = nx.read_edgelist(fname)
+        assert H is not H2  # they should be different graphs
+        G.remove_node("g")  # isolated nodes are not written in edgelist
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_edgelist_digraph(self, tmp_path):
+        G = self.DG
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, create_using=nx.DiGraph())
+        H2 = nx.read_edgelist(fname, create_using=nx.DiGraph())
+        assert H is not H2  # they should be different graphs
+        G.remove_node("g")  # isolated nodes are not written in edgelist
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_edgelist_integers(self, tmp_path):
+        G = nx.convert_node_labels_to_integers(self.G)
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, nodetype=int)
+        # isolated nodes are not written in edgelist
+        G.remove_nodes_from(list(nx.isolates(G)))
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_edgelist_multigraph(self, tmp_path):
+        G = self.XG
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiGraph())
+        H2 = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_edgelist_multidigraph(self, tmp_path):
+        G = self.XDG
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        H2 = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+
+def test_edgelist_consistent_strip_handling():
+    """See gh-7462
+
+    Input when printed looks like::
+
+        1       2       3
+        2       3
+        3       4       3.0
+
+    Note the trailing \\t after the `3` in the second row, indicating an empty
+    data value.
+    """
+    s = io.StringIO("1\t2\t3\n2\t3\t\n3\t4\t3.0")
+    G = nx.parse_edgelist(s, delimiter="\t", nodetype=int, data=[("value", str)])
+    assert sorted(G.edges(data="value")) == [(1, 2, "3"), (2, 3, ""), (3, 4, "3.0")]

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_gexf.py

@@ -0,0 +1,557 @@
+import io
+import time
+
+import pytest
+
+import networkx as nx
+
+
+class TestGEXF:
+    @classmethod
+    def setup_class(cls):
+        cls.simple_directed_data = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version="1.2">
+    <graph mode="static" defaultedgetype="directed">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" />
+        </edges>
+    </graph>
+</gexf>
+"""
+        cls.simple_directed_graph = nx.DiGraph()
+        cls.simple_directed_graph.add_node("0", label="Hello")
+        cls.simple_directed_graph.add_node("1", label="World")
+        cls.simple_directed_graph.add_edge("0", "1", id="0")
+
+        cls.simple_directed_fh = io.BytesIO(cls.simple_directed_data.encode("UTF-8"))
+
+        cls.attribute_data = """<?xml version="1.0" encoding="UTF-8"?>\
+<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:xsi="http://www.w3.\
+org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.gexf.net/\
+1.2draft http://www.gexf.net/1.2draft/gexf.xsd" version="1.2">
+  <meta lastmodifieddate="2009-03-20">
+    <creator>Gephi.org</creator>
+    <description>A Web network</description>
+  </meta>
+  <graph defaultedgetype="directed">
+    <attributes class="node">
+      <attribute id="0" title="url" type="string"/>
+      <attribute id="1" title="indegree" type="integer"/>
+      <attribute id="2" title="frog" type="boolean">
+        <default>true</default>
+      </attribute>
+    </attributes>
+    <nodes>
+      <node id="0" label="Gephi">
+        <attvalues>
+          <attvalue for="0" value="https://gephi.org"/>
+          <attvalue for="1" value="1"/>
+          <attvalue for="2" value="false"/>
+        </attvalues>
+      </node>
+      <node id="1" label="Webatlas">
+        <attvalues>
+          <attvalue for="0" value="http://webatlas.fr"/>
+          <attvalue for="1" value="2"/>
+          <attvalue for="2" value="false"/>
+        </attvalues>
+      </node>
+      <node id="2" label="RTGI">
+        <attvalues>
+          <attvalue for="0" value="http://rtgi.fr"/>
+          <attvalue for="1" value="1"/>
+          <attvalue for="2" value="true"/>
+        </attvalues>
+      </node>
+      <node id="3" label="BarabasiLab">
+        <attvalues>
+          <attvalue for="0" value="http://barabasilab.com"/>
+          <attvalue for="1" value="1"/>
+          <attvalue for="2" value="true"/>
+        </attvalues>
+      </node>
+    </nodes>
+    <edges>
+      <edge id="0" source="0" target="1" label="foo"/>
+      <edge id="1" source="0" target="2"/>
+      <edge id="2" source="1" target="0"/>
+      <edge id="3" source="2" target="1"/>
+      <edge id="4" source="0" target="3"/>
+    </edges>
+  </graph>
+</gexf>
+"""
+        cls.attribute_graph = nx.DiGraph()
+        cls.attribute_graph.graph["node_default"] = {"frog": True}
+        cls.attribute_graph.add_node(
+            "0", label="Gephi", url="https://gephi.org", indegree=1, frog=False
+        )
+        cls.attribute_graph.add_node(
+            "1", label="Webatlas", url="http://webatlas.fr", indegree=2, frog=False
+        )
+        cls.attribute_graph.add_node(
+            "2", label="RTGI", url="http://rtgi.fr", indegree=1, frog=True
+        )
+        cls.attribute_graph.add_node(
+            "3",
+            label="BarabasiLab",
+            url="http://barabasilab.com",
+            indegree=1,
+            frog=True,
+        )
+        cls.attribute_graph.add_edge("0", "1", id="0", label="foo")
+        cls.attribute_graph.add_edge("0", "2", id="1")
+        cls.attribute_graph.add_edge("1", "0", id="2")
+        cls.attribute_graph.add_edge("2", "1", id="3")
+        cls.attribute_graph.add_edge("0", "3", id="4")
+        cls.attribute_fh = io.BytesIO(cls.attribute_data.encode("UTF-8"))
+
+        cls.simple_undirected_data = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version="1.2">
+    <graph mode="static" defaultedgetype="undirected">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" />
+        </edges>
+    </graph>
+</gexf>
+"""
+        cls.simple_undirected_graph = nx.Graph()
+        cls.simple_undirected_graph.add_node("0", label="Hello")
+        cls.simple_undirected_graph.add_node("1", label="World")
+        cls.simple_undirected_graph.add_edge("0", "1", id="0")
+
+        cls.simple_undirected_fh = io.BytesIO(
+            cls.simple_undirected_data.encode("UTF-8")
+        )
+
+    def test_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        H = nx.read_gexf(self.simple_directed_fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_write_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_read_simple_undirected_graphml(self):
+        G = self.simple_undirected_graph
+        H = nx.read_gexf(self.simple_undirected_fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+        self.simple_undirected_fh.seek(0)
+
+    def test_read_attribute_graphml(self):
+        G = self.attribute_graph
+        H = nx.read_gexf(self.attribute_fh)
+        assert sorted(G.nodes(True)) == sorted(H.nodes(data=True))
+        ge = sorted(G.edges(data=True))
+        he = sorted(H.edges(data=True))
+        for a, b in zip(ge, he):
+            assert a == b
+        self.attribute_fh.seek(0)
+
+    def test_directed_edge_in_undirected(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="undirected" name="">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" type="directed"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_gexf, fh)
+
+    def test_undirected_edge_in_directed(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="directed" name="">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" type="undirected"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_gexf, fh)
+
+    def test_key_raises(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="directed" name="">
+        <nodes>
+            <node id="0" label="Hello">
+              <attvalues>
+                <attvalue for='0' value='1'/>
+              </attvalues>
+            </node>
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" type="undirected"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_gexf, fh)
+
+    def test_relabel(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="directed" name="">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_gexf(fh, relabel=True)
+        assert sorted(G.nodes()) == ["Hello", "Word"]
+
+    def test_default_attribute(self):
+        G = nx.Graph()
+        G.add_node(1, label="1", color="green")
+        nx.add_path(G, [0, 1, 2, 3])
+        G.add_edge(1, 2, foo=3)
+        G.graph["node_default"] = {"color": "yellow"}
+        G.graph["edge_default"] = {"foo": 7}
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+        # Reading a gexf graph always sets mode attribute to either
+        # 'static' or 'dynamic'. Remove the mode attribute from the
+        # read graph for the sake of comparing remaining attributes.
+        del H.graph["mode"]
+        assert G.graph == H.graph
+
+    def test_serialize_ints_to_strings(self):
+        G = nx.Graph()
+        G.add_node(1, id=7, label=77)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert list(H) == [7]
+        assert H.nodes[7]["label"] == "77"
+
+    def test_write_with_node_attributes(self):
+        # Addresses #673.
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (2, 3)])
+        for i in range(4):
+            G.nodes[i]["id"] = i
+            G.nodes[i]["label"] = i
+            G.nodes[i]["pid"] = i
+            G.nodes[i]["start"] = i
+            G.nodes[i]["end"] = i + 1
+
+        expected = f"""<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:xsi\
+="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation=\
+"http://www.gexf.net/1.2draft http://www.gexf.net/1.2draft/\
+gexf.xsd" version="1.2">
+  <meta lastmodifieddate="{time.strftime('%Y-%m-%d')}">
+    <creator>NetworkX {nx.__version__}</creator>
+  </meta>
+  <graph defaultedgetype="undirected" mode="dynamic" name="" timeformat="long">
+    <nodes>
+      <node id="0" label="0" pid="0" start="0" end="1" />
+      <node id="1" label="1" pid="1" start="1" end="2" />
+      <node id="2" label="2" pid="2" start="2" end="3" />
+      <node id="3" label="3" pid="3" start="3" end="4" />
+    </nodes>
+    <edges>
+      <edge source="0" target="1" id="0" />
+      <edge source="1" target="2" id="1" />
+      <edge source="2" target="3" id="2" />
+    </edges>
+  </graph>
+</gexf>"""
+        obtained = "\n".join(nx.generate_gexf(G))
+        assert expected == obtained
+
+    def test_edge_id_construct(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1, {"id": 0}), (1, 2, {"id": 2}), (2, 3)])
+
+        expected = f"""<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:xsi\
+="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.\
+gexf.net/1.2draft http://www.gexf.net/1.2draft/gexf.xsd" version="1.2">
+  <meta lastmodifieddate="{time.strftime('%Y-%m-%d')}">
+    <creator>NetworkX {nx.__version__}</creator>
+  </meta>
+  <graph defaultedgetype="undirected" mode="static" name="">
+    <nodes>
+      <node id="0" label="0" />
+      <node id="1" label="1" />
+      <node id="2" label="2" />
+      <node id="3" label="3" />
+    </nodes>
+    <edges>
+      <edge source="0" target="1" id="0" />
+      <edge source="1" target="2" id="2" />
+      <edge source="2" target="3" id="1" />
+    </edges>
+  </graph>
+</gexf>"""
+
+        obtained = "\n".join(nx.generate_gexf(G))
+        assert expected == obtained
+
+    def test_numpy_type(self):
+        np = pytest.importorskip("numpy")
+        G = nx.path_graph(4)
+        nx.set_node_attributes(G, {n: n for n in np.arange(4)}, "number")
+        G[0][1]["edge-number"] = np.float64(1.1)
+
+        expected = f"""<gexf xmlns="http://www.gexf.net/1.2draft"\
+ xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation\
+="http://www.gexf.net/1.2draft http://www.gexf.net/1.2draft/gexf.xsd"\
+ version="1.2">
+  <meta lastmodifieddate="{time.strftime('%Y-%m-%d')}">
+    <creator>NetworkX {nx.__version__}</creator>
+  </meta>
+  <graph defaultedgetype="undirected" mode="static" name="">
+    <attributes mode="static" class="edge">
+      <attribute id="1" title="edge-number" type="float" />
+    </attributes>
+    <attributes mode="static" class="node">
+      <attribute id="0" title="number" type="int" />
+    </attributes>
+    <nodes>
+      <node id="0" label="0">
+        <attvalues>
+          <attvalue for="0" value="0" />
+        </attvalues>
+      </node>
+      <node id="1" label="1">
+        <attvalues>
+          <attvalue for="0" value="1" />
+        </attvalues>
+      </node>
+      <node id="2" label="2">
+        <attvalues>
+          <attvalue for="0" value="2" />
+        </attvalues>
+      </node>
+      <node id="3" label="3">
+        <attvalues>
+          <attvalue for="0" value="3" />
+        </attvalues>
+      </node>
+    </nodes>
+    <edges>
+      <edge source="0" target="1" id="0">
+        <attvalues>
+          <attvalue for="1" value="1.1" />
+        </attvalues>
+      </edge>
+      <edge source="1" target="2" id="1" />
+      <edge source="2" target="3" id="2" />
+    </edges>
+  </graph>
+</gexf>"""
+        obtained = "\n".join(nx.generate_gexf(G))
+        assert expected == obtained
+
+    def test_bool(self):
+        G = nx.Graph()
+        G.add_node(1, testattr=True)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert H.nodes[1]["testattr"]
+
+    # Test for NaN, INF and -INF
+    def test_specials(self):
+        from math import isnan
+
+        inf, nan = float("inf"), float("nan")
+        G = nx.Graph()
+        G.add_node(1, testattr=inf, strdata="inf", key="a")
+        G.add_node(2, testattr=nan, strdata="nan", key="b")
+        G.add_node(3, testattr=-inf, strdata="-inf", key="c")
+
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        filetext = fh.read()
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+
+        assert b"INF" in filetext
+        assert b"NaN" in filetext
+        assert b"-INF" in filetext
+
+        assert H.nodes[1]["testattr"] == inf
+        assert isnan(H.nodes[2]["testattr"])
+        assert H.nodes[3]["testattr"] == -inf
+
+        assert H.nodes[1]["strdata"] == "inf"
+        assert H.nodes[2]["strdata"] == "nan"
+        assert H.nodes[3]["strdata"] == "-inf"
+
+        assert H.nodes[1]["networkx_key"] == "a"
+        assert H.nodes[2]["networkx_key"] == "b"
+        assert H.nodes[3]["networkx_key"] == "c"
+
+    def test_simple_list(self):
+        G = nx.Graph()
+        list_value = [(1, 2, 3), (9, 1, 2)]
+        G.add_node(1, key=list_value)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert H.nodes[1]["networkx_key"] == list_value
+
+    def test_dynamic_mode(self):
+        G = nx.Graph()
+        G.add_node(1, label="1", color="green")
+        G.graph["mode"] = "dynamic"
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_multigraph_with_missing_attributes(self):
+        G = nx.MultiGraph()
+        G.add_node(0, label="1", color="green")
+        G.add_node(1, label="2", color="green")
+        G.add_edge(0, 1, id="0", weight=3, type="undirected", start=0, end=1)
+        G.add_edge(0, 1, id="1", label="foo", start=0, end=1)
+        G.add_edge(0, 1)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_missing_viz_attributes(self):
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["viz"] = {"size": 54}
+        G.nodes[0]["viz"]["position"] = {"x": 0, "y": 1, "z": 0}
+        G.nodes[0]["viz"]["color"] = {"r": 0, "g": 0, "b": 256}
+        G.nodes[0]["viz"]["shape"] = "http://random.url"
+        G.nodes[0]["viz"]["thickness"] = 2
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh, version="1.1draft")
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+        # Test missing alpha value for version >draft1.1 - set default alpha value
+        # to 1.0 instead of `None` when writing for better general compatibility
+        fh = io.BytesIO()
+        # G.nodes[0]["viz"]["color"] does not have an alpha value explicitly defined
+        # so the default is used instead
+        nx.write_gexf(G, fh, version="1.2draft")
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert H.nodes[0]["viz"]["color"]["a"] == 1.0
+
+        # Second graph for the other branch
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["viz"] = {"size": 54}
+        G.nodes[0]["viz"]["position"] = {"x": 0, "y": 1, "z": 0}
+        G.nodes[0]["viz"]["color"] = {"r": 0, "g": 0, "b": 256, "a": 0.5}
+        G.nodes[0]["viz"]["shape"] = "ftp://random.url"
+        G.nodes[0]["viz"]["thickness"] = 2
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_slice_and_spell(self):
+        # Test spell first, so version = 1.2
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["spells"] = [(1, 2)]
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["slices"] = [(1, 2)]
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh, version="1.1draft")
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_add_parent(self):
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green", parents=[1, 2])
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_gml.py

@@ -0,0 +1,744 @@
+import codecs
+import io
+import math
+from ast import literal_eval
+from contextlib import contextmanager
+from textwrap import dedent
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.gml import literal_destringizer, literal_stringizer
+
+
+class TestGraph:
+    @classmethod
+    def setup_class(cls):
+        cls.simple_data = """Creator "me"
+Version "xx"
+graph [
+ comment "This is a sample graph"
+ directed 1
+ IsPlanar 1
+ pos  [ x 0 y 1 ]
+ node [
+   id 1
+   label "Node 1"
+   pos [ x 1 y 1 ]
+ ]
+ node [
+    id 2
+    pos [ x 1 y 2 ]
+    label "Node 2"
+    ]
+  node [
+    id 3
+    label "Node 3"
+    pos [ x 1 y 3 ]
+  ]
+  edge [
+    source 1
+    target 2
+    label "Edge from node 1 to node 2"
+    color [line "blue" thickness 3]
+
+  ]
+  edge [
+    source 2
+    target 3
+    label "Edge from node 2 to node 3"
+  ]
+  edge [
+    source 3
+    target 1
+    label "Edge from node 3 to node 1"
+  ]
+]
+"""
+
+    def test_parse_gml_cytoscape_bug(self):
+        # example from issue #321, originally #324 in trac
+        cytoscape_example = """
+Creator "Cytoscape"
+Version 1.0
+graph   [
+    node    [
+        root_index  -3
+        id  -3
+        graphics    [
+            x   -96.0
+            y   -67.0
+            w   40.0
+            h   40.0
+            fill    "#ff9999"
+            type    "ellipse"
+            outline "#666666"
+            outline_width   1.5
+        ]
+        label   "node2"
+    ]
+    node    [
+        root_index  -2
+        id  -2
+        graphics    [
+            x   63.0
+            y   37.0
+            w   40.0
+            h   40.0
+            fill    "#ff9999"
+            type    "ellipse"
+            outline "#666666"
+            outline_width   1.5
+        ]
+        label   "node1"
+    ]
+    node    [
+        root_index  -1
+        id  -1
+        graphics    [
+            x   -31.0
+            y   -17.0
+            w   40.0
+            h   40.0
+            fill    "#ff9999"
+            type    "ellipse"
+            outline "#666666"
+            outline_width   1.5
+        ]
+        label   "node0"
+    ]
+    edge    [
+        root_index  -2
+        target  -2
+        source  -1
+        graphics    [
+            width   1.5
+            fill    "#0000ff"
+            type    "line"
+            Line    [
+            ]
+            source_arrow    0
+            target_arrow    3
+        ]
+        label   "DirectedEdge"
+    ]
+    edge    [
+        root_index  -1
+        target  -1
+        source  -3
+        graphics    [
+            width   1.5
+            fill    "#0000ff"
+            type    "line"
+            Line    [
+            ]
+            source_arrow    0
+            target_arrow    3
+        ]
+        label   "DirectedEdge"
+    ]
+]
+"""
+        nx.parse_gml(cytoscape_example)
+
+    def test_parse_gml(self):
+        G = nx.parse_gml(self.simple_data, label="label")
+        assert sorted(G.nodes()) == ["Node 1", "Node 2", "Node 3"]
+        assert sorted(G.edges()) == [
+            ("Node 1", "Node 2"),
+            ("Node 2", "Node 3"),
+            ("Node 3", "Node 1"),
+        ]
+
+        assert sorted(G.edges(data=True)) == [
+            (
+                "Node 1",
+                "Node 2",
+                {
+                    "color": {"line": "blue", "thickness": 3},
+                    "label": "Edge from node 1 to node 2",
+                },
+            ),
+            ("Node 2", "Node 3", {"label": "Edge from node 2 to node 3"}),
+            ("Node 3", "Node 1", {"label": "Edge from node 3 to node 1"}),
+        ]
+
+    def test_read_gml(self, tmp_path):
+        fname = tmp_path / "test.gml"
+        with open(fname, "w") as fh:
+            fh.write(self.simple_data)
+        Gin = nx.read_gml(fname, label="label")
+        G = nx.parse_gml(self.simple_data, label="label")
+        assert sorted(G.nodes(data=True)) == sorted(Gin.nodes(data=True))
+        assert sorted(G.edges(data=True)) == sorted(Gin.edges(data=True))
+
+    def test_labels_are_strings(self):
+        # GML requires labels to be strings (i.e., in quotes)
+        answer = """graph [
+  node [
+    id 0
+    label "1203"
+  ]
+]"""
+        G = nx.Graph()
+        G.add_node(1203)
+        data = "\n".join(nx.generate_gml(G, stringizer=literal_stringizer))
+        assert data == answer
+
+    def test_relabel_duplicate(self):
+        data = """
+graph
+[
+        label   ""
+        directed        1
+        node
+        [
+                id      0
+                label   "same"
+        ]
+        node
+        [
+                id      1
+                label   "same"
+        ]
+]
+"""
+        fh = io.BytesIO(data.encode("UTF-8"))
+        fh.seek(0)
+        pytest.raises(nx.NetworkXError, nx.read_gml, fh, label="label")
+
+    @pytest.mark.parametrize("stringizer", (None, literal_stringizer))
+    def test_tuplelabels(self, stringizer):
+        # https://github.com/networkx/networkx/pull/1048
+        # Writing tuple labels to GML failed.
+        G = nx.Graph()
+        G.add_edge((0, 1), (1, 0))
+        data = "\n".join(nx.generate_gml(G, stringizer=stringizer))
+        answer = """graph [
+  node [
+    id 0
+    label "(0,1)"
+  ]
+  node [
+    id 1
+    label "(1,0)"
+  ]
+  edge [
+    source 0
+    target 1
+  ]
+]"""
+        assert data == answer
+
+    def test_quotes(self, tmp_path):
+        # https://github.com/networkx/networkx/issues/1061
+        # Encoding quotes as HTML entities.
+        G = nx.path_graph(1)
+        G.name = "path_graph(1)"
+        attr = 'This is "quoted" and this is a copyright: ' + chr(169)
+        G.nodes[0]["demo"] = attr
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+        answer = """graph [
+  name "path_graph(1)"
+  node [
+    id 0
+    label "0"
+    demo "This is "quoted" and this is a copyright: ©"
+  ]
+]"""
+        assert data == answer
+
+    def test_unicode_node(self, tmp_path):
+        node = "node" + chr(169)
+        G = nx.Graph()
+        G.add_node(node)
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+        answer = """graph [
+  node [
+    id 0
+    label "node©"
+  ]
+]"""
+        assert data == answer
+
+    def test_float_label(self, tmp_path):
+        node = 1.0
+        G = nx.Graph()
+        G.add_node(node)
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+        answer = """graph [
+  node [
+    id 0
+    label "1.0"
+  ]
+]"""
+        assert data == answer
+
+    def test_special_float_label(self, tmp_path):
+        special_floats = [float("nan"), float("+inf"), float("-inf")]
+        try:
+            import numpy as np
+
+            special_floats += [np.nan, np.inf, np.inf * -1]
+        except ImportError:
+            special_floats += special_floats
+
+        G = nx.cycle_graph(len(special_floats))
+        attrs = dict(enumerate(special_floats))
+        nx.set_node_attributes(G, attrs, "nodefloat")
+        edges = list(G.edges)
+        attrs = {edges[i]: value for i, value in enumerate(special_floats)}
+        nx.set_edge_attributes(G, attrs, "edgefloat")
+
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+            answer = """graph [
+  node [
+    id 0
+    label "0"
+    nodefloat NAN
+  ]
+  node [
+    id 1
+    label "1"
+    nodefloat +INF
+  ]
+  node [
+    id 2
+    label "2"
+    nodefloat -INF
+  ]
+  node [
+    id 3
+    label "3"
+    nodefloat NAN
+  ]
+  node [
+    id 4
+    label "4"
+    nodefloat +INF
+  ]
+  node [
+    id 5
+    label "5"
+    nodefloat -INF
+  ]
+  edge [
+    source 0
+    target 1
+    edgefloat NAN
+  ]
+  edge [
+    source 0
+    target 5
+    edgefloat +INF
+  ]
+  edge [
+    source 1
+    target 2
+    edgefloat -INF
+  ]
+  edge [
+    source 2
+    target 3
+    edgefloat NAN
+  ]
+  edge [
+    source 3
+    target 4
+    edgefloat +INF
+  ]
+  edge [
+    source 4
+    target 5
+    edgefloat -INF
+  ]
+]"""
+            assert data == answer
+
+            fobj.seek(0)
+            graph = nx.read_gml(fobj)
+            for indx, value in enumerate(special_floats):
+                node_value = graph.nodes[str(indx)]["nodefloat"]
+                if math.isnan(value):
+                    assert math.isnan(node_value)
+                else:
+                    assert node_value == value
+
+                edge = edges[indx]
+                string_edge = (str(edge[0]), str(edge[1]))
+                edge_value = graph.edges[string_edge]["edgefloat"]
+                if math.isnan(value):
+                    assert math.isnan(edge_value)
+                else:
+                    assert edge_value == value
+
+    def test_name(self):
+        G = nx.parse_gml('graph [ name "x" node [ id 0 label "x" ] ]')
+        assert "x" == G.graph["name"]
+        G = nx.parse_gml('graph [ node [ id 0 label "x" ] ]')
+        assert "" == G.name
+        assert "name" not in G.graph
+
+    def test_graph_types(self):
+        for directed in [None, False, True]:
+            for multigraph in [None, False, True]:
+                gml = "graph ["
+                if directed is not None:
+                    gml += " directed " + str(int(directed))
+                if multigraph is not None:
+                    gml += " multigraph " + str(int(multigraph))
+                gml += ' node [ id 0 label "0" ]'
+                gml += " edge [ source 0 target 0 ]"
+                gml += " ]"
+                G = nx.parse_gml(gml)
+                assert bool(directed) == G.is_directed()
+                assert bool(multigraph) == G.is_multigraph()
+                gml = "graph [\n"
+                if directed is True:
+                    gml += "  directed 1\n"
+                if multigraph is True:
+                    gml += "  multigraph 1\n"
+                gml += """  node [
+    id 0
+    label "0"
+  ]
+  edge [
+    source 0
+    target 0
+"""
+                if multigraph:
+                    gml += "    key 0\n"
+                gml += "  ]\n]"
+                assert gml == "\n".join(nx.generate_gml(G))
+
+    def test_data_types(self):
+        data = [
+            True,
+            False,
+            10**20,
+            -2e33,
+            "'",
+            '"&&&""',
+            [{(b"\xfd",): "\x7f", chr(0x4444): (1, 2)}, (2, "3")],
+        ]
+        data.append(chr(0x14444))
+        data.append(literal_eval("{2.3j, 1 - 2.3j, ()}"))
+        G = nx.Graph()
+        G.name = data
+        G.graph["data"] = data
+        G.add_node(0, int=-1, data={"data": data})
+        G.add_edge(0, 0, float=-2.5, data=data)
+        gml = "\n".join(nx.generate_gml(G, stringizer=literal_stringizer))
+        G = nx.parse_gml(gml, destringizer=literal_destringizer)
+        assert data == G.name
+        assert {"name": data, "data": data} == G.graph
+        assert list(G.nodes(data=True)) == [(0, {"int": -1, "data": {"data": data}})]
+        assert list(G.edges(data=True)) == [(0, 0, {"float": -2.5, "data": data})]
+        G = nx.Graph()
+        G.graph["data"] = "frozenset([1, 2, 3])"
+        G = nx.parse_gml(nx.generate_gml(G), destringizer=literal_eval)
+        assert G.graph["data"] == "frozenset([1, 2, 3])"
+
+    def test_escape_unescape(self):
+        gml = """graph [
+  name "&"䑄��&unknown;"
+]"""
+        G = nx.parse_gml(gml)
+        assert (
+            '&"\x0f' + chr(0x4444) + "��&unknown;"
+            == G.name
+        )
+        gml = "\n".join(nx.generate_gml(G))
+        alnu = "#1234567890;&#x1234567890abcdef"
+        answer = (
+            """graph [
+  name "&"䑄&"""
+            + alnu
+            + """;&unknown;"
+]"""
+        )
+        assert answer == gml
+
+    def test_exceptions(self, tmp_path):
+        pytest.raises(ValueError, literal_destringizer, "(")
+        pytest.raises(ValueError, literal_destringizer, "frozenset([1, 2, 3])")
+        pytest.raises(ValueError, literal_destringizer, literal_destringizer)
+        pytest.raises(ValueError, literal_stringizer, frozenset([1, 2, 3]))
+        pytest.raises(ValueError, literal_stringizer, literal_stringizer)
+        with open(tmp_path / "test.gml", "w+b") as f:
+            f.write(codecs.BOM_UTF8 + b"graph[]")
+            f.seek(0)
+            pytest.raises(nx.NetworkXError, nx.read_gml, f)
+
+        def assert_parse_error(gml):
+            pytest.raises(nx.NetworkXError, nx.parse_gml, gml)
+
+        assert_parse_error(["graph [\n\n", "]"])
+        assert_parse_error("")
+        assert_parse_error('Creator ""')
+        assert_parse_error("0")
+        assert_parse_error("graph ]")
+        assert_parse_error("graph [ 1 ]")
+        assert_parse_error("graph [ 1.E+2 ]")
+        assert_parse_error('graph [ "A" ]')
+        assert_parse_error("graph [ ] graph ]")
+        assert_parse_error("graph [ ] graph [ ]")
+        assert_parse_error("graph [ data [1, 2, 3] ]")
+        assert_parse_error("graph [ node [ ] ]")
+        assert_parse_error("graph [ node [ id 0 ] ]")
+        nx.parse_gml('graph [ node [ id "a" ] ]', label="id")
+        assert_parse_error("graph [ node [ id 0 label 0 ] node [ id 0 label 1 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] node [ id 1 label 0 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ source 0 ] ]")
+        nx.parse_gml("graph [edge [ source 0 target 0 ] node [ id 0 label 0 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ source 1 target 0 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ source 0 target 1 ] ]")
+        assert_parse_error(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 ] edge [ source 1 target 0 ] ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 ] edge [ source 1 target 0 ] "
+            "directed 1 ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 ] edge [ source 0 target 1 ]"
+            "multigraph 1 ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 key 0 ] edge [ source 0 target 1 ]"
+            "multigraph 1 ]"
+        )
+        assert_parse_error(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 key 0 ] edge [ source 0 target 1 key 0 ]"
+            "multigraph 1 ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 key 0 ] edge [ source 1 target 0 key 0 ]"
+            "directed 1 multigraph 1 ]"
+        )
+
+        # Tests for string convertible alphanumeric id and label values
+        nx.parse_gml("graph [edge [ source a target a ] node [ id a label b ] ]")
+        nx.parse_gml(
+            "graph [ node [ id n42 label 0 ] node [ id x43 label 1 ]"
+            "edge [ source n42 target x43 key 0 ]"
+            "edge [ source x43 target n42 key 0 ]"
+            "directed 1 multigraph 1 ]"
+        )
+        assert_parse_error(
+            "graph [edge [ source '\u4200' target '\u4200' ] "
+            + "node [ id '\u4200' label b ] ]"
+        )
+
+        def assert_generate_error(*args, **kwargs):
+            pytest.raises(
+                nx.NetworkXError, lambda: list(nx.generate_gml(*args, **kwargs))
+            )
+
+        G = nx.Graph()
+        G.graph[3] = 3
+        assert_generate_error(G)
+        G = nx.Graph()
+        G.graph["3"] = 3
+        assert_generate_error(G)
+        G = nx.Graph()
+        G.graph["data"] = frozenset([1, 2, 3])
+        assert_generate_error(G, stringizer=literal_stringizer)
+
+    def test_label_kwarg(self):
+        G = nx.parse_gml(self.simple_data, label="id")
+        assert sorted(G.nodes) == [1, 2, 3]
+        labels = [G.nodes[n]["label"] for n in sorted(G.nodes)]
+        assert labels == ["Node 1", "Node 2", "Node 3"]
+
+        G = nx.parse_gml(self.simple_data, label=None)
+        assert sorted(G.nodes) == [1, 2, 3]
+        labels = [G.nodes[n]["label"] for n in sorted(G.nodes)]
+        assert labels == ["Node 1", "Node 2", "Node 3"]
+
+    def test_outofrange_integers(self, tmp_path):
+        # GML restricts integers to 32 signed bits.
+        # Check that we honor this restriction on export
+        G = nx.Graph()
+        # Test export for numbers that barely fit or don't fit into 32 bits,
+        # and 3 numbers in the middle
+        numbers = {
+            "toosmall": (-(2**31)) - 1,
+            "small": -(2**31),
+            "med1": -4,
+            "med2": 0,
+            "med3": 17,
+            "big": (2**31) - 1,
+            "toobig": 2**31,
+        }
+        G.add_node("Node", **numbers)
+
+        fname = tmp_path / "test.gml"
+        nx.write_gml(G, fname)
+        # Check that the export wrote the nonfitting numbers as strings
+        G2 = nx.read_gml(fname)
+        for attr, value in G2.nodes["Node"].items():
+            if attr == "toosmall" or attr == "toobig":
+                assert type(value) == str
+            else:
+                assert type(value) == int
+
+    def test_multiline(self):
+        # example from issue #6836
+        multiline_example = """
+graph
+[
+    node
+    [
+	    id 0
+	    label "multiline node"
+	    label2 "multiline1
+    multiline2
+    multiline3"
+	    alt_name "id 0"
+    ]
+]
+"""
+        G = nx.parse_gml(multiline_example)
+        assert G.nodes["multiline node"] == {
+            "label2": "multiline1 multiline2 multiline3",
+            "alt_name": "id 0",
+        }
+
+
+@contextmanager
+def byte_file():
+    _file_handle = io.BytesIO()
+    yield _file_handle
+    _file_handle.seek(0)
+
+
+class TestPropertyLists:
+    def test_writing_graph_with_multi_element_property_list(self):
+        g = nx.Graph()
+        g.add_node("n1", properties=["element", 0, 1, 2.5, True, False])
+        with byte_file() as f:
+            nx.write_gml(g, f)
+        result = f.read().decode()
+
+        assert result == dedent(
+            """\
+            graph [
+              node [
+                id 0
+                label "n1"
+                properties "element"
+                properties 0
+                properties 1
+                properties 2.5
+                properties 1
+                properties 0
+              ]
+            ]
+        """
+        )
+
+    def test_writing_graph_with_one_element_property_list(self):
+        g = nx.Graph()
+        g.add_node("n1", properties=["element"])
+        with byte_file() as f:
+            nx.write_gml(g, f)
+        result = f.read().decode()
+
+        assert result == dedent(
+            """\
+            graph [
+              node [
+                id 0
+                label "n1"
+                properties "_networkx_list_start"
+                properties "element"
+              ]
+            ]
+        """
+        )
+
+    def test_reading_graph_with_list_property(self):
+        with byte_file() as f:
+            f.write(
+                dedent(
+                    """
+              graph [
+                node [
+                  id 0
+                  label "n1"
+                  properties "element"
+                  properties 0
+                  properties 1
+                  properties 2.5
+                ]
+              ]
+            """
+                ).encode("ascii")
+            )
+            f.seek(0)
+            graph = nx.read_gml(f)
+        assert graph.nodes(data=True)["n1"] == {"properties": ["element", 0, 1, 2.5]}
+
+    def test_reading_graph_with_single_element_list_property(self):
+        with byte_file() as f:
+            f.write(
+                dedent(
+                    """
+              graph [
+                node [
+                  id 0
+                  label "n1"
+                  properties "_networkx_list_start"
+                  properties "element"
+                ]
+              ]
+            """
+                ).encode("ascii")
+            )
+            f.seek(0)
+            graph = nx.read_gml(f)
+        assert graph.nodes(data=True)["n1"] == {"properties": ["element"]}
+
+
+@pytest.mark.parametrize("coll", ([], ()))
+def test_stringize_empty_list_tuple(coll):
+    G = nx.path_graph(2)
+    G.nodes[0]["test"] = coll  # test serializing an empty collection
+    f = io.BytesIO()
+    nx.write_gml(G, f)  # Smoke test - should not raise
+    f.seek(0)
+    H = nx.read_gml(f)
+    assert H.nodes["0"]["test"] == coll  # Check empty list round-trips properly
+    # Check full round-tripping. Note that nodes are loaded as strings by
+    # default, so there needs to be some remapping prior to comparison
+    H = nx.relabel_nodes(H, {"0": 0, "1": 1})
+    assert nx.utils.graphs_equal(G, H)
+    # Same as above, but use destringizer for node remapping. Should have no
+    # effect on node attr
+    f.seek(0)
+    H = nx.read_gml(f, destringizer=int)
+    assert nx.utils.graphs_equal(G, H)

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_graph6.py

@@ -0,0 +1,168 @@
+from io import BytesIO
+
+import pytest
+
+import networkx as nx
+import networkx.readwrite.graph6 as g6
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestGraph6Utils:
+    def test_n_data_n_conversion(self):
+        for i in [0, 1, 42, 62, 63, 64, 258047, 258048, 7744773, 68719476735]:
+            assert g6.data_to_n(g6.n_to_data(i))[0] == i
+            assert g6.data_to_n(g6.n_to_data(i))[1] == []
+            assert g6.data_to_n(g6.n_to_data(i) + [42, 43])[1] == [42, 43]
+
+
+class TestFromGraph6Bytes:
+    def test_from_graph6_bytes(self):
+        data = b"DF{"
+        G = nx.from_graph6_bytes(data)
+        assert nodes_equal(G.nodes(), [0, 1, 2, 3, 4])
+        assert edges_equal(
+            G.edges(), [(0, 3), (0, 4), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        )
+
+    def test_read_equals_from_bytes(self):
+        data = b"DF{"
+        G = nx.from_graph6_bytes(data)
+        fh = BytesIO(data)
+        Gin = nx.read_graph6(fh)
+        assert nodes_equal(G.nodes(), Gin.nodes())
+        assert edges_equal(G.edges(), Gin.edges())
+
+
+class TestReadGraph6:
+    def test_read_many_graph6(self):
+        """Test for reading many graphs from a file into a list."""
+        data = b"DF{\nD`{\nDqK\nD~{\n"
+        fh = BytesIO(data)
+        glist = nx.read_graph6(fh)
+        assert len(glist) == 4
+        for G in glist:
+            assert sorted(G) == list(range(5))
+
+
+class TestWriteGraph6:
+    """Unit tests for writing a graph to a file in graph6 format."""
+
+    def test_null_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.null_graph(), result)
+        assert result.getvalue() == b">>graph6<<?\n"
+
+    def test_trivial_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.trivial_graph(), result)
+        assert result.getvalue() == b">>graph6<<@\n"
+
+    def test_complete_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.complete_graph(4), result)
+        assert result.getvalue() == b">>graph6<<C~\n"
+
+    def test_large_complete_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.complete_graph(67), result, header=False)
+        assert result.getvalue() == b"~?@B" + b"~" * 368 + b"w\n"
+
+    def test_no_header(self):
+        result = BytesIO()
+        nx.write_graph6(nx.complete_graph(4), result, header=False)
+        assert result.getvalue() == b"C~\n"
+
+    def test_complete_bipartite_graph(self):
+        result = BytesIO()
+        G = nx.complete_bipartite_graph(6, 9)
+        nx.write_graph6(G, result, header=False)
+        # The expected encoding here was verified by Sage.
+        assert result.getvalue() == b"N??F~z{~Fw^_~?~?^_?\n"
+
+    @pytest.mark.parametrize("G", (nx.MultiGraph(), nx.DiGraph()))
+    def test_no_directed_or_multi_graphs(self, G):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.write_graph6(G, BytesIO())
+
+    def test_length(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            g = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            gstr = BytesIO()
+            nx.write_graph6(g, gstr, header=False)
+            # Strip the trailing newline.
+            gstr = gstr.getvalue().rstrip()
+            assert len(gstr) == ((i - 1) * i // 2 + 5) // 6 + (1 if i < 63 else 4)
+
+    def test_roundtrip(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            G = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            f = BytesIO()
+            nx.write_graph6(G, f)
+            f.seek(0)
+            H = nx.read_graph6(f)
+            assert nodes_equal(G.nodes(), H.nodes())
+            assert edges_equal(G.edges(), H.edges())
+
+    def test_write_path(self, tmp_path):
+        with open(tmp_path / "test.g6", "w+b") as f:
+            g6.write_graph6_file(nx.null_graph(), f)
+            f.seek(0)
+            assert f.read() == b">>graph6<<?\n"
+
+    @pytest.mark.parametrize("edge", ((0, 1), (1, 2), (1, 42)))
+    def test_relabeling(self, edge):
+        G = nx.Graph([edge])
+        f = BytesIO()
+        nx.write_graph6(G, f)
+        f.seek(0)
+        assert f.read() == b">>graph6<<A_\n"
+
+
+class TestToGraph6Bytes:
+    def test_null_graph(self):
+        G = nx.null_graph()
+        assert g6.to_graph6_bytes(G) == b">>graph6<<?\n"
+
+    def test_trivial_graph(self):
+        G = nx.trivial_graph()
+        assert g6.to_graph6_bytes(G) == b">>graph6<<@\n"
+
+    def test_complete_graph(self):
+        assert g6.to_graph6_bytes(nx.complete_graph(4)) == b">>graph6<<C~\n"
+
+    def test_large_complete_graph(self):
+        G = nx.complete_graph(67)
+        assert g6.to_graph6_bytes(G, header=False) == b"~?@B" + b"~" * 368 + b"w\n"
+
+    def test_no_header(self):
+        G = nx.complete_graph(4)
+        assert g6.to_graph6_bytes(G, header=False) == b"C~\n"
+
+    def test_complete_bipartite_graph(self):
+        G = nx.complete_bipartite_graph(6, 9)
+        assert g6.to_graph6_bytes(G, header=False) == b"N??F~z{~Fw^_~?~?^_?\n"
+
+    @pytest.mark.parametrize("G", (nx.MultiGraph(), nx.DiGraph()))
+    def test_no_directed_or_multi_graphs(self, G):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            g6.to_graph6_bytes(G)
+
+    def test_length(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            G = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            # Strip the trailing newline.
+            gstr = g6.to_graph6_bytes(G, header=False).rstrip()
+            assert len(gstr) == ((i - 1) * i // 2 + 5) // 6 + (1 if i < 63 else 4)
+
+    def test_roundtrip(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            G = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            data = g6.to_graph6_bytes(G)
+            H = nx.from_graph6_bytes(data.rstrip())
+            assert nodes_equal(G.nodes(), H.nodes())
+            assert edges_equal(G.edges(), H.edges())
+
+    @pytest.mark.parametrize("edge", ((0, 1), (1, 2), (1, 42)))
+    def test_relabeling(self, edge):
+        G = nx.Graph([edge])
+        assert g6.to_graph6_bytes(G) == b">>graph6<<A_\n"

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_graphml.py

@@ -0,0 +1,1531 @@
+import io
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.graphml import GraphMLWriter
+from networkx.utils import edges_equal, nodes_equal
+
+
+class BaseGraphML:
+    @classmethod
+    def setup_class(cls):
+        cls.simple_directed_data = """<?xml version="1.0" encoding="UTF-8"?>
+<!-- This file was written by the JAVA GraphML Library.-->
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G" edgedefault="directed">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n3"/>
+    <node id="n4"/>
+    <node id="n5"/>
+    <node id="n6"/>
+    <node id="n7"/>
+    <node id="n8"/>
+    <node id="n9"/>
+    <node id="n10"/>
+    <edge id="foo" source="n0" target="n2"/>
+    <edge source="n1" target="n2"/>
+    <edge source="n2" target="n3"/>
+    <edge source="n3" target="n5"/>
+    <edge source="n3" target="n4"/>
+    <edge source="n4" target="n6"/>
+    <edge source="n6" target="n5"/>
+    <edge source="n5" target="n7"/>
+    <edge source="n6" target="n8"/>
+    <edge source="n8" target="n7"/>
+    <edge source="n8" target="n9"/>
+  </graph>
+</graphml>"""
+        cls.simple_directed_graph = nx.DiGraph()
+        cls.simple_directed_graph.add_node("n10")
+        cls.simple_directed_graph.add_edge("n0", "n2", id="foo")
+        cls.simple_directed_graph.add_edge("n0", "n2")
+        cls.simple_directed_graph.add_edges_from(
+            [
+                ("n1", "n2"),
+                ("n2", "n3"),
+                ("n3", "n5"),
+                ("n3", "n4"),
+                ("n4", "n6"),
+                ("n6", "n5"),
+                ("n5", "n7"),
+                ("n6", "n8"),
+                ("n8", "n7"),
+                ("n8", "n9"),
+            ]
+        )
+        cls.simple_directed_fh = io.BytesIO(cls.simple_directed_data.encode("UTF-8"))
+
+        cls.attribute_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+      xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+      xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+        http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="color" attr.type="string">
+    <default>yellow</default>
+  </key>
+  <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">green</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">blue</data>
+    </node>
+    <node id="n3">
+      <data key="d0">red</data>
+    </node>
+    <node id="n4"/>
+    <node id="n5">
+      <data key="d0">turquoise</data>
+    </node>
+    <edge id="e0" source="n0" target="n2">
+      <data key="d1">1.0</data>
+    </edge>
+    <edge id="e1" source="n0" target="n1">
+      <data key="d1">1.0</data>
+    </edge>
+    <edge id="e2" source="n1" target="n3">
+      <data key="d1">2.0</data>
+    </edge>
+    <edge id="e3" source="n3" target="n2"/>
+    <edge id="e4" source="n2" target="n4"/>
+    <edge id="e5" source="n3" target="n5"/>
+    <edge id="e6" source="n5" target="n4">
+      <data key="d1">1.1</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        cls.attribute_graph = nx.DiGraph(id="G")
+        cls.attribute_graph.graph["node_default"] = {"color": "yellow"}
+        cls.attribute_graph.add_node("n0", color="green")
+        cls.attribute_graph.add_node("n2", color="blue")
+        cls.attribute_graph.add_node("n3", color="red")
+        cls.attribute_graph.add_node("n4")
+        cls.attribute_graph.add_node("n5", color="turquoise")
+        cls.attribute_graph.add_edge("n0", "n2", id="e0", weight=1.0)
+        cls.attribute_graph.add_edge("n0", "n1", id="e1", weight=1.0)
+        cls.attribute_graph.add_edge("n1", "n3", id="e2", weight=2.0)
+        cls.attribute_graph.add_edge("n3", "n2", id="e3")
+        cls.attribute_graph.add_edge("n2", "n4", id="e4")
+        cls.attribute_graph.add_edge("n3", "n5", id="e5")
+        cls.attribute_graph.add_edge("n5", "n4", id="e6", weight=1.1)
+        cls.attribute_fh = io.BytesIO(cls.attribute_data.encode("UTF-8"))
+
+        cls.node_attribute_default_data = """<?xml version="1.0" encoding="UTF-8"?>
+        <graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+              xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+              xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+                http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+          <key id="d0" for="node" attr.name="boolean_attribute" attr.type="boolean"><default>false</default></key>
+          <key id="d1" for="node" attr.name="int_attribute" attr.type="int"><default>0</default></key>
+          <key id="d2" for="node" attr.name="long_attribute" attr.type="long"><default>0</default></key>
+          <key id="d3" for="node" attr.name="float_attribute" attr.type="float"><default>0.0</default></key>
+          <key id="d4" for="node" attr.name="double_attribute" attr.type="double"><default>0.0</default></key>
+          <key id="d5" for="node" attr.name="string_attribute" attr.type="string"><default>Foo</default></key>
+          <graph id="G" edgedefault="directed">
+            <node id="n0"/>
+            <node id="n1"/>
+            <edge id="e0" source="n0" target="n1"/>
+          </graph>
+        </graphml>
+        """
+        cls.node_attribute_default_graph = nx.DiGraph(id="G")
+        cls.node_attribute_default_graph.graph["node_default"] = {
+            "boolean_attribute": False,
+            "int_attribute": 0,
+            "long_attribute": 0,
+            "float_attribute": 0.0,
+            "double_attribute": 0.0,
+            "string_attribute": "Foo",
+        }
+        cls.node_attribute_default_graph.add_node("n0")
+        cls.node_attribute_default_graph.add_node("n1")
+        cls.node_attribute_default_graph.add_edge("n0", "n1", id="e0")
+        cls.node_attribute_default_fh = io.BytesIO(
+            cls.node_attribute_default_data.encode("UTF-8")
+        )
+
+        cls.attribute_named_key_ids_data = """<?xml version='1.0' encoding='utf-8'?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="edge_prop" for="edge" attr.name="edge_prop" attr.type="string"/>
+  <key id="prop2" for="node" attr.name="prop2" attr.type="string"/>
+  <key id="prop1" for="node" attr.name="prop1" attr.type="string"/>
+  <graph edgedefault="directed">
+    <node id="0">
+      <data key="prop1">val1</data>
+      <data key="prop2">val2</data>
+    </node>
+    <node id="1">
+      <data key="prop1">val_one</data>
+      <data key="prop2">val2</data>
+    </node>
+    <edge source="0" target="1">
+      <data key="edge_prop">edge_value</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        cls.attribute_named_key_ids_graph = nx.DiGraph()
+        cls.attribute_named_key_ids_graph.add_node("0", prop1="val1", prop2="val2")
+        cls.attribute_named_key_ids_graph.add_node("1", prop1="val_one", prop2="val2")
+        cls.attribute_named_key_ids_graph.add_edge("0", "1", edge_prop="edge_value")
+        fh = io.BytesIO(cls.attribute_named_key_ids_data.encode("UTF-8"))
+        cls.attribute_named_key_ids_fh = fh
+
+        cls.attribute_numeric_type_data = """<?xml version='1.0' encoding='utf-8'?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key attr.name="weight" attr.type="double" for="node" id="d1" />
+  <key attr.name="weight" attr.type="double" for="edge" id="d0" />
+  <graph edgedefault="directed">
+    <node id="n0">
+      <data key="d1">1</data>
+    </node>
+    <node id="n1">
+      <data key="d1">2.0</data>
+    </node>
+    <edge source="n0" target="n1">
+      <data key="d0">1</data>
+    </edge>
+    <edge source="n1" target="n0">
+      <data key="d0">k</data>
+    </edge>
+    <edge source="n1" target="n1">
+      <data key="d0">1.0</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        cls.attribute_numeric_type_graph = nx.DiGraph()
+        cls.attribute_numeric_type_graph.add_node("n0", weight=1)
+        cls.attribute_numeric_type_graph.add_node("n1", weight=2.0)
+        cls.attribute_numeric_type_graph.add_edge("n0", "n1", weight=1)
+        cls.attribute_numeric_type_graph.add_edge("n1", "n1", weight=1.0)
+        fh = io.BytesIO(cls.attribute_numeric_type_data.encode("UTF-8"))
+        cls.attribute_numeric_type_fh = fh
+
+        cls.simple_undirected_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge id="foo" source="n0" target="n2"/>
+    <edge source="n1" target="n2"/>
+    <edge source="n2" target="n3"/>
+  </graph>
+</graphml>"""
+        #    <edge source="n8" target="n10" directed="false"/>
+        cls.simple_undirected_graph = nx.Graph()
+        cls.simple_undirected_graph.add_node("n10")
+        cls.simple_undirected_graph.add_edge("n0", "n2", id="foo")
+        cls.simple_undirected_graph.add_edges_from([("n1", "n2"), ("n2", "n3")])
+        fh = io.BytesIO(cls.simple_undirected_data.encode("UTF-8"))
+        cls.simple_undirected_fh = fh
+
+        cls.undirected_multigraph_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge id="e0" source="n0" target="n2"/>
+    <edge id="e1" source="n1" target="n2"/>
+    <edge id="e2" source="n2" target="n1"/>
+  </graph>
+</graphml>"""
+        cls.undirected_multigraph = nx.MultiGraph()
+        cls.undirected_multigraph.add_node("n10")
+        cls.undirected_multigraph.add_edge("n0", "n2", id="e0")
+        cls.undirected_multigraph.add_edge("n1", "n2", id="e1")
+        cls.undirected_multigraph.add_edge("n2", "n1", id="e2")
+        fh = io.BytesIO(cls.undirected_multigraph_data.encode("UTF-8"))
+        cls.undirected_multigraph_fh = fh
+
+        cls.undirected_multigraph_no_multiedge_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge id="e0" source="n0" target="n2"/>
+    <edge id="e1" source="n1" target="n2"/>
+    <edge id="e2" source="n2" target="n3"/>
+  </graph>
+</graphml>"""
+        cls.undirected_multigraph_no_multiedge = nx.MultiGraph()
+        cls.undirected_multigraph_no_multiedge.add_node("n10")
+        cls.undirected_multigraph_no_multiedge.add_edge("n0", "n2", id="e0")
+        cls.undirected_multigraph_no_multiedge.add_edge("n1", "n2", id="e1")
+        cls.undirected_multigraph_no_multiedge.add_edge("n2", "n3", id="e2")
+        fh = io.BytesIO(cls.undirected_multigraph_no_multiedge_data.encode("UTF-8"))
+        cls.undirected_multigraph_no_multiedge_fh = fh
+
+        cls.multigraph_only_ids_for_multiedges_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge source="n0" target="n2"/>
+    <edge id="e1" source="n1" target="n2"/>
+    <edge id="e2" source="n2" target="n1"/>
+  </graph>
+</graphml>"""
+        cls.multigraph_only_ids_for_multiedges = nx.MultiGraph()
+        cls.multigraph_only_ids_for_multiedges.add_node("n10")
+        cls.multigraph_only_ids_for_multiedges.add_edge("n0", "n2")
+        cls.multigraph_only_ids_for_multiedges.add_edge("n1", "n2", id="e1")
+        cls.multigraph_only_ids_for_multiedges.add_edge("n2", "n1", id="e2")
+        fh = io.BytesIO(cls.multigraph_only_ids_for_multiedges_data.encode("UTF-8"))
+        cls.multigraph_only_ids_for_multiedges_fh = fh
+
+
+class TestReadGraphML(BaseGraphML):
+    def test_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        H = nx.read_graphml(self.simple_directed_fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+        PG = nx.parse_graphml(self.simple_directed_data)
+        assert sorted(G.nodes()) == sorted(PG.nodes())
+        assert sorted(G.edges()) == sorted(PG.edges())
+        assert sorted(G.edges(data=True)) == sorted(PG.edges(data=True))
+
+    def test_read_simple_undirected_graphml(self):
+        G = self.simple_undirected_graph
+        H = nx.read_graphml(self.simple_undirected_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.simple_undirected_fh.seek(0)
+
+        PG = nx.parse_graphml(self.simple_undirected_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_undirected_multigraph_graphml(self):
+        G = self.undirected_multigraph
+        H = nx.read_graphml(self.undirected_multigraph_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.undirected_multigraph_fh.seek(0)
+
+        PG = nx.parse_graphml(self.undirected_multigraph_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_undirected_multigraph_no_multiedge_graphml(self):
+        G = self.undirected_multigraph_no_multiedge
+        H = nx.read_graphml(self.undirected_multigraph_no_multiedge_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.undirected_multigraph_no_multiedge_fh.seek(0)
+
+        PG = nx.parse_graphml(self.undirected_multigraph_no_multiedge_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_undirected_multigraph_only_ids_for_multiedges_graphml(self):
+        G = self.multigraph_only_ids_for_multiedges
+        H = nx.read_graphml(self.multigraph_only_ids_for_multiedges_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.multigraph_only_ids_for_multiedges_fh.seek(0)
+
+        PG = nx.parse_graphml(self.multigraph_only_ids_for_multiedges_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_attribute_graphml(self):
+        G = self.attribute_graph
+        H = nx.read_graphml(self.attribute_fh)
+        assert nodes_equal(G.nodes(True), sorted(H.nodes(data=True)))
+        ge = sorted(G.edges(data=True))
+        he = sorted(H.edges(data=True))
+        for a, b in zip(ge, he):
+            assert a == b
+        self.attribute_fh.seek(0)
+
+        PG = nx.parse_graphml(self.attribute_data)
+        assert sorted(G.nodes(True)) == sorted(PG.nodes(data=True))
+        ge = sorted(G.edges(data=True))
+        he = sorted(PG.edges(data=True))
+        for a, b in zip(ge, he):
+            assert a == b
+
+    def test_node_default_attribute_graphml(self):
+        G = self.node_attribute_default_graph
+        H = nx.read_graphml(self.node_attribute_default_fh)
+        assert G.graph["node_default"] == H.graph["node_default"]
+
+    def test_directed_edge_in_undirected(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <edge source="n0" target="n1"/>
+    <edge source="n1" target="n2" directed='true'/>
+  </graph>
+</graphml>"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_undirected_edge_in_directed(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G" edgedefault='directed'>
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <edge source="n0" target="n1"/>
+    <edge source="n1" target="n2" directed='false'/>
+  </graph>
+</graphml>"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_key_raise(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="color" attr.type="string">
+    <default>yellow</default>
+  </key>
+  <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">green</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">blue</data>
+    </node>
+    <edge id="e0" source="n0" target="n2">
+      <data key="d2">1.0</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_hyperedge_raise(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="color" attr.type="string">
+    <default>yellow</default>
+  </key>
+  <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">green</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">blue</data>
+    </node>
+    <hyperedge id="e0" source="n0" target="n2">
+       <endpoint node="n0"/>
+       <endpoint node="n1"/>
+       <endpoint node="n2"/>
+    </hyperedge>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_multigraph_keys(self):
+        # Test that reading multigraphs uses edge id attributes as keys
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G" edgedefault="directed">
+    <node id="n0"/>
+    <node id="n1"/>
+    <edge id="e0" source="n0" target="n1"/>
+    <edge id="e1" source="n0" target="n1"/>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        expected = [("n0", "n1", "e0"), ("n0", "n1", "e1")]
+        assert sorted(G.edges(keys=True)) == expected
+        fh.seek(0)
+        H = nx.parse_graphml(s)
+        assert sorted(H.edges(keys=True)) == expected
+
+    def test_preserve_multi_edge_data(self):
+        """
+        Test that data and keys of edges are preserved on consequent
+        write and reads
+        """
+        G = nx.MultiGraph()
+        G.add_node(1)
+        G.add_node(2)
+        G.add_edges_from(
+            [
+                # edges with no data, no keys:
+                (1, 2),
+                # edges with only data:
+                (1, 2, {"key": "data_key1"}),
+                (1, 2, {"id": "data_id2"}),
+                (1, 2, {"key": "data_key3", "id": "data_id3"}),
+                # edges with both data and keys:
+                (1, 2, 103, {"key": "data_key4"}),
+                (1, 2, 104, {"id": "data_id5"}),
+                (1, 2, 105, {"key": "data_key6", "id": "data_id7"}),
+            ]
+        )
+        fh = io.BytesIO()
+        nx.write_graphml(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh, node_type=int)
+        assert edges_equal(G.edges(data=True, keys=True), H.edges(data=True, keys=True))
+        assert G._adj == H._adj
+
+        Gadj = {
+            str(node): {
+                str(nbr): {str(ekey): dd for ekey, dd in key_dict.items()}
+                for nbr, key_dict in nbr_dict.items()
+            }
+            for node, nbr_dict in G._adj.items()
+        }
+        fh.seek(0)
+        HH = nx.read_graphml(fh, node_type=str, edge_key_type=str)
+        assert Gadj == HH._adj
+
+        fh.seek(0)
+        string_fh = fh.read()
+        HH = nx.parse_graphml(string_fh, node_type=str, edge_key_type=str)
+        assert Gadj == HH._adj
+
+    def test_yfiles_extension(self):
+        data = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xmlns:y="http://www.yworks.com/xml/graphml"
+         xmlns:yed="http://www.yworks.com/xml/yed/3"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <!--Created by yFiles for Java 2.7-->
+  <key for="graphml" id="d0" yfiles.type="resources"/>
+  <key attr.name="url" attr.type="string" for="node" id="d1"/>
+  <key attr.name="description" attr.type="string" for="node" id="d2"/>
+  <key for="node" id="d3" yfiles.type="nodegraphics"/>
+  <key attr.name="Description" attr.type="string" for="graph" id="d4">
+    <default/>
+  </key>
+  <key attr.name="url" attr.type="string" for="edge" id="d5"/>
+  <key attr.name="description" attr.type="string" for="edge" id="d6"/>
+  <key for="edge" id="d7" yfiles.type="edgegraphics"/>
+  <graph edgedefault="directed" id="G">
+    <node id="n0">
+      <data key="d3">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="125.0" y="100.0"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content"
+           borderDistance="0.0" fontFamily="Dialog" fontSize="13"
+           fontStyle="plain" hasBackgroundColor="false" hasLineColor="false"
+           height="19.1328125" modelName="internal" modelPosition="c"
+           textColor="#000000" visible="true" width="12.27099609375"
+           x="8.864501953125" y="5.43359375">1</y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <node id="n1">
+      <data key="d3">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="183.0" y="205.0"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content"
+          borderDistance="0.0" fontFamily="Dialog" fontSize="13"
+          fontStyle="plain" hasBackgroundColor="false" hasLineColor="false"
+          height="19.1328125" modelName="internal" modelPosition="c"
+          textColor="#000000" visible="true" width="12.27099609375"
+          x="8.864501953125" y="5.43359375">2</y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <node id="n2">
+      <data key="d6" xml:space="preserve"><![CDATA[description
+line1
+line2]]></data>
+      <data key="d3">
+        <y:GenericNode configuration="com.yworks.flowchart.terminator">
+          <y:Geometry height="40.0" width="80.0" x="950.0" y="286.0"/>
+          <y:Fill color="#E8EEF7" color2="#B7C9E3" transparent="false"/>
+          <y:BorderStyle color="#000000" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content"
+          fontFamily="Dialog" fontSize="12" fontStyle="plain"
+          hasBackgroundColor="false" hasLineColor="false" height="17.96875"
+          horizontalTextPosition="center" iconTextGap="4" modelName="custom"
+          textColor="#000000" verticalTextPosition="bottom" visible="true"
+          width="67.984375" x="6.0078125" xml:space="preserve"
+          y="11.015625">3<y:LabelModel>
+          <y:SmartNodeLabelModel distance="4.0"/></y:LabelModel>
+          <y:ModelParameter><y:SmartNodeLabelModelParameter labelRatioX="0.0"
+          labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0"
+          offsetY="0.0" upX="0.0" upY="-1.0"/></y:ModelParameter></y:NodeLabel>
+        </y:GenericNode>
+      </data>
+    </node>
+    <edge id="e0" source="n0" target="n1">
+      <data key="d7">
+        <y:PolyLineEdge>
+          <y:Path sx="0.0" sy="0.0" tx="0.0" ty="0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+  </graph>
+  <data key="d0">
+    <y:Resources/>
+  </data>
+</graphml>
+"""
+        fh = io.BytesIO(data.encode("UTF-8"))
+        G = nx.read_graphml(fh, force_multigraph=True)
+        assert list(G.edges()) == [("n0", "n1")]
+        assert G.has_edge("n0", "n1", key="e0")
+        assert G.nodes["n0"]["label"] == "1"
+        assert G.nodes["n1"]["label"] == "2"
+        assert G.nodes["n2"]["label"] == "3"
+        assert G.nodes["n0"]["shape_type"] == "rectangle"
+        assert G.nodes["n1"]["shape_type"] == "rectangle"
+        assert G.nodes["n2"]["shape_type"] == "com.yworks.flowchart.terminator"
+        assert G.nodes["n2"]["description"] == "description\nline1\nline2"
+        fh.seek(0)
+        G = nx.read_graphml(fh)
+        assert list(G.edges()) == [("n0", "n1")]
+        assert G["n0"]["n1"]["id"] == "e0"
+        assert G.nodes["n0"]["label"] == "1"
+        assert G.nodes["n1"]["label"] == "2"
+        assert G.nodes["n2"]["label"] == "3"
+        assert G.nodes["n0"]["shape_type"] == "rectangle"
+        assert G.nodes["n1"]["shape_type"] == "rectangle"
+        assert G.nodes["n2"]["shape_type"] == "com.yworks.flowchart.terminator"
+        assert G.nodes["n2"]["description"] == "description\nline1\nline2"
+
+        H = nx.parse_graphml(data, force_multigraph=True)
+        assert list(H.edges()) == [("n0", "n1")]
+        assert H.has_edge("n0", "n1", key="e0")
+        assert H.nodes["n0"]["label"] == "1"
+        assert H.nodes["n1"]["label"] == "2"
+        assert H.nodes["n2"]["label"] == "3"
+
+        H = nx.parse_graphml(data)
+        assert list(H.edges()) == [("n0", "n1")]
+        assert H["n0"]["n1"]["id"] == "e0"
+        assert H.nodes["n0"]["label"] == "1"
+        assert H.nodes["n1"]["label"] == "2"
+        assert H.nodes["n2"]["label"] == "3"
+
+    def test_bool(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">false</data>
+    </node>
+    <node id="n3">
+      <data key="d0">FaLsE</data>
+    </node>
+    <node id="n4">
+      <data key="d0">True</data>
+    </node>
+    <node id="n5">
+      <data key="d0">0</data>
+    </node>
+    <node id="n6">
+      <data key="d0">1</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        H = nx.parse_graphml(s)
+        for graph in [G, H]:
+            assert graph.nodes["n0"]["test"]
+            assert not graph.nodes["n2"]["test"]
+            assert not graph.nodes["n3"]["test"]
+            assert graph.nodes["n4"]["test"]
+            assert not graph.nodes["n5"]["test"]
+            assert graph.nodes["n6"]["test"]
+
+    def test_graphml_header_line(self):
+        good = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        bad = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml>
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        ugly = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="https://ghghgh">
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        for s in (good, bad):
+            fh = io.BytesIO(s.encode("UTF-8"))
+            G = nx.read_graphml(fh)
+            H = nx.parse_graphml(s)
+            for graph in [G, H]:
+                assert graph.nodes["n0"]["test"]
+
+        fh = io.BytesIO(ugly.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, ugly)
+
+    def test_read_attributes_with_groups(self):
+        data = """\
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:java="http://www.yworks.com/xml/yfiles-common/1.0/java" xmlns:sys="http://www.yworks.com/xml/yfiles-common/markup/primitives/2.0" xmlns:x="http://www.yworks.com/xml/yfiles-common/markup/2.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:y="http://www.yworks.com/xml/graphml" xmlns:yed="http://www.yworks.com/xml/yed/3" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://www.yworks.com/xml/schema/graphml/1.1/ygraphml.xsd">
+  <!--Created by yEd 3.17-->
+  <key attr.name="Description" attr.type="string" for="graph" id="d0"/>
+  <key for="port" id="d1" yfiles.type="portgraphics"/>
+  <key for="port" id="d2" yfiles.type="portgeometry"/>
+  <key for="port" id="d3" yfiles.type="portuserdata"/>
+  <key attr.name="CustomProperty" attr.type="string" for="node" id="d4">
+    <default/>
+  </key>
+  <key attr.name="url" attr.type="string" for="node" id="d5"/>
+  <key attr.name="description" attr.type="string" for="node" id="d6"/>
+  <key for="node" id="d7" yfiles.type="nodegraphics"/>
+  <key for="graphml" id="d8" yfiles.type="resources"/>
+  <key attr.name="url" attr.type="string" for="edge" id="d9"/>
+  <key attr.name="description" attr.type="string" for="edge" id="d10"/>
+  <key for="edge" id="d11" yfiles.type="edgegraphics"/>
+  <graph edgedefault="directed" id="G">
+    <data key="d0"/>
+    <node id="n0">
+      <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+      <data key="d6"/>
+      <data key="d7">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="125.0" y="-255.4611111111111"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">2<y:LabelModel>
+              <y:SmartNodeLabelModel distance="4.0"/>
+            </y:LabelModel>
+            <y:ModelParameter>
+              <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+            </y:ModelParameter>
+          </y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <node id="n1" yfiles.foldertype="group">
+      <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+      <data key="d5"/>
+      <data key="d6"/>
+      <data key="d7">
+        <y:ProxyAutoBoundsNode>
+          <y:Realizers active="0">
+            <y:GroupNode>
+              <y:Geometry height="250.38333333333333" width="140.0" x="-30.0" y="-330.3833333333333"/>
+              <y:Fill color="#F5F5F5" transparent="false"/>
+              <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+              <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="140.0" x="0.0" y="0.0">Group 3</y:NodeLabel>
+              <y:Shape type="roundrectangle"/>
+              <y:State closed="false" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+              <y:Insets bottom="15" bottomF="15.0" left="15" leftF="15.0" right="15" rightF="15.0" top="15" topF="15.0"/>
+              <y:BorderInsets bottom="1" bottomF="1.0" left="0" leftF="0.0" right="0" rightF="0.0" top="1" topF="1.0001736111111086"/>
+            </y:GroupNode>
+            <y:GroupNode>
+              <y:Geometry height="50.0" width="50.0" x="0.0" y="60.0"/>
+              <y:Fill color="#F5F5F5" transparent="false"/>
+              <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+              <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="65.201171875" x="-7.6005859375" y="0.0">Folder 3</y:NodeLabel>
+              <y:Shape type="roundrectangle"/>
+              <y:State closed="true" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+              <y:Insets bottom="5" bottomF="5.0" left="5" leftF="5.0" right="5" rightF="5.0" top="5" topF="5.0"/>
+              <y:BorderInsets bottom="0" bottomF="0.0" left="0" leftF="0.0" right="0" rightF="0.0" top="0" topF="0.0"/>
+            </y:GroupNode>
+          </y:Realizers>
+        </y:ProxyAutoBoundsNode>
+      </data>
+      <graph edgedefault="directed" id="n1:">
+        <node id="n1::n0" yfiles.foldertype="group">
+          <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+          <data key="d5"/>
+          <data key="d6"/>
+          <data key="d7">
+            <y:ProxyAutoBoundsNode>
+              <y:Realizers active="0">
+                <y:GroupNode>
+                  <y:Geometry height="83.46111111111111" width="110.0" x="-15.0" y="-292.9222222222222"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="110.0" x="0.0" y="0.0">Group 1</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="false" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="15" bottomF="15.0" left="15" leftF="15.0" right="15" rightF="15.0" top="15" topF="15.0"/>
+                  <y:BorderInsets bottom="1" bottomF="1.0" left="0" leftF="0.0" right="0" rightF="0.0" top="1" topF="1.0001736111111086"/>
+                </y:GroupNode>
+                <y:GroupNode>
+                  <y:Geometry height="50.0" width="50.0" x="0.0" y="60.0"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="65.201171875" x="-7.6005859375" y="0.0">Folder 1</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="true" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="5" bottomF="5.0" left="5" leftF="5.0" right="5" rightF="5.0" top="5" topF="5.0"/>
+                  <y:BorderInsets bottom="0" bottomF="0.0" left="0" leftF="0.0" right="0" rightF="0.0" top="0" topF="0.0"/>
+                </y:GroupNode>
+              </y:Realizers>
+            </y:ProxyAutoBoundsNode>
+          </data>
+          <graph edgedefault="directed" id="n1::n0:">
+            <node id="n1::n0::n0">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="50.0" y="-255.4611111111111"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">1<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+            <node id="n1::n0::n1">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="0.0" y="-255.4611111111111"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">3<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+          </graph>
+        </node>
+        <node id="n1::n1" yfiles.foldertype="group">
+          <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+          <data key="d5"/>
+          <data key="d6"/>
+          <data key="d7">
+            <y:ProxyAutoBoundsNode>
+              <y:Realizers active="0">
+                <y:GroupNode>
+                  <y:Geometry height="83.46111111111111" width="110.0" x="-15.0" y="-179.4611111111111"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="110.0" x="0.0" y="0.0">Group 2</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="false" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="15" bottomF="15.0" left="15" leftF="15.0" right="15" rightF="15.0" top="15" topF="15.0"/>
+                  <y:BorderInsets bottom="1" bottomF="1.0" left="0" leftF="0.0" right="0" rightF="0.0" top="1" topF="1.0001736111111086"/>
+                </y:GroupNode>
+                <y:GroupNode>
+                  <y:Geometry height="50.0" width="50.0" x="0.0" y="60.0"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="65.201171875" x="-7.6005859375" y="0.0">Folder 2</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="true" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="5" bottomF="5.0" left="5" leftF="5.0" right="5" rightF="5.0" top="5" topF="5.0"/>
+                  <y:BorderInsets bottom="0" bottomF="0.0" left="0" leftF="0.0" right="0" rightF="0.0" top="0" topF="0.0"/>
+                </y:GroupNode>
+              </y:Realizers>
+            </y:ProxyAutoBoundsNode>
+          </data>
+          <graph edgedefault="directed" id="n1::n1:">
+            <node id="n1::n1::n0">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="0.0" y="-142.0"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">5<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+            <node id="n1::n1::n1">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="50.0" y="-142.0"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">6<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+          </graph>
+        </node>
+      </graph>
+    </node>
+    <node id="n2">
+      <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+      <data key="d6"/>
+      <data key="d7">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="125.0" y="-142.0"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">9<y:LabelModel>
+              <y:SmartNodeLabelModel distance="4.0"/>
+            </y:LabelModel>
+            <y:ModelParameter>
+              <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+            </y:ModelParameter>
+          </y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <edge id="n1::n1::e0" source="n1::n1::n0" target="n1::n1::n1">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+    <edge id="n1::n0::e0" source="n1::n0::n1" target="n1::n0::n0">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+    <edge id="e0" source="n1::n0::n0" target="n0">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+    <edge id="e1" source="n1::n1::n1" target="n2">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+  </graph>
+  <data key="d8">
+    <y:Resources/>
+  </data>
+</graphml>
+"""
+        # verify that nodes / attributes are correctly read when part of a group
+        fh = io.BytesIO(data.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        data = [x for _, x in G.nodes(data=True)]
+        assert len(data) == 9
+        for node_data in data:
+            assert node_data["CustomProperty"] != ""
+
+    def test_long_attribute_type(self):
+        # test that graphs with attr.type="long" (as produced by botch and
+        # dose3) can be parsed
+        s = """<?xml version='1.0' encoding='utf-8'?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key attr.name="cudfversion" attr.type="long" for="node" id="d6" />
+  <graph edgedefault="directed">
+    <node id="n1">
+      <data key="d6">4284</data>
+    </node>
+  </graph>
+</graphml>"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        expected = [("n1", {"cudfversion": 4284})]
+        assert sorted(G.nodes(data=True)) == expected
+        fh.seek(0)
+        H = nx.parse_graphml(s)
+        assert sorted(H.nodes(data=True)) == expected
+
+
+class TestWriteGraphML(BaseGraphML):
+    writer = staticmethod(nx.write_graphml_lxml)
+
+    @classmethod
+    def setup_class(cls):
+        BaseGraphML.setup_class()
+        _ = pytest.importorskip("lxml.etree")
+
+    def test_write_interface(self):
+        try:
+            import lxml.etree
+
+            assert nx.write_graphml == nx.write_graphml_lxml
+        except ImportError:
+            assert nx.write_graphml == nx.write_graphml_xml
+
+    def test_write_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        G.graph["hi"] = "there"
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_GraphMLWriter_add_graphs(self):
+        gmlw = GraphMLWriter()
+        G = self.simple_directed_graph
+        H = G.copy()
+        gmlw.add_graphs([G, H])
+
+    def test_write_read_simple_no_prettyprint(self):
+        G = self.simple_directed_graph
+        G.graph["hi"] = "there"
+        G.graph["id"] = "1"
+        fh = io.BytesIO()
+        self.writer(G, fh, prettyprint=False)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_write_read_attribute_named_key_ids_graphml(self):
+        from xml.etree.ElementTree import parse
+
+        G = self.attribute_named_key_ids_graph
+        fh = io.BytesIO()
+        self.writer(G, fh, named_key_ids=True)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        fh.seek(0)
+
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        assert edges_equal(G.edges(data=True), H.edges(data=True))
+        self.attribute_named_key_ids_fh.seek(0)
+
+        xml = parse(fh)
+        # Children are the key elements, and the graph element
+        children = list(xml.getroot())
+        assert len(children) == 4
+
+        keys = [child.items() for child in children[:3]]
+
+        assert len(keys) == 3
+        assert ("id", "edge_prop") in keys[0]
+        assert ("attr.name", "edge_prop") in keys[0]
+        assert ("id", "prop2") in keys[1]
+        assert ("attr.name", "prop2") in keys[1]
+        assert ("id", "prop1") in keys[2]
+        assert ("attr.name", "prop1") in keys[2]
+
+        # Confirm the read graph nodes/edge are identical when compared to
+        # default writing behavior.
+        default_behavior_fh = io.BytesIO()
+        nx.write_graphml(G, default_behavior_fh)
+        default_behavior_fh.seek(0)
+        H = nx.read_graphml(default_behavior_fh)
+
+        named_key_ids_behavior_fh = io.BytesIO()
+        nx.write_graphml(G, named_key_ids_behavior_fh, named_key_ids=True)
+        named_key_ids_behavior_fh.seek(0)
+        J = nx.read_graphml(named_key_ids_behavior_fh)
+
+        assert all(n1 == n2 for (n1, n2) in zip(H.nodes, J.nodes))
+        assert all(e1 == e2 for (e1, e2) in zip(H.edges, J.edges))
+
+    def test_write_read_attribute_numeric_type_graphml(self):
+        from xml.etree.ElementTree import parse
+
+        G = self.attribute_numeric_type_graph
+        fh = io.BytesIO()
+        self.writer(G, fh, infer_numeric_types=True)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        fh.seek(0)
+
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        assert edges_equal(G.edges(data=True), H.edges(data=True))
+        self.attribute_numeric_type_fh.seek(0)
+
+        xml = parse(fh)
+        # Children are the key elements, and the graph element
+        children = list(xml.getroot())
+        assert len(children) == 3
+
+        keys = [child.items() for child in children[:2]]
+
+        assert len(keys) == 2
+        assert ("attr.type", "double") in keys[0]
+        assert ("attr.type", "double") in keys[1]
+
+    def test_more_multigraph_keys(self, tmp_path):
+        """Writing keys as edge id attributes means keys become strings.
+        The original keys are stored as data, so read them back in
+        if `str(key) == edge_id`
+        This allows the adjacency to remain the same.
+        """
+        G = nx.MultiGraph()
+        G.add_edges_from([("a", "b", 2), ("a", "b", 3)])
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname)
+        assert H.is_multigraph()
+        assert edges_equal(G.edges(keys=True), H.edges(keys=True))
+        assert G._adj == H._adj
+
+    def test_default_attribute(self):
+        G = nx.Graph(name="Fred")
+        G.add_node(1, label=1, color="green")
+        nx.add_path(G, [0, 1, 2, 3])
+        G.add_edge(1, 2, weight=3)
+        G.graph["node_default"] = {"color": "yellow"}
+        G.graph["edge_default"] = {"weight": 7}
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh, node_type=int)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        assert G.graph == H.graph
+
+    def test_mixed_type_attributes(self):
+        G = nx.MultiGraph()
+        G.add_node("n0", special=False)
+        G.add_node("n1", special=0)
+        G.add_edge("n0", "n1", special=False)
+        G.add_edge("n0", "n1", special=0)
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert not H.nodes["n0"]["special"]
+        assert H.nodes["n1"]["special"] == 0
+        assert not H.edges["n0", "n1", 0]["special"]
+        assert H.edges["n0", "n1", 1]["special"] == 0
+
+    def test_str_number_mixed_type_attributes(self):
+        G = nx.MultiGraph()
+        G.add_node("n0", special="hello")
+        G.add_node("n1", special=0)
+        G.add_edge("n0", "n1", special="hello")
+        G.add_edge("n0", "n1", special=0)
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert H.nodes["n0"]["special"] == "hello"
+        assert H.nodes["n1"]["special"] == 0
+        assert H.edges["n0", "n1", 0]["special"] == "hello"
+        assert H.edges["n0", "n1", 1]["special"] == 0
+
+    def test_mixed_int_type_number_attributes(self):
+        np = pytest.importorskip("numpy")
+        G = nx.MultiGraph()
+        G.add_node("n0", special=np.int64(0))
+        G.add_node("n1", special=1)
+        G.add_edge("n0", "n1", special=np.int64(2))
+        G.add_edge("n0", "n1", special=3)
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert H.nodes["n0"]["special"] == 0
+        assert H.nodes["n1"]["special"] == 1
+        assert H.edges["n0", "n1", 0]["special"] == 2
+        assert H.edges["n0", "n1", 1]["special"] == 3
+
+    def test_multigraph_to_graph(self, tmp_path):
+        # test converting multigraph to graph if no parallel edges found
+        G = nx.MultiGraph()
+        G.add_edges_from([("a", "b", 2), ("b", "c", 3)])  # no multiedges
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname)
+        assert not H.is_multigraph()
+        H = nx.read_graphml(fname, force_multigraph=True)
+        assert H.is_multigraph()
+
+        # add a multiedge
+        G.add_edge("a", "b", "e-id")
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname)
+        assert H.is_multigraph()
+        H = nx.read_graphml(fname, force_multigraph=True)
+        assert H.is_multigraph()
+
+    def test_write_generate_edge_id_from_attribute(self, tmp_path):
+        from xml.etree.ElementTree import parse
+
+        G = nx.Graph()
+        G.add_edges_from([("a", "b"), ("b", "c"), ("a", "c")])
+        edge_attributes = {e: str(e) for e in G.edges}
+        nx.set_edge_attributes(G, edge_attributes, "eid")
+        fname = tmp_path / "test.graphml"
+        # set edge_id_from_attribute e.g. "eid" for write_graphml()
+        self.writer(G, fname, edge_id_from_attribute="eid")
+        # set edge_id_from_attribute e.g. "eid" for generate_graphml()
+        generator = nx.generate_graphml(G, edge_id_from_attribute="eid")
+
+        H = nx.read_graphml(fname)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        # NetworkX adds explicit edge "id" from file as attribute
+        nx.set_edge_attributes(G, edge_attributes, "id")
+        assert edges_equal(G.edges(data=True), H.edges(data=True))
+
+        tree = parse(fname)
+        children = list(tree.getroot())
+        assert len(children) == 2
+        edge_ids = [
+            edge.attrib["id"]
+            for edge in tree.getroot().findall(
+                ".//{http://graphml.graphdrawing.org/xmlns}edge"
+            )
+        ]
+        # verify edge id value is equal to specified attribute value
+        assert sorted(edge_ids) == sorted(edge_attributes.values())
+
+        # check graphml generated from generate_graphml()
+        data = "".join(generator)
+        J = nx.parse_graphml(data)
+        assert sorted(G.nodes()) == sorted(J.nodes())
+        assert sorted(G.edges()) == sorted(J.edges())
+        # NetworkX adds explicit edge "id" from file as attribute
+        nx.set_edge_attributes(G, edge_attributes, "id")
+        assert edges_equal(G.edges(data=True), J.edges(data=True))
+
+    def test_multigraph_write_generate_edge_id_from_attribute(self, tmp_path):
+        from xml.etree.ElementTree import parse
+
+        G = nx.MultiGraph()
+        G.add_edges_from([("a", "b"), ("b", "c"), ("a", "c"), ("a", "b")])
+        edge_attributes = {e: str(e) for e in G.edges}
+        nx.set_edge_attributes(G, edge_attributes, "eid")
+        fname = tmp_path / "test.graphml"
+        # set edge_id_from_attribute e.g. "eid" for write_graphml()
+        self.writer(G, fname, edge_id_from_attribute="eid")
+        # set edge_id_from_attribute e.g. "eid" for generate_graphml()
+        generator = nx.generate_graphml(G, edge_id_from_attribute="eid")
+
+        H = nx.read_graphml(fname)
+        assert H.is_multigraph()
+        H = nx.read_graphml(fname, force_multigraph=True)
+        assert H.is_multigraph()
+
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        assert sorted(data.get("eid") for u, v, data in H.edges(data=True)) == sorted(
+            edge_attributes.values()
+        )
+        # NetworkX uses edge_ids as keys in multigraphs if no key
+        assert sorted(key for u, v, key in H.edges(keys=True)) == sorted(
+            edge_attributes.values()
+        )
+
+        tree = parse(fname)
+        children = list(tree.getroot())
+        assert len(children) == 2
+        edge_ids = [
+            edge.attrib["id"]
+            for edge in tree.getroot().findall(
+                ".//{http://graphml.graphdrawing.org/xmlns}edge"
+            )
+        ]
+        # verify edge id value is equal to specified attribute value
+        assert sorted(edge_ids) == sorted(edge_attributes.values())
+
+        # check graphml generated from generate_graphml()
+        graphml_data = "".join(generator)
+        J = nx.parse_graphml(graphml_data)
+        assert J.is_multigraph()
+
+        assert nodes_equal(G.nodes(), J.nodes())
+        assert edges_equal(G.edges(), J.edges())
+        assert sorted(data.get("eid") for u, v, data in J.edges(data=True)) == sorted(
+            edge_attributes.values()
+        )
+        # NetworkX uses edge_ids as keys in multigraphs if no key
+        assert sorted(key for u, v, key in J.edges(keys=True)) == sorted(
+            edge_attributes.values()
+        )
+
+    def test_numpy_float64(self, tmp_path):
+        np = pytest.importorskip("numpy")
+        wt = np.float64(3.4)
+        G = nx.Graph([(1, 2, {"weight": wt})])
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname, node_type=int)
+        assert G.edges == H.edges
+        wtG = G[1][2]["weight"]
+        wtH = H[1][2]["weight"]
+        assert wtG == pytest.approx(wtH, abs=1e-6)
+        assert type(wtG) == np.float64
+        assert type(wtH) == float
+
+    def test_numpy_float32(self, tmp_path):
+        np = pytest.importorskip("numpy")
+        wt = np.float32(3.4)
+        G = nx.Graph([(1, 2, {"weight": wt})])
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname, node_type=int)
+        assert G.edges == H.edges
+        wtG = G[1][2]["weight"]
+        wtH = H[1][2]["weight"]
+        assert wtG == pytest.approx(wtH, abs=1e-6)
+        assert type(wtG) == np.float32
+        assert type(wtH) == float
+
+    def test_numpy_float64_inference(self, tmp_path):
+        np = pytest.importorskip("numpy")
+        G = self.attribute_numeric_type_graph
+        G.edges[("n1", "n1")]["weight"] = np.float64(1.1)
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname, infer_numeric_types=True)
+        H = nx.read_graphml(fname)
+        assert G._adj == H._adj
+
+    def test_unicode_attributes(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        node_type = str
+        G.add_edge(name1, "Radiohead", foo=name2)
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname, node_type=node_type)
+        assert G._adj == H._adj
+
+    def test_unicode_escape(self):
+        # test for handling json escaped strings in python 2 Issue #1880
+        import json
+
+        a = {"a": '{"a": "123"}'}  # an object with many chars to escape
+        sa = json.dumps(a)
+        G = nx.Graph()
+        G.graph["test"] = sa
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert G.graph["test"] == H.graph["test"]
+
+
+class TestXMLGraphML(TestWriteGraphML):
+    writer = staticmethod(nx.write_graphml_xml)
+
+    @classmethod
+    def setup_class(cls):
+        TestWriteGraphML.setup_class()
+
+
+def test_exception_for_unsupported_datatype_node_attr():
+    """Test that a detailed exception is raised when an attribute is of a type
+    not supported by GraphML, e.g. a list"""
+    pytest.importorskip("lxml.etree")
+    # node attribute
+    G = nx.Graph()
+    G.add_node(0, my_list_attribute=[0, 1, 2])
+    fh = io.BytesIO()
+    with pytest.raises(TypeError, match="GraphML does not support"):
+        nx.write_graphml(G, fh)
+
+
+def test_exception_for_unsupported_datatype_edge_attr():
+    """Test that a detailed exception is raised when an attribute is of a type
+    not supported by GraphML, e.g. a list"""
+    pytest.importorskip("lxml.etree")
+    # edge attribute
+    G = nx.Graph()
+    G.add_edge(0, 1, my_list_attribute=[0, 1, 2])
+    fh = io.BytesIO()
+    with pytest.raises(TypeError, match="GraphML does not support"):
+        nx.write_graphml(G, fh)
+
+
+def test_exception_for_unsupported_datatype_graph_attr():
+    """Test that a detailed exception is raised when an attribute is of a type
+    not supported by GraphML, e.g. a list"""
+    pytest.importorskip("lxml.etree")
+    # graph attribute
+    G = nx.Graph()
+    G.graph["my_list_attribute"] = [0, 1, 2]
+    fh = io.BytesIO()
+    with pytest.raises(TypeError, match="GraphML does not support"):
+        nx.write_graphml(G, fh)
+
+
+def test_empty_attribute():
+    """Tests that a GraphML string with an empty attribute can be parsed
+    correctly."""
+    s = """<?xml version='1.0' encoding='utf-8'?>
+    <graphml>
+      <key id="d1" for="node" attr.name="foo" attr.type="string"/>
+      <key id="d2" for="node" attr.name="bar" attr.type="string"/>
+      <graph>
+        <node id="0">
+          <data key="d1">aaa</data>
+          <data key="d2">bbb</data>
+        </node>
+        <node id="1">
+          <data key="d1">ccc</data>
+          <data key="d2"></data>
+        </node>
+      </graph>
+    </graphml>"""
+    fh = io.BytesIO(s.encode("UTF-8"))
+    G = nx.read_graphml(fh)
+    assert G.nodes["0"] == {"foo": "aaa", "bar": "bbb"}
+    assert G.nodes["1"] == {"foo": "ccc", "bar": ""}

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_leda.py

@@ -0,0 +1,30 @@
+import io
+
+import networkx as nx
+
+
+class TestLEDA:
+    def test_parse_leda(self):
+        data = """#header section         \nLEDA.GRAPH \nstring\nint\n-1\n#nodes section\n5 \n|{v1}| \n|{v2}| \n|{v3}| \n|{v4}| \n|{v5}| \n\n#edges section\n7 \n1 2 0 |{4}| \n1 3 0 |{3}| \n2 3 0 |{2}| \n3 4 0 |{3}| \n3 5 0 |{7}| \n4 5 0 |{6}| \n5 1 0 |{foo}|"""
+        G = nx.parse_leda(data)
+        G = nx.parse_leda(data.split("\n"))
+        assert sorted(G.nodes()) == ["v1", "v2", "v3", "v4", "v5"]
+        assert sorted(G.edges(data=True)) == [
+            ("v1", "v2", {"label": "4"}),
+            ("v1", "v3", {"label": "3"}),
+            ("v2", "v3", {"label": "2"}),
+            ("v3", "v4", {"label": "3"}),
+            ("v3", "v5", {"label": "7"}),
+            ("v4", "v5", {"label": "6"}),
+            ("v5", "v1", {"label": "foo"}),
+        ]
+
+    def test_read_LEDA(self):
+        fh = io.BytesIO()
+        data = """#header section         \nLEDA.GRAPH \nstring\nint\n-1\n#nodes section\n5 \n|{v1}| \n|{v2}| \n|{v3}| \n|{v4}| \n|{v5}| \n\n#edges section\n7 \n1 2 0 |{4}| \n1 3 0 |{3}| \n2 3 0 |{2}| \n3 4 0 |{3}| \n3 5 0 |{7}| \n4 5 0 |{6}| \n5 1 0 |{foo}|"""
+        G = nx.parse_leda(data)
+        fh.write(data.encode("UTF-8"))
+        fh.seek(0)
+        Gin = nx.read_leda(fh)
+        assert sorted(G.nodes()) == sorted(Gin.nodes())
+        assert sorted(G.edges()) == sorted(Gin.edges())

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_p2g.py

@@ -0,0 +1,62 @@
+import io
+
+import networkx as nx
+from networkx.readwrite.p2g import read_p2g, write_p2g
+from networkx.utils import edges_equal
+
+
+class TestP2G:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+
+    def test_read_p2g(self):
+        s = b"""\
+name
+3 4
+a
+1 2
+b
+
+c
+0 2
+"""
+        bytesIO = io.BytesIO(s)
+        G = read_p2g(bytesIO)
+        assert G.name == "name"
+        assert sorted(G) == ["a", "b", "c"]
+        edges = [(str(u), str(v)) for u, v in G.edges()]
+        assert edges_equal(G.edges(), [("a", "c"), ("a", "b"), ("c", "a"), ("c", "c")])
+
+    def test_write_p2g(self):
+        s = b"""foo
+3 2
+1
+1 
+2
+2 
+3
+
+"""
+        fh = io.BytesIO()
+        G = nx.DiGraph()
+        G.name = "foo"
+        G.add_edges_from([(1, 2), (2, 3)])
+        write_p2g(G, fh)
+        fh.seek(0)
+        r = fh.read()
+        assert r == s
+
+    def test_write_read_p2g(self):
+        fh = io.BytesIO()
+        G = nx.DiGraph()
+        G.name = "foo"
+        G.add_edges_from([("a", "b"), ("b", "c")])
+        write_p2g(G, fh)
+        fh.seek(0)
+        H = read_p2g(fh)
+        assert edges_equal(G.edges(), H.edges())

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_pajek.py

@@ -0,0 +1,126 @@
+"""
+Pajek tests
+"""
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestPajek:
+    @classmethod
+    def setup_class(cls):
+        cls.data = """*network Tralala\n*vertices 4\n   1 "A1"         0.0938 0.0896   ellipse x_fact 1 y_fact 1\n   2 "Bb"         0.8188 0.2458   ellipse x_fact 1 y_fact 1\n   3 "C"          0.3688 0.7792   ellipse x_fact 1\n   4 "D2"         0.9583 0.8563   ellipse x_fact 1\n*arcs\n1 1 1  h2 0 w 3 c Blue s 3 a1 -130 k1 0.6 a2 -130 k2 0.6 ap 0.5 l "Bezier loop" lc BlueViolet fos 20 lr 58 lp 0.3 la 360\n2 1 1  h2 0 a1 120 k1 1.3 a2 -120 k2 0.3 ap 25 l "Bezier arc" lphi 270 la 180 lr 19 lp 0.5\n1 2 1  h2 0 a1 40 k1 2.8 a2 30 k2 0.8 ap 25 l "Bezier arc" lphi 90 la 0 lp 0.65\n4 2 -1  h2 0 w 1 k1 -2 k2 250 ap 25 l "Circular arc" c Red lc OrangeRed\n3 4 1  p Dashed h2 0 w 2 c OliveGreen ap 25 l "Straight arc" lc PineGreen\n1 3 1  p Dashed h2 0 w 5 k1 -1 k2 -20 ap 25 l "Oval arc" c Brown lc Black\n3 3 -1  h1 6 w 1 h2 12 k1 -2 k2 -15 ap 0.5 l "Circular loop" c Red lc OrangeRed lphi 270 la 180"""
+        cls.G = nx.MultiDiGraph()
+        cls.G.add_nodes_from(["A1", "Bb", "C", "D2"])
+        cls.G.add_edges_from(
+            [
+                ("A1", "A1"),
+                ("A1", "Bb"),
+                ("A1", "C"),
+                ("Bb", "A1"),
+                ("C", "C"),
+                ("C", "D2"),
+                ("D2", "Bb"),
+            ]
+        )
+
+        cls.G.graph["name"] = "Tralala"
+
+    def test_parse_pajek_simple(self):
+        # Example without node positions or shape
+        data = """*Vertices 2\n1 "1"\n2 "2"\n*Edges\n1 2\n2 1"""
+        G = nx.parse_pajek(data)
+        assert sorted(G.nodes()) == ["1", "2"]
+        assert edges_equal(G.edges(), [("1", "2"), ("1", "2")])
+
+    def test_parse_pajek(self):
+        G = nx.parse_pajek(self.data)
+        assert sorted(G.nodes()) == ["A1", "Bb", "C", "D2"]
+        assert edges_equal(
+            G.edges(),
+            [
+                ("A1", "A1"),
+                ("A1", "Bb"),
+                ("A1", "C"),
+                ("Bb", "A1"),
+                ("C", "C"),
+                ("C", "D2"),
+                ("D2", "Bb"),
+            ],
+        )
+
+    def test_parse_pajet_mat(self):
+        data = """*Vertices 3\n1 "one"\n2 "two"\n3 "three"\n*Matrix\n1 1 0\n0 1 0\n0 1 0\n"""
+        G = nx.parse_pajek(data)
+        assert set(G.nodes()) == {"one", "two", "three"}
+        assert G.nodes["two"] == {"id": "2"}
+        assert edges_equal(
+            set(G.edges()),
+            {("one", "one"), ("two", "one"), ("two", "two"), ("two", "three")},
+        )
+
+    def test_read_pajek(self, tmp_path):
+        G = nx.parse_pajek(self.data)
+        # Read data from file
+        fname = tmp_path / "test.pjk"
+        with open(fname, "wb") as fh:
+            fh.write(self.data.encode("UTF-8"))
+
+        Gin = nx.read_pajek(fname)
+        assert sorted(G.nodes()) == sorted(Gin.nodes())
+        assert edges_equal(G.edges(), Gin.edges())
+        assert self.G.graph == Gin.graph
+        for n in G:
+            assert G.nodes[n] == Gin.nodes[n]
+
+    def test_write_pajek(self):
+        import io
+
+        G = nx.parse_pajek(self.data)
+        fh = io.BytesIO()
+        nx.write_pajek(G, fh)
+        fh.seek(0)
+        H = nx.read_pajek(fh)
+        assert nodes_equal(list(G), list(H))
+        assert edges_equal(list(G.edges()), list(H.edges()))
+        # Graph name is left out for now, therefore it is not tested.
+        # assert_equal(G.graph, H.graph)
+
+    def test_ignored_attribute(self):
+        import io
+
+        G = nx.Graph()
+        fh = io.BytesIO()
+        G.add_node(1, int_attr=1)
+        G.add_node(2, empty_attr="  ")
+        G.add_edge(1, 2, int_attr=2)
+        G.add_edge(2, 3, empty_attr="  ")
+
+        import warnings
+
+        with warnings.catch_warnings(record=True) as w:
+            nx.write_pajek(G, fh)
+            assert len(w) == 4
+
+    def test_noname(self):
+        # Make sure we can parse a line such as:  *network
+        # Issue #952
+        line = "*network\n"
+        other_lines = self.data.split("\n")[1:]
+        data = line + "\n".join(other_lines)
+        G = nx.parse_pajek(data)
+
+    def test_unicode(self):
+        import io
+
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", foo=name2)
+        fh = io.BytesIO()
+        nx.write_pajek(G, fh)
+        fh.seek(0)
+        H = nx.read_pajek(fh)
+        assert nodes_equal(list(G), list(H))
+        assert edges_equal(list(G.edges()), list(H.edges()))
+        assert G.graph == H.graph

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_sparse6.py

@@ -0,0 +1,166 @@
+from io import BytesIO
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestSparseGraph6:
+    def test_from_sparse6_bytes(self):
+        data = b":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM"
+        G = nx.from_sparse6_bytes(data)
+        assert nodes_equal(
+            sorted(G.nodes()),
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
+        )
+        assert edges_equal(
+            G.edges(),
+            [
+                (0, 1),
+                (0, 2),
+                (0, 3),
+                (1, 12),
+                (1, 14),
+                (2, 13),
+                (2, 15),
+                (3, 16),
+                (3, 17),
+                (4, 7),
+                (4, 9),
+                (4, 11),
+                (5, 6),
+                (5, 8),
+                (5, 9),
+                (6, 10),
+                (6, 11),
+                (7, 8),
+                (7, 10),
+                (8, 12),
+                (9, 15),
+                (10, 14),
+                (11, 13),
+                (12, 16),
+                (13, 17),
+                (14, 17),
+                (15, 16),
+            ],
+        )
+
+    def test_from_bytes_multigraph_graph(self):
+        graph_data = b":An"
+        G = nx.from_sparse6_bytes(graph_data)
+        assert type(G) == nx.Graph
+        multigraph_data = b":Ab"
+        M = nx.from_sparse6_bytes(multigraph_data)
+        assert type(M) == nx.MultiGraph
+
+    def test_read_sparse6(self):
+        data = b":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM"
+        G = nx.from_sparse6_bytes(data)
+        fh = BytesIO(data)
+        Gin = nx.read_sparse6(fh)
+        assert nodes_equal(G.nodes(), Gin.nodes())
+        assert edges_equal(G.edges(), Gin.edges())
+
+    def test_read_many_graph6(self):
+        # Read many graphs into list
+        data = b":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM\n" b":Q___dCfDEdcEgcbEGbFIaJ`JaHN`IM"
+        fh = BytesIO(data)
+        glist = nx.read_sparse6(fh)
+        assert len(glist) == 2
+        for G in glist:
+            assert nodes_equal(
+                G.nodes(),
+                [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
+            )
+
+
+class TestWriteSparse6:
+    """Unit tests for writing graphs in the sparse6 format.
+
+    Most of the test cases were checked against the sparse6 encoder in Sage.
+
+    """
+
+    def test_null_graph(self):
+        G = nx.null_graph()
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:?\n"
+
+    def test_trivial_graph(self):
+        G = nx.trivial_graph()
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:@\n"
+
+    def test_empty_graph(self):
+        G = nx.empty_graph(5)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:D\n"
+
+    def test_large_empty_graph(self):
+        G = nx.empty_graph(68)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:~?@C\n"
+
+    def test_very_large_empty_graph(self):
+        G = nx.empty_graph(258049)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:~~???~?@\n"
+
+    def test_complete_graph(self):
+        G = nx.complete_graph(4)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:CcKI\n"
+
+    def test_no_header(self):
+        G = nx.complete_graph(4)
+        result = BytesIO()
+        nx.write_sparse6(G, result, header=False)
+        assert result.getvalue() == b":CcKI\n"
+
+    def test_padding(self):
+        codes = (b":Cdv", b":DaYn", b":EaYnN", b":FaYnL", b":GaYnLz")
+        for n, code in enumerate(codes, start=4):
+            G = nx.path_graph(n)
+            result = BytesIO()
+            nx.write_sparse6(G, result, header=False)
+            assert result.getvalue() == code + b"\n"
+
+    def test_complete_bipartite(self):
+        G = nx.complete_bipartite_graph(6, 9)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        # Compared with sage
+        expected = b">>sparse6<<:Nk" + b"?G`cJ" * 9 + b"\n"
+        assert result.getvalue() == expected
+
+    def test_read_write_inverse(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            m = min(2 * i, i * i // 2)
+            g = nx.random_graphs.gnm_random_graph(i, m, seed=i)
+            gstr = BytesIO()
+            nx.write_sparse6(g, gstr, header=False)
+            # Strip the trailing newline.
+            gstr = gstr.getvalue().rstrip()
+            g2 = nx.from_sparse6_bytes(gstr)
+            assert g2.order() == g.order()
+            assert edges_equal(g2.edges(), g.edges())
+
+    def test_no_directed_graphs(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.write_sparse6(nx.DiGraph(), BytesIO())
+
+    def test_write_path(self, tmp_path):
+        # Get a valid temporary file name
+        fullfilename = str(tmp_path / "test.s6")
+        # file should be closed now, so write_sparse6 can open it
+        nx.write_sparse6(nx.null_graph(), fullfilename)
+        with open(fullfilename, mode="rb") as fh:
+            assert fh.read() == b">>sparse6<<:?\n"

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/tests/test_text.py

@@ -0,0 +1,1742 @@
+import random
+from itertools import product
+from textwrap import dedent
+
+import pytest
+
+import networkx as nx
+
+
+def test_generate_network_text_forest_directed():
+    # Create a directed forest with labels
+    graph = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    for node in graph.nodes:
+        graph.nodes[node]["label"] = "node_" + chr(ord("a") + node)
+
+    node_target = dedent(
+        """
+        ╙── 0
+            ├─╼ 1
+            │   ├─╼ 3
+            │   └─╼ 4
+            └─╼ 2
+                ├─╼ 5
+                └─╼ 6
+        """
+    ).strip()
+
+    label_target = dedent(
+        """
+        ╙── node_a
+            ├─╼ node_b
+            │   ├─╼ node_d
+            │   └─╼ node_e
+            └─╼ node_c
+                ├─╼ node_f
+                └─╼ node_g
+        """
+    ).strip()
+
+    # Basic node case
+    ret = nx.generate_network_text(graph, with_labels=False)
+    assert "\n".join(ret) == node_target
+
+    # Basic label case
+    ret = nx.generate_network_text(graph, with_labels=True)
+    assert "\n".join(ret) == label_target
+
+
+def test_write_network_text_empty_graph():
+    def _graph_str(g, **kw):
+        printbuf = []
+        nx.write_network_text(g, printbuf.append, end="", **kw)
+        return "\n".join(printbuf)
+
+    assert _graph_str(nx.DiGraph()) == "╙"
+    assert _graph_str(nx.Graph()) == "╙"
+    assert _graph_str(nx.DiGraph(), ascii_only=True) == "+"
+    assert _graph_str(nx.Graph(), ascii_only=True) == "+"
+
+
+def test_write_network_text_within_forest_glyph():
+    g = nx.DiGraph()
+    g.add_nodes_from([1, 2, 3, 4])
+    g.add_edge(2, 4)
+    lines = []
+    write = lines.append
+    nx.write_network_text(g, path=write, end="")
+    nx.write_network_text(g, path=write, ascii_only=True, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╟── 1
+        ╟── 2
+        ╎   └─╼ 4
+        ╙── 3
+        +-- 1
+        +-- 2
+        :   L-> 4
+        +-- 3
+        """
+    ).strip()
+    assert text == target
+
+
+def test_generate_network_text_directed_multi_tree():
+    tree1 = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    tree2 = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    forest = nx.disjoint_union_all([tree1, tree2])
+    ret = "\n".join(nx.generate_network_text(forest))
+
+    target = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   │   ├─╼ 3
+        ╎   │   └─╼ 4
+        ╎   └─╼ 2
+        ╎       ├─╼ 5
+        ╎       └─╼ 6
+        ╙── 7
+            ├─╼ 8
+            │   ├─╼ 10
+            │   └─╼ 11
+            └─╼ 9
+                ├─╼ 12
+                └─╼ 13
+        """
+    ).strip()
+    assert ret == target
+
+    tree3 = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    forest = nx.disjoint_union_all([tree1, tree2, tree3])
+    ret = "\n".join(nx.generate_network_text(forest, sources=[0, 14, 7]))
+
+    target = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   │   ├─╼ 3
+        ╎   │   └─╼ 4
+        ╎   └─╼ 2
+        ╎       ├─╼ 5
+        ╎       └─╼ 6
+        ╟── 14
+        ╎   ├─╼ 15
+        ╎   │   ├─╼ 17
+        ╎   │   └─╼ 18
+        ╎   └─╼ 16
+        ╎       ├─╼ 19
+        ╎       └─╼ 20
+        ╙── 7
+            ├─╼ 8
+            │   ├─╼ 10
+            │   └─╼ 11
+            └─╼ 9
+                ├─╼ 12
+                └─╼ 13
+        """
+    ).strip()
+    assert ret == target
+
+    ret = "\n".join(
+        nx.generate_network_text(forest, sources=[0, 14, 7], ascii_only=True)
+    )
+
+    target = dedent(
+        """
+        +-- 0
+        :   |-> 1
+        :   |   |-> 3
+        :   |   L-> 4
+        :   L-> 2
+        :       |-> 5
+        :       L-> 6
+        +-- 14
+        :   |-> 15
+        :   |   |-> 17
+        :   |   L-> 18
+        :   L-> 16
+        :       |-> 19
+        :       L-> 20
+        +-- 7
+            |-> 8
+            |   |-> 10
+            |   L-> 11
+            L-> 9
+                |-> 12
+                L-> 13
+        """
+    ).strip()
+    assert ret == target
+
+
+def test_generate_network_text_undirected_multi_tree():
+    tree1 = nx.balanced_tree(r=2, h=2, create_using=nx.Graph)
+    tree2 = nx.balanced_tree(r=2, h=2, create_using=nx.Graph)
+    tree2 = nx.relabel_nodes(tree2, {n: n + len(tree1) for n in tree2.nodes})
+    forest = nx.union(tree1, tree2)
+    ret = "\n".join(nx.generate_network_text(forest, sources=[0, 7]))
+
+    target = dedent(
+        """
+        ╟── 0
+        ╎   ├── 1
+        ╎   │   ├── 3
+        ╎   │   └── 4
+        ╎   └── 2
+        ╎       ├── 5
+        ╎       └── 6
+        ╙── 7
+            ├── 8
+            │   ├── 10
+            │   └── 11
+            └── 9
+                ├── 12
+                └── 13
+        """
+    ).strip()
+    assert ret == target
+
+    ret = "\n".join(nx.generate_network_text(forest, sources=[0, 7], ascii_only=True))
+
+    target = dedent(
+        """
+        +-- 0
+        :   |-- 1
+        :   |   |-- 3
+        :   |   L-- 4
+        :   L-- 2
+        :       |-- 5
+        :       L-- 6
+        +-- 7
+            |-- 8
+            |   |-- 10
+            |   L-- 11
+            L-- 9
+                |-- 12
+                L-- 13
+        """
+    ).strip()
+    assert ret == target
+
+
+def test_generate_network_text_forest_undirected():
+    # Create a directed forest
+    graph = nx.balanced_tree(r=2, h=2, create_using=nx.Graph)
+
+    node_target0 = dedent(
+        """
+        ╙── 0
+            ├── 1
+            │   ├── 3
+            │   └── 4
+            └── 2
+                ├── 5
+                └── 6
+        """
+    ).strip()
+
+    # defined starting point
+    ret = "\n".join(nx.generate_network_text(graph, sources=[0]))
+    assert ret == node_target0
+
+    # defined starting point
+    node_target2 = dedent(
+        """
+        ╙── 2
+            ├── 0
+            │   └── 1
+            │       ├── 3
+            │       └── 4
+            ├── 5
+            └── 6
+        """
+    ).strip()
+    ret = "\n".join(nx.generate_network_text(graph, sources=[2]))
+    assert ret == node_target2
+
+
+def test_generate_network_text_overspecified_sources():
+    """
+    When sources are directly specified, we won't be able to determine when we
+    are in the last component, so there will always be a trailing, leftmost
+    pipe.
+    """
+    graph = nx.disjoint_union_all(
+        [
+            nx.balanced_tree(r=2, h=1, create_using=nx.DiGraph),
+            nx.balanced_tree(r=1, h=2, create_using=nx.DiGraph),
+            nx.balanced_tree(r=2, h=1, create_using=nx.DiGraph),
+        ]
+    )
+
+    # defined starting point
+    target1 = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   └─╼ 2
+        ╟── 3
+        ╎   └─╼ 4
+        ╎       └─╼ 5
+        ╟── 6
+        ╎   ├─╼ 7
+        ╎   └─╼ 8
+        """
+    ).strip()
+
+    target2 = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   └─╼ 2
+        ╟── 3
+        ╎   └─╼ 4
+        ╎       └─╼ 5
+        ╙── 6
+            ├─╼ 7
+            └─╼ 8
+        """
+    ).strip()
+
+    got1 = "\n".join(nx.generate_network_text(graph, sources=graph.nodes))
+    got2 = "\n".join(nx.generate_network_text(graph))
+    assert got1 == target1
+    assert got2 == target2
+
+
+def test_write_network_text_iterative_add_directed_edges():
+    """
+    Walk through the cases going from a disconnected to fully connected graph
+    """
+    graph = nx.DiGraph()
+    graph.add_nodes_from([1, 2, 3, 4])
+    lines = []
+    write = lines.append
+    write("--- initial state ---")
+    nx.write_network_text(graph, path=write, end="")
+    for i, j in product(graph.nodes, graph.nodes):
+        write(f"--- add_edge({i}, {j}) ---")
+        graph.add_edge(i, j)
+        nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    # defined starting point
+    target = dedent(
+        """
+        --- initial state ---
+        ╟── 1
+        ╟── 2
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 1) ---
+        ╟── 1 ╾ 1
+        ╎   └─╼  ...
+        ╟── 2
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 2) ---
+        ╟── 1 ╾ 1
+        ╎   ├─╼ 2
+        ╎   └─╼  ...
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 3) ---
+        ╟── 1 ╾ 1
+        ╎   ├─╼ 2
+        ╎   ├─╼ 3
+        ╎   └─╼  ...
+        ╙── 4
+        --- add_edge(1, 4) ---
+        ╙── 1 ╾ 1
+            ├─╼ 2
+            ├─╼ 3
+            ├─╼ 4
+            └─╼  ...
+        --- add_edge(2, 1) ---
+        ╙── 2 ╾ 1
+            └─╼ 1 ╾ 1
+                ├─╼ 3
+                ├─╼ 4
+                └─╼  ...
+        --- add_edge(2, 2) ---
+        ╙── 1 ╾ 1, 2
+            ├─╼ 2 ╾ 2
+            │   └─╼  ...
+            ├─╼ 3
+            ├─╼ 4
+            └─╼  ...
+        --- add_edge(2, 3) ---
+        ╙── 1 ╾ 1, 2
+            ├─╼ 2 ╾ 2
+            │   ├─╼ 3 ╾ 1
+            │   └─╼  ...
+            ├─╼ 4
+            └─╼  ...
+        --- add_edge(2, 4) ---
+        ╙── 1 ╾ 1, 2
+            ├─╼ 2 ╾ 2
+            │   ├─╼ 3 ╾ 1
+            │   ├─╼ 4 ╾ 1
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 1) ---
+        ╙── 2 ╾ 1, 2
+            ├─╼ 1 ╾ 1, 3
+            │   ├─╼ 3 ╾ 2
+            │   │   └─╼  ...
+            │   ├─╼ 4 ╾ 2
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 2) ---
+        ╙── 3 ╾ 1, 2
+            ├─╼ 1 ╾ 1, 2
+            │   ├─╼ 2 ╾ 2, 3
+            │   │   ├─╼ 4 ╾ 1
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 3) ---
+        ╙── 1 ╾ 1, 2, 3
+            ├─╼ 2 ╾ 2, 3
+            │   ├─╼ 3 ╾ 1, 3
+            │   │   └─╼  ...
+            │   ├─╼ 4 ╾ 1
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 4) ---
+        ╙── 1 ╾ 1, 2, 3
+            ├─╼ 2 ╾ 2, 3
+            │   ├─╼ 3 ╾ 1, 3
+            │   │   ├─╼ 4 ╾ 1, 2
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 1) ---
+        ╙── 2 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 1, 3, 4
+            │   ├─╼ 3 ╾ 2, 3
+            │   │   ├─╼ 4 ╾ 1, 2
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 2) ---
+        ╙── 3 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 1, 2, 4
+            │   ├─╼ 2 ╾ 2, 3, 4
+            │   │   ├─╼ 4 ╾ 1, 3
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 3) ---
+        ╙── 4 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 1, 2, 3
+            │   ├─╼ 2 ╾ 2, 3, 4
+            │   │   ├─╼ 3 ╾ 1, 3, 4
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 4) ---
+        ╙── 1 ╾ 1, 2, 3, 4
+            ├─╼ 2 ╾ 2, 3, 4
+            │   ├─╼ 3 ╾ 1, 3, 4
+            │   │   ├─╼ 4 ╾ 1, 2, 4
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_iterative_add_undirected_edges():
+    """
+    Walk through the cases going from a disconnected to fully connected graph
+    """
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4])
+    lines = []
+    write = lines.append
+    write("--- initial state ---")
+    nx.write_network_text(graph, path=write, end="")
+    for i, j in product(graph.nodes, graph.nodes):
+        if i == j:
+            continue
+        write(f"--- add_edge({i}, {j}) ---")
+        graph.add_edge(i, j)
+        nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- initial state ---
+        ╟── 1
+        ╟── 2
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 2) ---
+        ╟── 3
+        ╟── 4
+        ╙── 1
+            └── 2
+        --- add_edge(1, 3) ---
+        ╟── 4
+        ╙── 2
+            └── 1
+                └── 3
+        --- add_edge(1, 4) ---
+        ╙── 2
+            └── 1
+                ├── 3
+                └── 4
+        --- add_edge(2, 1) ---
+        ╙── 2
+            └── 1
+                ├── 3
+                └── 4
+        --- add_edge(2, 3) ---
+        ╙── 4
+            └── 1
+                ├── 2
+                │   └── 3 ─ 1
+                └──  ...
+        --- add_edge(2, 4) ---
+        ╙── 3
+            ├── 1
+            │   ├── 2 ─ 3
+            │   │   └── 4 ─ 1
+            │   └──  ...
+            └──  ...
+        --- add_edge(3, 1) ---
+        ╙── 3
+            ├── 1
+            │   ├── 2 ─ 3
+            │   │   └── 4 ─ 1
+            │   └──  ...
+            └──  ...
+        --- add_edge(3, 2) ---
+        ╙── 3
+            ├── 1
+            │   ├── 2 ─ 3
+            │   │   └── 4 ─ 1
+            │   └──  ...
+            └──  ...
+        --- add_edge(3, 4) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        --- add_edge(4, 1) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        --- add_edge(4, 2) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        --- add_edge(4, 3) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_iterative_add_random_directed_edges():
+    """
+    Walk through the cases going from a disconnected to fully connected graph
+    """
+
+    rng = random.Random(724466096)
+    graph = nx.DiGraph()
+    graph.add_nodes_from([1, 2, 3, 4, 5])
+    possible_edges = list(product(graph.nodes, graph.nodes))
+    rng.shuffle(possible_edges)
+    graph.add_edges_from(possible_edges[0:8])
+    lines = []
+    write = lines.append
+    write("--- initial state ---")
+    nx.write_network_text(graph, path=write, end="")
+    for i, j in possible_edges[8:12]:
+        write(f"--- add_edge({i}, {j}) ---")
+        graph.add_edge(i, j)
+        nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- initial state ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(4, 1) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1, 4
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(2, 1) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1, 4, 2
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(5, 2) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2, 5
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1, 4, 2
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(1, 5) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2, 5
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5 ╾ 1
+                │   │   ├─╼ 1 ╾ 1, 4, 2
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_nearly_forest():
+    g = nx.DiGraph()
+    g.add_edge(1, 2)
+    g.add_edge(1, 5)
+    g.add_edge(2, 3)
+    g.add_edge(3, 4)
+    g.add_edge(5, 6)
+    g.add_edge(6, 7)
+    g.add_edge(6, 8)
+    orig = g.copy()
+    g.add_edge(1, 8)  # forward edge
+    g.add_edge(4, 2)  # back edge
+    g.add_edge(6, 3)  # cross edge
+    lines = []
+    write = lines.append
+    write("--- directed case ---")
+    nx.write_network_text(orig, path=write, end="")
+    write("--- add (1, 8), (4, 2), (6, 3) ---")
+    nx.write_network_text(g, path=write, end="")
+    write("--- undirected case ---")
+    nx.write_network_text(orig.to_undirected(), path=write, sources=[1], end="")
+    write("--- add (1, 8), (4, 2), (6, 3) ---")
+    nx.write_network_text(g.to_undirected(), path=write, sources=[1], end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case ---
+        ╙── 1
+            ├─╼ 2
+            │   └─╼ 3
+            │       └─╼ 4
+            └─╼ 5
+                └─╼ 6
+                    ├─╼ 7
+                    └─╼ 8
+        --- add (1, 8), (4, 2), (6, 3) ---
+        ╙── 1
+            ├─╼ 2 ╾ 4
+            │   └─╼ 3 ╾ 6
+            │       └─╼ 4
+            │           └─╼  ...
+            ├─╼ 5
+            │   └─╼ 6
+            │       ├─╼ 7
+            │       ├─╼ 8 ╾ 1
+            │       └─╼  ...
+            └─╼  ...
+        --- undirected case ---
+        ╙── 1
+            ├── 2
+            │   └── 3
+            │       └── 4
+            └── 5
+                └── 6
+                    ├���─ 7
+                    └── 8
+        --- add (1, 8), (4, 2), (6, 3) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3
+            │   │   ├── 4 ─ 2
+            │   │   └── 6
+            │   │       ├── 5 ─ 1
+            │   │       ├── 7
+            │   │       └── 8 ─ 1
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_complete_graph_ascii_only():
+    graph = nx.generators.complete_graph(5, create_using=nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- directed case ---")
+    nx.write_network_text(graph, path=write, ascii_only=True, end="")
+    write("--- undirected case ---")
+    nx.write_network_text(graph.to_undirected(), path=write, ascii_only=True, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case ---
+        +-- 0 <- 1, 2, 3, 4
+            |-> 1 <- 2, 3, 4
+            |   |-> 2 <- 0, 3, 4
+            |   |   |-> 3 <- 0, 1, 4
+            |   |   |   |-> 4 <- 0, 1, 2
+            |   |   |   |   L->  ...
+            |   |   |   L->  ...
+            |   |   L->  ...
+            |   L->  ...
+            L->  ...
+        --- undirected case ---
+        +-- 0
+            |-- 1
+            |   |-- 2 - 0
+            |   |   |-- 3 - 0, 1
+            |   |   |   L-- 4 - 0, 1, 2
+            |   |   L--  ...
+            |   L--  ...
+            L--  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_with_labels():
+    graph = nx.generators.complete_graph(5, create_using=nx.DiGraph)
+    for n in graph.nodes:
+        graph.nodes[n]["label"] = f"Node(n={n})"
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, with_labels=True, ascii_only=False, end="")
+    text = "\n".join(lines)
+    # Non trees with labels can get somewhat out of hand with network text
+    # because we need to immediately show every non-tree edge to the right
+    target = dedent(
+        """
+        ╙── Node(n=0) ╾ Node(n=1), Node(n=2), Node(n=3), Node(n=4)
+            ├─╼ Node(n=1) ╾ Node(n=2), Node(n=3), Node(n=4)
+            │   ├─╼ Node(n=2) ╾ Node(n=0), Node(n=3), Node(n=4)
+            │   │   ├─╼ Node(n=3) ╾ Node(n=0), Node(n=1), Node(n=4)
+            │   │   │   ├─╼ Node(n=4) ╾ Node(n=0), Node(n=1), Node(n=2)
+            │   │   │   │   └─╼  ...
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_complete_graphs():
+    lines = []
+    write = lines.append
+    for k in [0, 1, 2, 3, 4, 5]:
+        g = nx.generators.complete_graph(k)
+        write(f"--- undirected k={k} ---")
+        nx.write_network_text(g, path=write, end="")
+
+    for k in [0, 1, 2, 3, 4, 5]:
+        g = nx.generators.complete_graph(k, nx.DiGraph)
+        write(f"--- directed k={k} ---")
+        nx.write_network_text(g, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- undirected k=0 ---
+        ╙
+        --- undirected k=1 ---
+        ╙── 0
+        --- undirected k=2 ---
+        ╙── 0
+            └── 1
+        --- undirected k=3 ---
+        ╙── 0
+            ├── 1
+            │   └── 2 ─ 0
+            └──  ...
+        --- undirected k=4 ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0
+            │   │   └── 3 ─ 0, 1
+            │   └──  ...
+            └──  ...
+        --- undirected k=5 ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0
+            │   │   ├── 3 ─ 0, 1
+            │   │   │   └── 4 ─ 0, 1, 2
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        --- directed k=0 ---
+        ╙
+        --- directed k=1 ---
+        ╙── 0
+        --- directed k=2 ---
+        ╙── 0 ╾ 1
+            └─╼ 1
+                └─╼  ...
+        --- directed k=3 ---
+        ╙── 0 ╾ 1, 2
+            ├─╼ 1 ╾ 2
+            │   ├─╼ 2 ╾ 0
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- directed k=4 ---
+        ╙── 0 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 2, 3
+            │   ├─╼ 2 ╾ 0, 3
+            │   │   ├─╼ 3 ╾ 0, 1
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- directed k=5 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   ├─╼ 2 ╾ 0, 3, 4
+            ��   │   ├─╼ 3 ╾ 0, 1, 4
+            │   │   │   ├─╼ 4 ╾ 0, 1, 2
+            │   │   │   │   └─╼  ...
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_multiple_sources():
+    g = nx.DiGraph()
+    g.add_edge(1, 2)
+    g.add_edge(1, 3)
+    g.add_edge(2, 4)
+    g.add_edge(3, 5)
+    g.add_edge(3, 6)
+    g.add_edge(5, 4)
+    g.add_edge(4, 1)
+    g.add_edge(1, 5)
+    lines = []
+    write = lines.append
+    # Use each node as the starting point to demonstrate how the representation
+    # changes.
+    nodes = sorted(g.nodes())
+    for n in nodes:
+        write(f"--- source node: {n} ---")
+        nx.write_network_text(g, path=write, sources=[n], end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- source node: 1 ---
+        ╙── 1 ╾ 4
+            ├─╼ 2
+            │   └─╼ 4 ╾ 5
+            │       └─╼  ...
+            ├─╼ 3
+            │   ├─╼ 5 ╾ 1
+            │   │   └─╼  ...
+            │   └─╼ 6
+            └─╼  ...
+        --- source node: 2 ---
+        ╙── 2 ╾ 1
+            └─╼ 4 ╾ 5
+                └─╼ 1
+                    ├─╼ 3
+                    │   ├─╼ 5 ╾ 1
+                    │   │   └─╼  ...
+                    │   └─╼ 6
+                    └─╼  ...
+        --- source node: 3 ---
+        ╙── 3 ╾ 1
+            ├─╼ 5 ╾ 1
+            │   └─╼ 4 ╾ 2
+            │       └─╼ 1
+            │           ├─╼ 2
+            │           │   └─╼  ...
+            │           └─╼  ...
+            └─╼ 6
+        --- source node: 4 ---
+        ╙── 4 ╾ 2, 5
+            └─╼ 1
+                ├─╼ 2
+                │   └─╼  ...
+                ├─╼ 3
+                │   ├─╼ 5 ╾ 1
+                │   │   └─╼  ...
+                │   └─╼ 6
+                └─╼  ...
+        --- source node: 5 ---
+        ╙── 5 ╾ 3, 1
+            └─╼ 4 ╾ 2
+                └─╼ 1
+                    ├─╼ 2
+                    │   └─╼  ...
+                    ├─╼ 3
+                    │   ├─╼ 6
+                    │   └─╼  ...
+                    └─╼  ...
+        --- source node: 6 ---
+        ╙── 6 ╾ 3
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_star_graph():
+    graph = nx.star_graph(5, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 1
+            └── 0
+                ├── 2
+                ├── 3
+                ├── 4
+                └── 5
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_path_graph():
+    graph = nx.path_graph(3, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 0
+            └── 1
+                └── 2
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_lollipop_graph():
+    graph = nx.lollipop_graph(4, 2, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 5
+            └── 4
+                └── 3
+                    ├── 0
+                    │   ├── 1 ─ 3
+                    │   │   └── 2 ─ 0, 3
+                    │   └──  ...
+                    └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_wheel_graph():
+    graph = nx.wheel_graph(7, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 1
+            ├── 0
+            │   ├── 2 ─ 1
+            │   │   └── 3 ─ 0
+            │   │       └── 4 ─ 0
+            │   │           └── 5 ─ 0
+            │   │               └── 6 ─ 0, 1
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_circular_ladder_graph():
+    graph = nx.circular_ladder_graph(4, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 0
+            ├── 1
+            │   ├── 2
+            │   │   ├── 3 ─ 0
+            │   │   │   └── 7
+            │   │   │       ├── 6 ─ 2
+            │   │   │       │   └── 5 ─ 1
+            │   │   │       │       └── 4 ─ 0, 7
+            │   │   │       └──  ...
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_dorogovtsev_goltsev_mendes_graph():
+    graph = nx.dorogovtsev_goltsev_mendes_graph(4, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 15
+            ├── 0
+            │   ├── 1 ─ 15
+            │   │   ├── 2 ─ 0
+            │   │   │   ├── 4 ─ 0
+            │   │   │   │   ├── 9 ─ 0
+            │   │   │   │   │   ├── 22 ─ 0
+            │   │   │   │   │   └── 38 ─ 4
+            │   │   │   │   ├── 13 ─ 2
+            │   │   │   │   │   ├── 34 ─ 2
+            │   │   │   │   │   └── 39 ─ 4
+            │   │   │   │   ├── 18 ─ 0
+            │   │   │   │   ├── 30 ─ 2
+            │   │   │   │   └──  ...
+            │   │   │   ├── 5 ─ 1
+            │   │   │   │   ├── 12 ─ 1
+            │   │   │   │   │   ├── 29 ─ 1
+            │   │   │   │   │   └── 40 ─ 5
+            │   │   │   │   ├── 14 ─ 2
+            │   │   │   │   │   ├── 35 ─ 2
+            │   │   │   │   │   └── 41 ─ 5
+            │   │   │   │   ├── 25 ─ 1
+            │   │   │   │   ├── 31 ─ 2
+            │   │   │   │   └──  ...
+            │   │   │   ├── 7 ─ 0
+            │   │   │   │   ├── 20 ─ 0
+            │   │   │   │   └── 32 ─ 2
+            │   │   │   ├── 10 ─ 1
+            │   │   │   │   ├── 27 ─ 1
+            │   │   │   │   └── 33 ─ 2
+            │   │   │   ├── 16 ─ 0
+            │   │   │   ├── 23 ─ 1
+            │   │   │   └──  ...
+            │   │   ├── 3 ─ 0
+            │   │   │   ├── 8 ─ 0
+            │   │   │   │   ├── 21 ─ 0
+            │   │   │   │   └── 36 ─ 3
+            │   │   │   ├── 11 ─ 1
+            │   │   │   │   ├── 28 ─ 1
+            │   │   │   │   └── 37 ─ 3
+            │   │   │   ├── 17 ─ 0
+            │   │   │   ├── 24 ─ 1
+            │   │   │   └──  ...
+            │   │   ├── 6 ─ 0
+            │   │   │   ├── 19 ─ 0
+            │   │   │   └── 26 ─ 1
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_tree_max_depth():
+    orig = nx.balanced_tree(r=1, h=3, create_using=nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- directed case, max_depth=0 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=0)
+    write("--- directed case, max_depth=1 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=1)
+    write("--- directed case, max_depth=2 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=2)
+    write("--- directed case, max_depth=3 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=3)
+    write("--- directed case, max_depth=4 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=4)
+    write("--- undirected case, max_depth=0 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=0)
+    write("--- undirected case, max_depth=1 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=1)
+    write("--- undirected case, max_depth=2 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=2)
+    write("--- undirected case, max_depth=3 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=3)
+    write("--- undirected case, max_depth=4 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=4)
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case, max_depth=0 ---
+        ╙ ...
+        --- directed case, max_depth=1 ---
+        ╙── 0
+            └─╼  ...
+        --- directed case, max_depth=2 ---
+        ╙── 0
+            └─��� 1
+                └─╼  ...
+        --- directed case, max_depth=3 ---
+        ╙── 0
+            └─╼ 1
+                └─╼ 2
+                    └─╼  ...
+        --- directed case, max_depth=4 ---
+        ╙── 0
+            └─╼ 1
+                └─╼ 2
+                    └─╼ 3
+        --- undirected case, max_depth=0 ---
+        ╙ ...
+        --- undirected case, max_depth=1 ---
+        ╙── 0 ─ 1
+            └──  ...
+        --- undirected case, max_depth=2 ---
+        ╙── 0
+            └── 1 ─ 2
+                └──  ...
+        --- undirected case, max_depth=3 ---
+        ╙── 0
+            └── 1
+                └── 2 ─ 3
+                    └──  ...
+        --- undirected case, max_depth=4 ---
+        ╙── 0
+            └── 1
+                └── 2
+                    └── 3
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_graph_max_depth():
+    orig = nx.erdos_renyi_graph(10, 0.15, directed=True, seed=40392)
+    lines = []
+    write = lines.append
+    write("--- directed case, max_depth=None ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=None)
+    write("--- directed case, max_depth=0 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=0)
+    write("--- directed case, max_depth=1 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=1)
+    write("--- directed case, max_depth=2 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=2)
+    write("--- directed case, max_depth=3 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=3)
+    write("--- undirected case, max_depth=None ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=None)
+    write("--- undirected case, max_depth=0 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=0)
+    write("--- undirected case, max_depth=1 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=1)
+    write("--- undirected case, max_depth=2 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=2)
+    write("--- undirected case, max_depth=3 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=3)
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case, max_depth=None ---
+        ╟── 4
+        ╎   ├─╼ 0 ╾ 3
+        ╎   ├─╼ 5 ╾ 7
+        ╎   │   └─╼ 3
+        ╎   │       ├─╼ 1 ╾ 9
+        ╎   │       │   └─╼ 9 ╾ 6
+        ╎   │       │       ├─╼ 6
+        ╎   │       │       │   └─╼  ...
+        ╎   │       │       ├─╼ 7 ╾ 4
+        ╎   │       │       │   ├─╼ 2
+        ╎   │       │       │   └─╼  ...
+        ╎   │       │       └─╼  ...
+        ╎   │       └─╼  ...
+        ╎   └─╼  ...
+        ╙── 8
+        --- directed case, max_depth=0 ---
+        ╙ ...
+        --- directed case, max_depth=1 ---
+        ╟── 4
+        ╎   └─╼  ...
+        ╙── 8
+        --- directed case, max_depth=2 ---
+        ╟── 4
+        ╎   ├─╼ 0 ╾ 3
+        ╎   ├─╼ 5 ╾ 7
+        ╎   │   └─╼  ...
+        ╎   └─╼ 7 ╾ 9
+        ╎       └─╼  ...
+        ╙── 8
+        --- directed case, max_depth=3 ---
+        ╟── 4
+        ╎   ├─╼ 0 ╾ 3
+        ╎   ├─╼ 5 ╾ 7
+        ╎   │   └─╼ 3
+        ╎   │       └─╼  ...
+        ╎   └─╼ 7 ╾ 9
+        ╎       ├─╼ 2
+        ╎       └─╼  ...
+        ╙── 8
+        --- undirected case, max_depth=None ---
+        ╟── 8
+        ╙── 2
+            └── 7
+                ├── 4
+                │   ├── 0
+                │   │   └── 3
+                │   │       ├── 1
+                │   │       │   └── 9 ─ 7
+                │   │       │       └── 6
+                │   │       └── 5 ─ 4, 7
+                │   └──  ...
+                └──  ...
+        --- undirected case, max_depth=0 ---
+        ╙ ...
+        --- undirected case, max_depth=1 ---
+        ╟── 8
+        ╙── 2 ─ 7
+            └──  ...
+        --- undirected case, max_depth=2 ---
+        ╟── 8
+        ╙── 2
+            └── 7 ─ 4, 5, 9
+                └──  ...
+        --- undirected case, max_depth=3 ---
+        ╟── 8
+        ╙── 2
+            └── 7
+                ├── 4 ─ 0, 5
+                │   └──  ...
+                ├── 5 ─ 4, 3
+                │   └──  ...
+                └── 9 ─ 1, 6
+                    └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_clique_max_depth():
+    orig = nx.complete_graph(5, nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- directed case, max_depth=None ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=None)
+    write("--- directed case, max_depth=0 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=0)
+    write("--- directed case, max_depth=1 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=1)
+    write("--- directed case, max_depth=2 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=2)
+    write("--- directed case, max_depth=3 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=3)
+    write("--- undirected case, max_depth=None ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=None)
+    write("--- undirected case, max_depth=0 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=0)
+    write("--- undirected case, max_depth=1 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=1)
+    write("--- undirected case, max_depth=2 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=2)
+    write("--- undirected case, max_depth=3 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=3)
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case, max_depth=None ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   ├─╼ 2 ╾ 0, 3, 4
+            │   │   ├─╼ 3 ╾ 0, 1, 4
+            │   │   │   ├─╼ 4 ╾ 0, 1, 2
+            │   │   │   │   └─╼  ...
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- directed case, max_depth=0 ---
+        ╙ ...
+        --- directed case, max_depth=1 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            └─╼  ...
+        --- directed case, max_depth=2 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   └─╼  ...
+            ├─╼ 2 ╾ 1, 3, 4
+            │   └─╼  ...
+            ├─╼ 3 ╾ 1, 2, 4
+            │   └─╼  ...
+            └─╼ 4 ╾ 1, 2, 3
+                └─╼  ...
+        --- directed case, max_depth=3 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   ├─╼ 2 ╾ 0, 3, 4
+            │   │   └─╼  ...
+            │   ├─╼ 3 ╾ 0, 2, 4
+            │   │   └─╼  ...
+            │   ├─╼ 4 ╾ 0, 2, 3
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- undirected case, max_depth=None ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0
+            │   │   ├── 3 ─ 0, 1
+            │   │   │   └── 4 ─ 0, 1, 2
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        --- undirected case, max_depth=0 ---
+        ╙ ...
+        --- undirected case, max_depth=1 ---
+        ╙── 0 ─ 1, 2, 3, 4
+            └──  ...
+        --- undirected case, max_depth=2 ---
+        ╙── 0
+            ├── 1 ─ 2, 3, 4
+            │   └──  ...
+            ├── 2 ─ 1, 3, 4
+            │   └──  ...
+            ├── 3 ─ 1, 2, 4
+            │   └──  ...
+            └── 4 ─ 1, 2, 3
+        --- undirected case, max_depth=3 ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0, 3, 4
+            │   │   └──  ...
+            │   ├── 3 ─ 0, 2, 4
+            │   │   └──  ...
+            │   └── 4 ─ 0, 2, 3
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_custom_label():
+    # Create a directed forest with labels
+    graph = nx.erdos_renyi_graph(5, 0.4, directed=True, seed=359222358)
+    for node in graph.nodes:
+        graph.nodes[node]["label"] = f"Node({node})"
+        graph.nodes[node]["chr"] = chr(node + ord("a") - 1)
+        if node % 2 == 0:
+            graph.nodes[node]["part"] = chr(node + ord("a"))
+
+    lines = []
+    write = lines.append
+    write("--- when with_labels=True, uses the 'label' attr ---")
+    nx.write_network_text(graph, path=write, with_labels=True, end="", max_depth=None)
+    write("--- when with_labels=False, uses str(node) value ---")
+    nx.write_network_text(graph, path=write, with_labels=False, end="", max_depth=None)
+    write("--- when with_labels is a string, use that attr ---")
+    nx.write_network_text(graph, path=write, with_labels="chr", end="", max_depth=None)
+    write("--- fallback to str(node) when the attr does not exist ---")
+    nx.write_network_text(graph, path=write, with_labels="part", end="", max_depth=None)
+
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- when with_labels=True, uses the 'label' attr ---
+        ╙── Node(1)
+            └─╼ Node(3) ╾ Node(2)
+                ├─╼ Node(0)
+                │   ├─╼ Node(2) ╾ Node(3), Node(4)
+                │   │   └─╼  ...
+                │   └─╼ Node(4)
+                │       └─╼  ...
+                └─╼  ...
+        --- when with_labels=False, uses str(node) value ---
+        ╙── 1
+            └─╼ 3 ╾ 2
+                ├─╼ 0
+                │   ├─╼ 2 ╾ 3, 4
+                │   │   └─╼  ...
+                │   └─╼ 4
+                │       └─╼  ...
+                └─╼  ...
+        --- when with_labels is a string, use that attr ---
+        ╙── a
+            └─╼ c ╾ b
+                ├─╼ `
+                │   ├─╼ b ╾ c, d
+                │   │   └─╼  ...
+                │   └─╼ d
+                │       └─╼  ...
+                └─╼  ...
+        --- fallback to str(node) when the attr does not exist ---
+        ╙── 1
+            └─╼ 3 ╾ c
+                ├─╼ a
+                │   ├─╼ c ╾ 3, e
+                │   │   └─╼  ...
+                │   └─╼ e
+                │       └─╼  ...
+                └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_vertical_chains():
+    graph1 = nx.lollipop_graph(4, 2, create_using=nx.Graph)
+    graph1.add_edge(0, -1)
+    graph1.add_edge(-1, -2)
+    graph1.add_edge(-2, -3)
+
+    graph2 = graph1.to_directed()
+    graph2.remove_edges_from([(u, v) for u, v in graph2.edges if v > u])
+
+    lines = []
+    write = lines.append
+    write("--- Undirected UTF ---")
+    nx.write_network_text(graph1, path=write, end="", vertical_chains=True)
+    write("--- Undirected ASCI ---")
+    nx.write_network_text(
+        graph1, path=write, end="", vertical_chains=True, ascii_only=True
+    )
+    write("--- Directed UTF ---")
+    nx.write_network_text(graph2, path=write, end="", vertical_chains=True)
+    write("--- Directed ASCI ---")
+    nx.write_network_text(
+        graph2, path=write, end="", vertical_chains=True, ascii_only=True
+    )
+
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- Undirected UTF ---
+        ╙── 5
+            │
+            4
+            │
+            3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   │
+            │   │   2 ─ 0, 3
+            │   ├── -1
+            │   │   │
+            │   │   -2
+            │   │   │
+            │   │   -3
+            │   └──  ...
+            └──  ...
+        --- Undirected ASCI ---
+        +-- 5
+            |
+            4
+            |
+            3
+            |-- 0
+            |   |-- 1 - 3
+            |   |   |
+            |   |   2 - 0, 3
+            |   |-- -1
+            |   |   |
+            |   |   -2
+            |   |   |
+            |   |   -3
+            |   L--  ...
+            L--  ...
+        --- Directed UTF ---
+        ╙── 5
+            ╽
+            4
+            ╽
+            3
+            ├─╼ 0 ╾ 1, 2
+            │   ╽
+            │   -1
+            │   ╽
+            │   -2
+            │   ╽
+            │   -3
+            ├─╼ 1 ╾ 2
+            │   └─╼  ...
+            └─╼ 2
+                └─╼  ...
+        --- Directed ASCI ---
+        +-- 5
+            !
+            4
+            !
+            3
+            |-> 0 <- 1, 2
+            |   !
+            |   -1
+            |   !
+            |   -2
+            |   !
+            |   -3
+            |-> 1 <- 2
+            |   L->  ...
+            L-> 2
+                L->  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_collapse_directed():
+    graph = nx.balanced_tree(r=2, h=3, create_using=nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- Original ---")
+    nx.write_network_text(graph, path=write, end="")
+    graph.nodes[1]["collapse"] = True
+    write("--- Collapse Node 1 ---")
+    nx.write_network_text(graph, path=write, end="")
+    write("--- Add alternate path (5, 3) to collapsed zone")
+    graph.add_edge(5, 3)
+    nx.write_network_text(graph, path=write, end="")
+    write("--- Collapse Node 0 ---")
+    graph.nodes[0]["collapse"] = True
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- Original ---
+        ╙── 0
+            ├─╼ 1
+            │   ├─╼ 3
+            │   │   ├─╼ 7
+            │   │   └─╼ 8
+            │   └─╼ 4
+            │       ├─╼ 9
+            │       └─╼ 10
+            └─╼ 2
+                ├─╼ 5
+                │   ├─╼ 11
+                │   └─╼ 12
+                └─╼ 6
+                    ├─╼ 13
+                    └─╼ 14
+        --- Collapse Node 1 ---
+        ╙── 0
+            ├─╼ 1
+            │   └─╼  ...
+            └─╼ 2
+                ├─╼ 5
+                │   ├─╼ 11
+                │   └─╼ 12
+                └─╼ 6
+                    ├─╼ 13
+                    └─╼ 14
+        --- Add alternate path (5, 3) to collapsed zone
+        ╙── 0
+            ├─╼ 1
+            │   └─╼  ...
+            └─╼ 2
+                ├─╼ 5
+                │   ├─╼ 11
+                │   ├─╼ 12
+                │   └─╼ 3 ╾ 1
+                │       ├─╼ 7
+                │       └─╼ 8
+                └─╼ 6
+                    ├─╼ 13
+                    └─╼ 14
+        --- Collapse Node 0 ---
+        ╙── 0
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_collapse_undirected():
+    graph = nx.balanced_tree(r=2, h=3, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    write("--- Original ---")
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    graph.nodes[1]["collapse"] = True
+    write("--- Collapse Node 1 ---")
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    write("--- Add alternate path (5, 3) to collapsed zone")
+    graph.add_edge(5, 3)
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    write("--- Collapse Node 0 ---")
+    graph.nodes[0]["collapse"] = True
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- Original ---
+        ╙── 0
+            ├── 1
+            │   ├── 3
+            │   │   ├── 7
+            │   │   └── 8
+            │   └── 4
+            │       ├── 9
+            │       └── 10
+            └── 2
+                ├── 5
+                │   ├── 11
+                │   └── 12
+                └── 6
+                    ├── 13
+                    └── 14
+        --- Collapse Node 1 ---
+        ╙── 0
+            ├── 1 ─ 3, 4
+            │   └──  ...
+            └── 2
+                ├── 5
+                │   ├── 11
+                │   └── 12
+                └── 6
+                    ├── 13
+                    └── 14
+        --- Add alternate path (5, 3) to collapsed zone
+        ╙── 0
+            ├── 1 ─ 3, 4
+            │   └──  ...
+            └── 2
+                ├── 5
+                │   ├── 11
+                │   ├── 12
+                │   └── 3 ─ 1
+                │       ├── 7
+                │       └── 8
+                └── 6
+                    ├── 13
+                    └── 14
+        --- Collapse Node 0 ---
+        ╙── 0 ─ 1, 2
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def generate_test_graphs():
+    """
+    Generate a gauntlet of different test graphs with different properties
+    """
+    import random
+
+    rng = random.Random(976689776)
+    num_randomized = 3
+
+    for directed in [0, 1]:
+        cls = nx.DiGraph if directed else nx.Graph
+
+        for num_nodes in range(17):
+            # Disconnected graph
+            graph = cls()
+            graph.add_nodes_from(range(num_nodes))
+            yield graph
+
+            # Randomize graphs
+            if num_nodes > 0:
+                for p in [0.1, 0.3, 0.5, 0.7, 0.9]:
+                    for seed in range(num_randomized):
+                        graph = nx.erdos_renyi_graph(
+                            num_nodes, p, directed=directed, seed=rng
+                        )
+                        yield graph
+
+                yield nx.complete_graph(num_nodes, cls)
+
+        yield nx.path_graph(3, create_using=cls)
+        yield nx.balanced_tree(r=1, h=3, create_using=cls)
+        if not directed:
+            yield nx.circular_ladder_graph(4, create_using=cls)
+            yield nx.star_graph(5, create_using=cls)
+            yield nx.lollipop_graph(4, 2, create_using=cls)
+            yield nx.wheel_graph(7, create_using=cls)
+            yield nx.dorogovtsev_goltsev_mendes_graph(4, create_using=cls)
+
+
+@pytest.mark.parametrize(
+    ("vertical_chains", "ascii_only"),
+    tuple(
+        [
+            (vertical_chains, ascii_only)
+            for vertical_chains in [0, 1]
+            for ascii_only in [0, 1]
+        ]
+    ),
+)
+def test_network_text_round_trip(vertical_chains, ascii_only):
+    """
+    Write the graph to network text format, then parse it back in, assert it is
+    the same as the original graph. Passing this test is strong validation of
+    both the format generator and parser.
+    """
+    from networkx.readwrite.text import _parse_network_text
+
+    for graph in generate_test_graphs():
+        graph = nx.relabel_nodes(graph, {n: str(n) for n in graph.nodes})
+        lines = list(
+            nx.generate_network_text(
+                graph, vertical_chains=vertical_chains, ascii_only=ascii_only
+            )
+        )
+        new = _parse_network_text(lines)
+        try:
+            assert new.nodes == graph.nodes
+            assert new.edges == graph.edges
+        except Exception:
+            nx.write_network_text(graph)
+            raise

mplug_owl2/lib/python3.10/site-packages/networkx/readwrite/text.py

@@ -0,0 +1,852 @@
+"""
+Text-based visual representations of graphs
+"""
+
+import sys
+import warnings
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["generate_network_text", "write_network_text"]
+
+
+class BaseGlyphs:
+    @classmethod
+    def as_dict(cls):
+        return {
+            a: getattr(cls, a)
+            for a in dir(cls)
+            if not a.startswith("_") and a != "as_dict"
+        }
+
+
+class AsciiBaseGlyphs(BaseGlyphs):
+    empty: str = "+"
+    newtree_last: str = "+-- "
+    newtree_mid: str = "+-- "
+    endof_forest: str = "    "
+    within_forest: str = ":   "
+    within_tree: str = "|   "
+
+
+class AsciiDirectedGlyphs(AsciiBaseGlyphs):
+    last: str = "L-> "
+    mid: str = "|-> "
+    backedge: str = "<-"
+    vertical_edge: str = "!"
+
+
+class AsciiUndirectedGlyphs(AsciiBaseGlyphs):
+    last: str = "L-- "
+    mid: str = "|-- "
+    backedge: str = "-"
+    vertical_edge: str = "|"
+
+
+class UtfBaseGlyphs(BaseGlyphs):
+    # Notes on available box and arrow characters
+    # https://en.wikipedia.org/wiki/Box-drawing_character
+    # https://stackoverflow.com/questions/2701192/triangle-arrow
+    empty: str = "╙"
+    newtree_last: str = "╙── "
+    newtree_mid: str = "╟── "
+    endof_forest: str = "    "
+    within_forest: str = "╎   "
+    within_tree: str = "│   "
+
+
+class UtfDirectedGlyphs(UtfBaseGlyphs):
+    last: str = "└─╼ "
+    mid: str = "├─╼ "
+    backedge: str = "╾"
+    vertical_edge: str = "╽"
+
+
+class UtfUndirectedGlyphs(UtfBaseGlyphs):
+    last: str = "└── "
+    mid: str = "├── "
+    backedge: str = "─"
+    vertical_edge: str = "│"
+
+
+def generate_network_text(
+    graph,
+    with_labels=True,
+    sources=None,
+    max_depth=None,
+    ascii_only=False,
+    vertical_chains=False,
+):
+    """Generate lines in the "network text" format
+
+    This works via a depth-first traversal of the graph and writing a line for
+    each unique node encountered. Non-tree edges are written to the right of
+    each node, and connection to a non-tree edge is indicated with an ellipsis.
+    This representation works best when the input graph is a forest, but any
+    graph can be represented.
+
+    This notation is original to networkx, although it is simple enough that it
+    may be known in existing literature. See #5602 for details. The procedure
+    is summarized as follows:
+
+    1. Given a set of source nodes (which can be specified, or automatically
+    discovered via finding the (strongly) connected components and choosing one
+    node with minimum degree from each), we traverse the graph in depth first
+    order.
+
+    2. Each reachable node will be printed exactly once on it's own line.
+
+    3. Edges are indicated in one of four ways:
+
+        a. a parent "L-style" connection on the upper left. This corresponds to
+        a traversal in the directed DFS tree.
+
+        b. a backref "<-style" connection shown directly on the right. For
+        directed graphs, these are drawn for any incoming edges to a node that
+        is not a parent edge. For undirected graphs, these are drawn for only
+        the non-parent edges that have already been represented (The edges that
+        have not been represented will be handled in the recursive case).
+
+        c. a child "L-style" connection on the lower right. Drawing of the
+        children are handled recursively.
+
+        d. if ``vertical_chains`` is true, and a parent node only has one child
+        a "vertical-style" edge is drawn between them.
+
+    4. The children of each node (wrt the directed DFS tree) are drawn
+    underneath and to the right of it. In the case that a child node has already
+    been drawn the connection is replaced with an ellipsis ("...") to indicate
+    that there is one or more connections represented elsewhere.
+
+    5. If a maximum depth is specified, an edge to nodes past this maximum
+    depth will be represented by an ellipsis.
+
+    6. If a node has a truthy "collapse" value, then we do not traverse past
+    that node.
+
+    Parameters
+    ----------
+    graph : nx.DiGraph | nx.Graph
+        Graph to represent
+
+    with_labels : bool | str
+        If True will use the "label" attribute of a node to display if it
+        exists otherwise it will use the node value itself. If given as a
+        string, then that attribute name will be used instead of "label".
+        Defaults to True.
+
+    sources : List
+        Specifies which nodes to start traversal from. Note: nodes that are not
+        reachable from one of these sources may not be shown. If unspecified,
+        the minimal set of nodes needed to reach all others will be used.
+
+    max_depth : int | None
+        The maximum depth to traverse before stopping. Defaults to None.
+
+    ascii_only : Boolean
+        If True only ASCII characters are used to construct the visualization
+
+    vertical_chains : Boolean
+        If True, chains of nodes will be drawn vertically when possible.
+
+    Yields
+    ------
+    str : a line of generated text
+
+    Examples
+    --------
+    >>> graph = nx.path_graph(10)
+    >>> graph.add_node("A")
+    >>> graph.add_node("B")
+    >>> graph.add_node("C")
+    >>> graph.add_node("D")
+    >>> graph.add_edge(9, "A")
+    >>> graph.add_edge(9, "B")
+    >>> graph.add_edge(9, "C")
+    >>> graph.add_edge("C", "D")
+    >>> graph.add_edge("C", "E")
+    >>> graph.add_edge("C", "F")
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        └── 1
+            └── 2
+                └── 3
+                    └── 4
+                        └── 5
+                            └── 6
+                                └── 7
+                                    └── 8
+                                        └── 9
+                                            ├── A
+                                            ├── B
+                                            └── C
+                                                ├── D
+                                                ├── E
+                                                └── F
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 0
+        │
+        1
+        │
+        2
+        │
+        3
+        │
+        4
+        │
+        5
+        │
+        6
+        │
+        7
+        │
+        8
+        │
+        9
+        ├── A
+        ├── B
+        └── C
+            ├── D
+            ├── E
+            └── F
+    """
+    from typing import Any, NamedTuple
+
+    class StackFrame(NamedTuple):
+        parent: Any
+        node: Any
+        indents: list
+        this_islast: bool
+        this_vertical: bool
+
+    collapse_attr = "collapse"
+
+    is_directed = graph.is_directed()
+
+    if is_directed:
+        glyphs = AsciiDirectedGlyphs if ascii_only else UtfDirectedGlyphs
+        succ = graph.succ
+        pred = graph.pred
+    else:
+        glyphs = AsciiUndirectedGlyphs if ascii_only else UtfUndirectedGlyphs
+        succ = graph.adj
+        pred = graph.adj
+
+    if isinstance(with_labels, str):
+        label_attr = with_labels
+    elif with_labels:
+        label_attr = "label"
+    else:
+        label_attr = None
+
+    if max_depth == 0:
+        yield glyphs.empty + " ..."
+    elif len(graph.nodes) == 0:
+        yield glyphs.empty
+    else:
+        # If the nodes to traverse are unspecified, find the minimal set of
+        # nodes that will reach the entire graph
+        if sources is None:
+            sources = _find_sources(graph)
+
+        # Populate the stack with each:
+        # 1. parent node in the DFS tree (or None for root nodes),
+        # 2. the current node in the DFS tree
+        # 2. a list of indentations indicating depth
+        # 3. a flag indicating if the node is the final one to be written.
+        # Reverse the stack so sources are popped in the correct order.
+        last_idx = len(sources) - 1
+        stack = [
+            StackFrame(None, node, [], (idx == last_idx), False)
+            for idx, node in enumerate(sources)
+        ][::-1]
+
+        num_skipped_children = defaultdict(lambda: 0)
+        seen_nodes = set()
+        while stack:
+            parent, node, indents, this_islast, this_vertical = stack.pop()
+
+            if node is not Ellipsis:
+                skip = node in seen_nodes
+                if skip:
+                    # Mark that we skipped a parent's child
+                    num_skipped_children[parent] += 1
+
+                if this_islast:
+                    # If we reached the last child of a parent, and we skipped
+                    # any of that parents children, then we should emit an
+                    # ellipsis at the end after this.
+                    if num_skipped_children[parent] and parent is not None:
+                        # Append the ellipsis to be emitted last
+                        next_islast = True
+                        try_frame = StackFrame(
+                            node, Ellipsis, indents, next_islast, False
+                        )
+                        stack.append(try_frame)
+
+                        # Redo this frame, but not as a last object
+                        next_islast = False
+                        try_frame = StackFrame(
+                            parent, node, indents, next_islast, this_vertical
+                        )
+                        stack.append(try_frame)
+                        continue
+
+                if skip:
+                    continue
+                seen_nodes.add(node)
+
+            if not indents:
+                # Top level items (i.e. trees in the forest) get different
+                # glyphs to indicate they are not actually connected
+                if this_islast:
+                    this_vertical = False
+                    this_prefix = indents + [glyphs.newtree_last]
+                    next_prefix = indents + [glyphs.endof_forest]
+                else:
+                    this_prefix = indents + [glyphs.newtree_mid]
+                    next_prefix = indents + [glyphs.within_forest]
+
+            else:
+                # Non-top-level items
+                if this_vertical:
+                    this_prefix = indents
+                    next_prefix = indents
+                else:
+                    if this_islast:
+                        this_prefix = indents + [glyphs.last]
+                        next_prefix = indents + [glyphs.endof_forest]
+                    else:
+                        this_prefix = indents + [glyphs.mid]
+                        next_prefix = indents + [glyphs.within_tree]
+
+            if node is Ellipsis:
+                label = " ..."
+                suffix = ""
+                children = []
+            else:
+                if label_attr is not None:
+                    label = str(graph.nodes[node].get(label_attr, node))
+                else:
+                    label = str(node)
+
+                # Determine if we want to show the children of this node.
+                if collapse_attr is not None:
+                    collapse = graph.nodes[node].get(collapse_attr, False)
+                else:
+                    collapse = False
+
+                # Determine:
+                # (1) children to traverse into after showing this node.
+                # (2) parents to immediately show to the right of this node.
+                if is_directed:
+                    # In the directed case we must show every successor node
+                    # note: it may be skipped later, but we don't have that
+                    # information here.
+                    children = list(succ[node])
+                    # In the directed case we must show every predecessor
+                    # except for parent we directly traversed from.
+                    handled_parents = {parent}
+                else:
+                    # Showing only the unseen children results in a more
+                    # concise representation for the undirected case.
+                    children = [
+                        child for child in succ[node] if child not in seen_nodes
+                    ]
+
+                    # In the undirected case, parents are also children, so we
+                    # only need to immediately show the ones we can no longer
+                    # traverse
+                    handled_parents = {*children, parent}
+
+                if max_depth is not None and len(indents) == max_depth - 1:
+                    # Use ellipsis to indicate we have reached maximum depth
+                    if children:
+                        children = [Ellipsis]
+                    handled_parents = {parent}
+
+                if collapse:
+                    # Collapsing a node is the same as reaching maximum depth
+                    if children:
+                        children = [Ellipsis]
+                    handled_parents = {parent}
+
+                # The other parents are other predecessors of this node that
+                # are not handled elsewhere.
+                other_parents = [p for p in pred[node] if p not in handled_parents]
+                if other_parents:
+                    if label_attr is not None:
+                        other_parents_labels = ", ".join(
+                            [
+                                str(graph.nodes[p].get(label_attr, p))
+                                for p in other_parents
+                            ]
+                        )
+                    else:
+                        other_parents_labels = ", ".join(
+                            [str(p) for p in other_parents]
+                        )
+                    suffix = " ".join(["", glyphs.backedge, other_parents_labels])
+                else:
+                    suffix = ""
+
+            # Emit the line for this node, this will be called for each node
+            # exactly once.
+            if this_vertical:
+                yield "".join(this_prefix + [glyphs.vertical_edge])
+
+            yield "".join(this_prefix + [label, suffix])
+
+            if vertical_chains:
+                if is_directed:
+                    num_children = len(set(children))
+                else:
+                    num_children = len(set(children) - {parent})
+                # The next node can be drawn vertically if it is the only
+                # remaining child of this node.
+                next_is_vertical = num_children == 1
+            else:
+                next_is_vertical = False
+
+            # Push children on the stack in reverse order so they are popped in
+            # the original order.
+            for idx, child in enumerate(children[::-1]):
+                next_islast = idx == 0
+                try_frame = StackFrame(
+                    node, child, next_prefix, next_islast, next_is_vertical
+                )
+                stack.append(try_frame)
+
+
+@open_file(1, "w")
+def write_network_text(
+    graph,
+    path=None,
+    with_labels=True,
+    sources=None,
+    max_depth=None,
+    ascii_only=False,
+    end="\n",
+    vertical_chains=False,
+):
+    """Creates a nice text representation of a graph
+
+    This works via a depth-first traversal of the graph and writing a line for
+    each unique node encountered. Non-tree edges are written to the right of
+    each node, and connection to a non-tree edge is indicated with an ellipsis.
+    This representation works best when the input graph is a forest, but any
+    graph can be represented.
+
+    Parameters
+    ----------
+    graph : nx.DiGraph | nx.Graph
+        Graph to represent
+
+    path : string or file or callable or None
+       Filename or file handle for data output.
+       if a function, then it will be called for each generated line.
+       if None, this will default to "sys.stdout.write"
+
+    with_labels : bool | str
+        If True will use the "label" attribute of a node to display if it
+        exists otherwise it will use the node value itself. If given as a
+        string, then that attribute name will be used instead of "label".
+        Defaults to True.
+
+    sources : List
+        Specifies which nodes to start traversal from. Note: nodes that are not
+        reachable from one of these sources may not be shown. If unspecified,
+        the minimal set of nodes needed to reach all others will be used.
+
+    max_depth : int | None
+        The maximum depth to traverse before stopping. Defaults to None.
+
+    ascii_only : Boolean
+        If True only ASCII characters are used to construct the visualization
+
+    end : string
+        The line ending character
+
+    vertical_chains : Boolean
+        If True, chains of nodes will be drawn vertically when possible.
+
+    Examples
+    --------
+    >>> graph = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├─╼ 1
+        │   ├─╼ 3
+        │   └─╼ 4
+        └─╼ 2
+            ├─╼ 5
+            └─╼ 6
+
+    >>> # A near tree with one non-tree edge
+    >>> graph.add_edge(5, 1)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├─╼ 1 ╾ 5
+        │   ├─╼ 3
+        │   └─╼ 4
+        └─╼ 2
+            ├─╼ 5
+            │   └─╼  ...
+            └─╼ 6
+
+    >>> graph = nx.cycle_graph(5)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├── 1
+        │   └── 2
+        │       └── 3
+        │           └── 4 ─ 0
+        └──  ...
+
+    >>> graph = nx.cycle_graph(5, nx.DiGraph)
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 0 ╾ 4
+        ╽
+        1
+        ╽
+        2
+        ╽
+        3
+        ╽
+        4
+        └─╼  ...
+
+    >>> nx.write_network_text(graph, vertical_chains=True, ascii_only=True)
+    +-- 0 <- 4
+        !
+        1
+        !
+        2
+        !
+        3
+        !
+        4
+        L->  ...
+
+    >>> graph = nx.generators.barbell_graph(4, 2)
+    >>> nx.write_network_text(graph, vertical_chains=False)
+    ╙── 4
+        ├── 5
+        │   └── 6
+        │       ├── 7
+        │       │   ├── 8 ─ 6
+        │       │   │   └── 9 ─ 6, 7
+        │       │   └──  ...
+        │       └──  ...
+        └── 3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   └── 2 ─ 0, 3
+            │   └──  ...
+            └──  ...
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 4
+        ├── 5
+        │   │
+        │   6
+        │   ├── 7
+        │   │   ├── 8 ─ 6
+        │   │   │   │
+        │   │   │   9 ─ 6, 7
+        │   │   └──  ...
+        │   └──  ...
+        └── 3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   │
+            │   │   2 ─ 0, 3
+            │   └──  ...
+            └──  ...
+
+    >>> graph = nx.complete_graph(5, create_using=nx.Graph)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├── 1
+        │   ├── 2 ─ 0
+        │   │   ├── 3 ─ 0, 1
+        │   │   │   └── 4 ─ 0, 1, 2
+        │   │   └──  ...
+        │   └──  ...
+        └──  ...
+
+    >>> graph = nx.complete_graph(3, create_using=nx.DiGraph)
+    >>> nx.write_network_text(graph)
+    ╙── 0 ╾ 1, 2
+        ├─╼ 1 ╾ 2
+        │   ├─╼ 2 ╾ 0
+        │   │   └─╼  ...
+        │   └─╼  ...
+        └─╼  ...
+    """
+    if path is None:
+        # The path is unspecified, write to stdout
+        _write = sys.stdout.write
+    elif hasattr(path, "write"):
+        # The path is already an open file
+        _write = path.write
+    elif callable(path):
+        # The path is a custom callable
+        _write = path
+    else:
+        raise TypeError(type(path))
+
+    for line in generate_network_text(
+        graph,
+        with_labels=with_labels,
+        sources=sources,
+        max_depth=max_depth,
+        ascii_only=ascii_only,
+        vertical_chains=vertical_chains,
+    ):
+        _write(line + end)
+
+
+def _find_sources(graph):
+    """
+    Determine a minimal set of nodes such that the entire graph is reachable
+    """
+    # For each connected part of the graph, choose at least
+    # one node as a starting point, preferably without a parent
+    if graph.is_directed():
+        # Choose one node from each SCC with minimum in_degree
+        sccs = list(nx.strongly_connected_components(graph))
+        # condensing the SCCs forms a dag, the nodes in this graph with
+        # 0 in-degree correspond to the SCCs from which the minimum set
+        # of nodes from which all other nodes can be reached.
+        scc_graph = nx.condensation(graph, sccs)
+        supernode_to_nodes = {sn: [] for sn in scc_graph.nodes()}
+        # Note: the order of mapping differs between pypy and cpython
+        # so we have to loop over graph nodes for consistency
+        mapping = scc_graph.graph["mapping"]
+        for n in graph.nodes:
+            sn = mapping[n]
+            supernode_to_nodes[sn].append(n)
+        sources = []
+        for sn in scc_graph.nodes():
+            if scc_graph.in_degree[sn] == 0:
+                scc = supernode_to_nodes[sn]
+                node = min(scc, key=lambda n: graph.in_degree[n])
+                sources.append(node)
+    else:
+        # For undirected graph, the entire graph will be reachable as
+        # long as we consider one node from every connected component
+        sources = [
+            min(cc, key=lambda n: graph.degree[n])
+            for cc in nx.connected_components(graph)
+        ]
+        sources = sorted(sources, key=lambda n: graph.degree[n])
+    return sources
+
+
+def _parse_network_text(lines):
+    """Reconstructs a graph from a network text representation.
+
+    This is mainly used for testing.  Network text is for display, not
+    serialization, as such this cannot parse all network text representations
+    because node labels can be ambiguous with the glyphs and indentation used
+    to represent edge structure. Additionally, there is no way to determine if
+    disconnected graphs were originally directed or undirected.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in network text format
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in network text format.
+    """
+    from itertools import chain
+    from typing import Any, NamedTuple, Union
+
+    class ParseStackFrame(NamedTuple):
+        node: Any
+        indent: int
+        has_vertical_child: int | None
+
+    initial_line_iter = iter(lines)
+
+    is_ascii = None
+    is_directed = None
+
+    ##############
+    # Initial Pass
+    ##############
+
+    # Do an initial pass over the lines to determine what type of graph it is.
+    # Remember what these lines were, so we can reiterate over them in the
+    # parsing pass.
+    initial_lines = []
+    try:
+        first_line = next(initial_line_iter)
+    except StopIteration:
+        ...
+    else:
+        initial_lines.append(first_line)
+        # The first character indicates if it is an ASCII or UTF graph
+        first_char = first_line[0]
+        if first_char in {
+            UtfBaseGlyphs.empty,
+            UtfBaseGlyphs.newtree_mid[0],
+            UtfBaseGlyphs.newtree_last[0],
+        }:
+            is_ascii = False
+        elif first_char in {
+            AsciiBaseGlyphs.empty,
+            AsciiBaseGlyphs.newtree_mid[0],
+            AsciiBaseGlyphs.newtree_last[0],
+        }:
+            is_ascii = True
+        else:
+            raise AssertionError(f"Unexpected first character: {first_char}")
+
+    if is_ascii:
+        directed_glyphs = AsciiDirectedGlyphs.as_dict()
+        undirected_glyphs = AsciiUndirectedGlyphs.as_dict()
+    else:
+        directed_glyphs = UtfDirectedGlyphs.as_dict()
+        undirected_glyphs = UtfUndirectedGlyphs.as_dict()
+
+    # For both directed / undirected glyphs, determine which glyphs never
+    # appear as substrings in the other undirected / directed glyphs.  Glyphs
+    # with this property unambiguously indicates if a graph is directed /
+    # undirected.
+    directed_items = set(directed_glyphs.values())
+    undirected_items = set(undirected_glyphs.values())
+    unambiguous_directed_items = []
+    for item in directed_items:
+        other_items = undirected_items
+        other_supersets = [other for other in other_items if item in other]
+        if not other_supersets:
+            unambiguous_directed_items.append(item)
+    unambiguous_undirected_items = []
+    for item in undirected_items:
+        other_items = directed_items
+        other_supersets = [other for other in other_items if item in other]
+        if not other_supersets:
+            unambiguous_undirected_items.append(item)
+
+    for line in initial_line_iter:
+        initial_lines.append(line)
+        if any(item in line for item in unambiguous_undirected_items):
+            is_directed = False
+            break
+        elif any(item in line for item in unambiguous_directed_items):
+            is_directed = True
+            break
+
+    if is_directed is None:
+        # Not enough information to determine, choose undirected by default
+        is_directed = False
+
+    glyphs = directed_glyphs if is_directed else undirected_glyphs
+
+    # the backedge symbol by itself can be ambiguous, but with spaces around it
+    # becomes unambiguous.
+    backedge_symbol = " " + glyphs["backedge"] + " "
+
+    # Reconstruct an iterator over all of the lines.
+    parsing_line_iter = chain(initial_lines, initial_line_iter)
+
+    ##############
+    # Parsing Pass
+    ##############
+
+    edges = []
+    nodes = []
+    is_empty = None
+
+    noparent = object()  # sentinel value
+
+    # keep a stack of previous nodes that could be parents of subsequent nodes
+    stack = [ParseStackFrame(noparent, -1, None)]
+
+    for line in parsing_line_iter:
+        if line == glyphs["empty"]:
+            # If the line is the empty glyph, we are done.
+            # There shouldn't be anything else after this.
+            is_empty = True
+            continue
+
+        if backedge_symbol in line:
+            # This line has one or more backedges, separate those out
+            node_part, backedge_part = line.split(backedge_symbol)
+            backedge_nodes = [u.strip() for u in backedge_part.split(", ")]
+            # Now the node can be parsed
+            node_part = node_part.rstrip()
+            prefix, node = node_part.rsplit(" ", 1)
+            node = node.strip()
+            # Add the backedges to the edge list
+            edges.extend([(u, node) for u in backedge_nodes])
+        else:
+            # No backedge, the tail of this line is the node
+            prefix, node = line.rsplit(" ", 1)
+            node = node.strip()
+
+        prev = stack.pop()
+
+        if node in glyphs["vertical_edge"]:
+            # Previous node is still the previous node, but we know it will
+            # have exactly one child, which will need to have its nesting level
+            # adjusted.
+            modified_prev = ParseStackFrame(
+                prev.node,
+                prev.indent,
+                True,
+            )
+            stack.append(modified_prev)
+            continue
+
+        # The length of the string before the node characters give us a hint
+        # about our nesting level. The only case where this doesn't work is
+        # when there are vertical chains, which is handled explicitly.
+        indent = len(prefix)
+        curr = ParseStackFrame(node, indent, None)
+
+        if prev.has_vertical_child:
+            # In this case we know prev must be the parent of our current line,
+            # so we don't have to search the stack. (which is good because the
+            # indentation check wouldn't work in this case).
+            ...
+        else:
+            # If the previous node nesting-level is greater than the current
+            # nodes nesting-level than the previous node was the end of a path,
+            # and is not our parent. We can safely pop nodes off the stack
+            # until we find one with a comparable nesting-level, which is our
+            # parent.
+            while curr.indent <= prev.indent:
+                prev = stack.pop()
+
+        if node == "...":
+            # The current previous node is no longer a valid parent,
+            # keep it popped from the stack.
+            stack.append(prev)
+        else:
+            # The previous and current nodes may still be parents, so add them
+            # back onto the stack.
+            stack.append(prev)
+            stack.append(curr)
+
+            # Add the node and the edge to its parent to the node / edge lists.
+            nodes.append(curr.node)
+            if prev.node is not noparent:
+                edges.append((prev.node, curr.node))
+
+    if is_empty:
+        # Sanity check
+        assert len(nodes) == 0
+
+    # Reconstruct the graph
+    cls = nx.DiGraph if is_directed else nx.Graph
+    new = cls()
+    new.add_nodes_from(nodes)
+    new.add_edges_from(edges)
+    return new

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h

@@ -0,0 +1,90 @@
+#ifndef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_
+#error You should not include this header directly
+#endif
+/*
+ * Private API (here for inline)
+ */
+static inline int
+_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter);
+
+/*
+ * Update to next item of the iterator
+ *
+ * Note: this simply increment the coordinates vector, last dimension
+ * incremented first , i.e, for dimension 3
+ * ...
+ * -1, -1, -1
+ * -1, -1,  0
+ * -1, -1,  1
+ *  ....
+ * -1,  0, -1
+ * -1,  0,  0
+ *  ....
+ * 0,  -1, -1
+ * 0,  -1,  0
+ *  ....
+ */
+#define _UPDATE_COORD_ITER(c) \
+    wb = iter->coordinates[c] < iter->bounds[c][1]; \
+    if (wb) { \
+        iter->coordinates[c] += 1; \
+        return 0; \
+    } \
+    else { \
+        iter->coordinates[c] = iter->bounds[c][0]; \
+    }
+
+static inline int
+_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter)
+{
+    npy_intp i, wb;
+
+    for (i = iter->nd - 1; i >= 0; --i) {
+        _UPDATE_COORD_ITER(i)
+    }
+
+    return 0;
+}
+
+/*
+ * Version optimized for 2d arrays, manual loop unrolling
+ */
+static inline int
+_PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter)
+{
+    npy_intp wb;
+
+    _UPDATE_COORD_ITER(1)
+    _UPDATE_COORD_ITER(0)
+
+    return 0;
+}
+#undef _UPDATE_COORD_ITER
+
+/*
+ * Advance to the next neighbour
+ */
+static inline int
+PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter)
+{
+    _PyArrayNeighborhoodIter_IncrCoord (iter);
+    iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
+
+    return 0;
+}
+
+/*
+ * Reset functions
+ */
+static inline int
+PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter)
+{
+    npy_intp i;
+
+    for (i = 0; i < iter->nd; ++i) {
+        iter->coordinates[i] = iter->bounds[i][0];
+    }
+    iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
+
+    return 0;
+}

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/npy_3kcompat.h

@@ -0,0 +1,595 @@
+/*
+ * This is a convenience header file providing compatibility utilities
+ * for supporting different minor versions of Python 3.
+ * It was originally used to support the transition from Python 2,
+ * hence the "3k" naming.
+ *
+ * If you want to use this for your own projects, it's recommended to make a
+ * copy of it. Although the stuff below is unlikely to change, we don't provide
+ * strong backwards compatibility guarantees at the moment.
+ */
+
+#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_
+#define NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_
+
+#include <Python.h>
+#include <stdio.h>
+
+#ifndef NPY_PY3K
+#define NPY_PY3K 1
+#endif
+
+#include "numpy/npy_common.h"
+#include "numpy/ndarrayobject.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/*
+ * PyInt -> PyLong
+ */
+
+
+/*
+ * This is a renamed copy of the Python non-limited API function _PyLong_AsInt. It is
+ * included here because it is missing from the PyPy API. It completes the PyLong_As*
+ * group of functions and can be useful in replacing PyInt_Check.
+ */
+static inline int
+Npy__PyLong_AsInt(PyObject *obj)
+{
+    int overflow;
+    long result = PyLong_AsLongAndOverflow(obj, &overflow);
+
+    /* INT_MAX and INT_MIN are defined in Python.h */
+    if (overflow || result > INT_MAX || result < INT_MIN) {
+        /* XXX: could be cute and give a different
+           message for overflow == -1 */
+        PyErr_SetString(PyExc_OverflowError,
+                        "Python int too large to convert to C int");
+        return -1;
+    }
+    return (int)result;
+}
+
+
+#if defined(NPY_PY3K)
+/* Return True only if the long fits in a C long */
+static inline int PyInt_Check(PyObject *op) {
+    int overflow = 0;
+    if (!PyLong_Check(op)) {
+        return 0;
+    }
+    PyLong_AsLongAndOverflow(op, &overflow);
+    return (overflow == 0);
+}
+
+
+#define PyInt_FromLong PyLong_FromLong
+#define PyInt_AsLong PyLong_AsLong
+#define PyInt_AS_LONG PyLong_AsLong
+#define PyInt_AsSsize_t PyLong_AsSsize_t
+#define PyNumber_Int PyNumber_Long
+
+/* NOTE:
+ *
+ * Since the PyLong type is very different from the fixed-range PyInt,
+ * we don't define PyInt_Type -> PyLong_Type.
+ */
+#endif /* NPY_PY3K */
+
+/* Py3 changes PySlice_GetIndicesEx' first argument's type to PyObject* */
+#ifdef NPY_PY3K
+#  define NpySlice_GetIndicesEx PySlice_GetIndicesEx
+#else
+#  define NpySlice_GetIndicesEx(op, nop, start, end, step, slicelength) \
+    PySlice_GetIndicesEx((PySliceObject *)op, nop, start, end, step, slicelength)
+#endif
+
+#if PY_VERSION_HEX < 0x030900a4
+    /* Introduced in https://github.com/python/cpython/commit/d2ec81a8c99796b51fb8c49b77a7fe369863226f */
+    #define Py_SET_TYPE(obj, type) ((Py_TYPE(obj) = (type)), (void)0)
+    /* Introduced in https://github.com/python/cpython/commit/b10dc3e7a11fcdb97e285882eba6da92594f90f9 */
+    #define Py_SET_SIZE(obj, size) ((Py_SIZE(obj) = (size)), (void)0)
+    /* Introduced in https://github.com/python/cpython/commit/c86a11221df7e37da389f9c6ce6e47ea22dc44ff */
+    #define Py_SET_REFCNT(obj, refcnt) ((Py_REFCNT(obj) = (refcnt)), (void)0)
+#endif
+
+
+#define Npy_EnterRecursiveCall(x) Py_EnterRecursiveCall(x)
+
+/*
+ * PyString -> PyBytes
+ */
+
+#if defined(NPY_PY3K)
+
+#define PyString_Type PyBytes_Type
+#define PyString_Check PyBytes_Check
+#define PyStringObject PyBytesObject
+#define PyString_FromString PyBytes_FromString
+#define PyString_FromStringAndSize PyBytes_FromStringAndSize
+#define PyString_AS_STRING PyBytes_AS_STRING
+#define PyString_AsStringAndSize PyBytes_AsStringAndSize
+#define PyString_FromFormat PyBytes_FromFormat
+#define PyString_Concat PyBytes_Concat
+#define PyString_ConcatAndDel PyBytes_ConcatAndDel
+#define PyString_AsString PyBytes_AsString
+#define PyString_GET_SIZE PyBytes_GET_SIZE
+#define PyString_Size PyBytes_Size
+
+#define PyUString_Type PyUnicode_Type
+#define PyUString_Check PyUnicode_Check
+#define PyUStringObject PyUnicodeObject
+#define PyUString_FromString PyUnicode_FromString
+#define PyUString_FromStringAndSize PyUnicode_FromStringAndSize
+#define PyUString_FromFormat PyUnicode_FromFormat
+#define PyUString_Concat PyUnicode_Concat2
+#define PyUString_ConcatAndDel PyUnicode_ConcatAndDel
+#define PyUString_GET_SIZE PyUnicode_GET_SIZE
+#define PyUString_Size PyUnicode_Size
+#define PyUString_InternFromString PyUnicode_InternFromString
+#define PyUString_Format PyUnicode_Format
+
+#define PyBaseString_Check(obj) (PyUnicode_Check(obj))
+
+#else
+
+#define PyBytes_Type PyString_Type
+#define PyBytes_Check PyString_Check
+#define PyBytesObject PyStringObject
+#define PyBytes_FromString PyString_FromString
+#define PyBytes_FromStringAndSize PyString_FromStringAndSize
+#define PyBytes_AS_STRING PyString_AS_STRING
+#define PyBytes_AsStringAndSize PyString_AsStringAndSize
+#define PyBytes_FromFormat PyString_FromFormat
+#define PyBytes_Concat PyString_Concat
+#define PyBytes_ConcatAndDel PyString_ConcatAndDel
+#define PyBytes_AsString PyString_AsString
+#define PyBytes_GET_SIZE PyString_GET_SIZE
+#define PyBytes_Size PyString_Size
+
+#define PyUString_Type PyString_Type
+#define PyUString_Check PyString_Check
+#define PyUStringObject PyStringObject
+#define PyUString_FromString PyString_FromString
+#define PyUString_FromStringAndSize PyString_FromStringAndSize
+#define PyUString_FromFormat PyString_FromFormat
+#define PyUString_Concat PyString_Concat
+#define PyUString_ConcatAndDel PyString_ConcatAndDel
+#define PyUString_GET_SIZE PyString_GET_SIZE
+#define PyUString_Size PyString_Size
+#define PyUString_InternFromString PyString_InternFromString
+#define PyUString_Format PyString_Format
+
+#define PyBaseString_Check(obj) (PyBytes_Check(obj) || PyUnicode_Check(obj))
+
+#endif /* NPY_PY3K */
+
+/*
+ * Macros to protect CRT calls against instant termination when passed an
+ * invalid parameter (https://bugs.python.org/issue23524).
+ */
+#if defined _MSC_VER && _MSC_VER >= 1900
+
+#include <stdlib.h>
+
+extern _invalid_parameter_handler _Py_silent_invalid_parameter_handler;
+#define NPY_BEGIN_SUPPRESS_IPH { _invalid_parameter_handler _Py_old_handler = \
+    _set_thread_local_invalid_parameter_handler(_Py_silent_invalid_parameter_handler);
+#define NPY_END_SUPPRESS_IPH _set_thread_local_invalid_parameter_handler(_Py_old_handler); }
+
+#else
+
+#define NPY_BEGIN_SUPPRESS_IPH
+#define NPY_END_SUPPRESS_IPH
+
+#endif /* _MSC_VER >= 1900 */
+
+
+static inline void
+PyUnicode_ConcatAndDel(PyObject **left, PyObject *right)
+{
+    Py_SETREF(*left, PyUnicode_Concat(*left, right));
+    Py_DECREF(right);
+}
+
+static inline void
+PyUnicode_Concat2(PyObject **left, PyObject *right)
+{
+    Py_SETREF(*left, PyUnicode_Concat(*left, right));
+}
+
+/*
+ * PyFile_* compatibility
+ */
+
+/*
+ * Get a FILE* handle to the file represented by the Python object
+ */
+static inline FILE*
+npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos)
+{
+    int fd, fd2, unbuf;
+    Py_ssize_t fd2_tmp;
+    PyObject *ret, *os, *io, *io_raw;
+    npy_off_t pos;
+    FILE *handle;
+
+    /* For Python 2 PyFileObject, use PyFile_AsFile */
+#if !defined(NPY_PY3K)
+    if (PyFile_Check(file)) {
+        return PyFile_AsFile(file);
+    }
+#endif
+
+    /* Flush first to ensure things end up in the file in the correct order */
+    ret = PyObject_CallMethod(file, "flush", "");
+    if (ret == NULL) {
+        return NULL;
+    }
+    Py_DECREF(ret);
+    fd = PyObject_AsFileDescriptor(file);
+    if (fd == -1) {
+        return NULL;
+    }
+
+    /*
+     * The handle needs to be dup'd because we have to call fclose
+     * at the end
+     */
+    os = PyImport_ImportModule("os");
+    if (os == NULL) {
+        return NULL;
+    }
+    ret = PyObject_CallMethod(os, "dup", "i", fd);
+    Py_DECREF(os);
+    if (ret == NULL) {
+        return NULL;
+    }
+    fd2_tmp = PyNumber_AsSsize_t(ret, PyExc_IOError);
+    Py_DECREF(ret);
+    if (fd2_tmp == -1 && PyErr_Occurred()) {
+        return NULL;
+    }
+    if (fd2_tmp < INT_MIN || fd2_tmp > INT_MAX) {
+        PyErr_SetString(PyExc_IOError,
+                        "Getting an 'int' from os.dup() failed");
+        return NULL;
+    }
+    fd2 = (int)fd2_tmp;
+
+    /* Convert to FILE* handle */
+#ifdef _WIN32
+    NPY_BEGIN_SUPPRESS_IPH
+    handle = _fdopen(fd2, mode);
+    NPY_END_SUPPRESS_IPH
+#else
+    handle = fdopen(fd2, mode);
+#endif
+    if (handle == NULL) {
+        PyErr_SetString(PyExc_IOError,
+                        "Getting a FILE* from a Python file object via "
+                        "_fdopen failed. If you built NumPy, you probably "
+                        "linked with the wrong debug/release runtime");
+        return NULL;
+    }
+
+    /* Record the original raw file handle position */
+    *orig_pos = npy_ftell(handle);
+    if (*orig_pos == -1) {
+        /* The io module is needed to determine if buffering is used */
+        io = PyImport_ImportModule("io");
+        if (io == NULL) {
+            fclose(handle);
+            return NULL;
+        }
+        /* File object instances of RawIOBase are unbuffered */
+        io_raw = PyObject_GetAttrString(io, "RawIOBase");
+        Py_DECREF(io);
+        if (io_raw == NULL) {
+            fclose(handle);
+            return NULL;
+        }
+        unbuf = PyObject_IsInstance(file, io_raw);
+        Py_DECREF(io_raw);
+        if (unbuf == 1) {
+            /* Succeed if the IO is unbuffered */
+            return handle;
+        }
+        else {
+            PyErr_SetString(PyExc_IOError, "obtaining file position failed");
+            fclose(handle);
+            return NULL;
+        }
+    }
+
+    /* Seek raw handle to the Python-side position */
+    ret = PyObject_CallMethod(file, "tell", "");
+    if (ret == NULL) {
+        fclose(handle);
+        return NULL;
+    }
+    pos = PyLong_AsLongLong(ret);
+    Py_DECREF(ret);
+    if (PyErr_Occurred()) {
+        fclose(handle);
+        return NULL;
+    }
+    if (npy_fseek(handle, pos, SEEK_SET) == -1) {
+        PyErr_SetString(PyExc_IOError, "seeking file failed");
+        fclose(handle);
+        return NULL;
+    }
+    return handle;
+}
+
+/*
+ * Close the dup-ed file handle, and seek the Python one to the current position
+ */
+static inline int
+npy_PyFile_DupClose2(PyObject *file, FILE* handle, npy_off_t orig_pos)
+{
+    int fd, unbuf;
+    PyObject *ret, *io, *io_raw;
+    npy_off_t position;
+
+    /* For Python 2 PyFileObject, do nothing */
+#if !defined(NPY_PY3K)
+    if (PyFile_Check(file)) {
+        return 0;
+    }
+#endif
+
+    position = npy_ftell(handle);
+
+    /* Close the FILE* handle */
+    fclose(handle);
+
+    /*
+     * Restore original file handle position, in order to not confuse
+     * Python-side data structures
+     */
+    fd = PyObject_AsFileDescriptor(file);
+    if (fd == -1) {
+        return -1;
+    }
+
+    if (npy_lseek(fd, orig_pos, SEEK_SET) == -1) {
+
+        /* The io module is needed to determine if buffering is used */
+        io = PyImport_ImportModule("io");
+        if (io == NULL) {
+            return -1;
+        }
+        /* File object instances of RawIOBase are unbuffered */
+        io_raw = PyObject_GetAttrString(io, "RawIOBase");
+        Py_DECREF(io);
+        if (io_raw == NULL) {
+            return -1;
+        }
+        unbuf = PyObject_IsInstance(file, io_raw);
+        Py_DECREF(io_raw);
+        if (unbuf == 1) {
+            /* Succeed if the IO is unbuffered */
+            return 0;
+        }
+        else {
+            PyErr_SetString(PyExc_IOError, "seeking file failed");
+            return -1;
+        }
+    }
+
+    if (position == -1) {
+        PyErr_SetString(PyExc_IOError, "obtaining file position failed");
+        return -1;
+    }
+
+    /* Seek Python-side handle to the FILE* handle position */
+    ret = PyObject_CallMethod(file, "seek", NPY_OFF_T_PYFMT "i", position, 0);
+    if (ret == NULL) {
+        return -1;
+    }
+    Py_DECREF(ret);
+    return 0;
+}
+
+static inline int
+npy_PyFile_Check(PyObject *file)
+{
+    int fd;
+    /* For Python 2, check if it is a PyFileObject */
+#if !defined(NPY_PY3K)
+    if (PyFile_Check(file)) {
+        return 1;
+    }
+#endif
+    fd = PyObject_AsFileDescriptor(file);
+    if (fd == -1) {
+        PyErr_Clear();
+        return 0;
+    }
+    return 1;
+}
+
+static inline PyObject*
+npy_PyFile_OpenFile(PyObject *filename, const char *mode)
+{
+    PyObject *open;
+    open = PyDict_GetItemString(PyEval_GetBuiltins(), "open");
+    if (open == NULL) {
+        return NULL;
+    }
+    return PyObject_CallFunction(open, "Os", filename, mode);
+}
+
+static inline int
+npy_PyFile_CloseFile(PyObject *file)
+{
+    PyObject *ret;
+
+    ret = PyObject_CallMethod(file, "close", NULL);
+    if (ret == NULL) {
+        return -1;
+    }
+    Py_DECREF(ret);
+    return 0;
+}
+
+
+/* This is a copy of _PyErr_ChainExceptions
+ */
+static inline void
+npy_PyErr_ChainExceptions(PyObject *exc, PyObject *val, PyObject *tb)
+{
+    if (exc == NULL)
+        return;
+
+    if (PyErr_Occurred()) {
+        /* only py3 supports this anyway */
+        #ifdef NPY_PY3K
+            PyObject *exc2, *val2, *tb2;
+            PyErr_Fetch(&exc2, &val2, &tb2);
+            PyErr_NormalizeException(&exc, &val, &tb);
+            if (tb != NULL) {
+                PyException_SetTraceback(val, tb);
+                Py_DECREF(tb);
+            }
+            Py_DECREF(exc);
+            PyErr_NormalizeException(&exc2, &val2, &tb2);
+            PyException_SetContext(val2, val);
+            PyErr_Restore(exc2, val2, tb2);
+        #endif
+    }
+    else {
+        PyErr_Restore(exc, val, tb);
+    }
+}
+
+
+/* This is a copy of _PyErr_ChainExceptions, with:
+ *  - a minimal implementation for python 2
+ *  - __cause__ used instead of __context__
+ */
+static inline void
+npy_PyErr_ChainExceptionsCause(PyObject *exc, PyObject *val, PyObject *tb)
+{
+    if (exc == NULL)
+        return;
+
+    if (PyErr_Occurred()) {
+        /* only py3 supports this anyway */
+        #ifdef NPY_PY3K
+            PyObject *exc2, *val2, *tb2;
+            PyErr_Fetch(&exc2, &val2, &tb2);
+            PyErr_NormalizeException(&exc, &val, &tb);
+            if (tb != NULL) {
+                PyException_SetTraceback(val, tb);
+                Py_DECREF(tb);
+            }
+            Py_DECREF(exc);
+            PyErr_NormalizeException(&exc2, &val2, &tb2);
+            PyException_SetCause(val2, val);
+            PyErr_Restore(exc2, val2, tb2);
+        #endif
+    }
+    else {
+        PyErr_Restore(exc, val, tb);
+    }
+}
+
+/*
+ * PyObject_Cmp
+ */
+#if defined(NPY_PY3K)
+static inline int
+PyObject_Cmp(PyObject *i1, PyObject *i2, int *cmp)
+{
+    int v;
+    v = PyObject_RichCompareBool(i1, i2, Py_LT);
+    if (v == 1) {
+        *cmp = -1;
+        return 1;
+    }
+    else if (v == -1) {
+        return -1;
+    }
+
+    v = PyObject_RichCompareBool(i1, i2, Py_GT);
+    if (v == 1) {
+        *cmp = 1;
+        return 1;
+    }
+    else if (v == -1) {
+        return -1;
+    }
+
+    v = PyObject_RichCompareBool(i1, i2, Py_EQ);
+    if (v == 1) {
+        *cmp = 0;
+        return 1;
+    }
+    else {
+        *cmp = 0;
+        return -1;
+    }
+}
+#endif
+
+/*
+ * PyCObject functions adapted to PyCapsules.
+ *
+ * The main job here is to get rid of the improved error handling
+ * of PyCapsules. It's a shame...
+ */
+static inline PyObject *
+NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
+{
+    PyObject *ret = PyCapsule_New(ptr, NULL, dtor);
+    if (ret == NULL) {
+        PyErr_Clear();
+    }
+    return ret;
+}
+
+static inline PyObject *
+NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context, void (*dtor)(PyObject *))
+{
+    PyObject *ret = NpyCapsule_FromVoidPtr(ptr, dtor);
+    if (ret != NULL && PyCapsule_SetContext(ret, context) != 0) {
+        PyErr_Clear();
+        Py_DECREF(ret);
+        ret = NULL;
+    }
+    return ret;
+}
+
+static inline void *
+NpyCapsule_AsVoidPtr(PyObject *obj)
+{
+    void *ret = PyCapsule_GetPointer(obj, NULL);
+    if (ret == NULL) {
+        PyErr_Clear();
+    }
+    return ret;
+}
+
+static inline void *
+NpyCapsule_GetDesc(PyObject *obj)
+{
+    return PyCapsule_GetContext(obj);
+}
+
+static inline int
+NpyCapsule_Check(PyObject *ptr)
+{
+    return PyCapsule_CheckExact(ptr);
+}
+
+#ifdef __cplusplus
+}
+#endif
+
+
+#endif  /* NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ */

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/npy_cpu.h

@@ -0,0 +1,129 @@
+/*
+ * This set (target) cpu specific macros:
+ *      - Possible values:
+ *              NPY_CPU_X86
+ *              NPY_CPU_AMD64
+ *              NPY_CPU_PPC
+ *              NPY_CPU_PPC64
+ *              NPY_CPU_PPC64LE
+ *              NPY_CPU_SPARC
+ *              NPY_CPU_S390
+ *              NPY_CPU_IA64
+ *              NPY_CPU_HPPA
+ *              NPY_CPU_ALPHA
+ *              NPY_CPU_ARMEL
+ *              NPY_CPU_ARMEB
+ *              NPY_CPU_SH_LE
+ *              NPY_CPU_SH_BE
+ *              NPY_CPU_ARCEL
+ *              NPY_CPU_ARCEB
+ *              NPY_CPU_RISCV64
+ *              NPY_CPU_LOONGARCH
+ *              NPY_CPU_WASM
+ */
+#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_
+#define NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_
+
+#include "numpyconfig.h"
+
+#if defined( __i386__ ) || defined(i386) || defined(_M_IX86)
+    /*
+     * __i386__ is defined by gcc and Intel compiler on Linux,
+     * _M_IX86 by VS compiler,
+     * i386 by Sun compilers on opensolaris at least
+     */
+    #define NPY_CPU_X86
+#elif defined(__x86_64__) || defined(__amd64__) || defined(__x86_64) || defined(_M_AMD64)
+    /*
+     * both __x86_64__ and __amd64__ are defined by gcc
+     * __x86_64 defined by sun compiler on opensolaris at least
+     * _M_AMD64 defined by MS compiler
+     */
+    #define NPY_CPU_AMD64
+#elif defined(__powerpc64__) && defined(__LITTLE_ENDIAN__)
+    #define NPY_CPU_PPC64LE
+#elif defined(__powerpc64__) && defined(__BIG_ENDIAN__)
+    #define NPY_CPU_PPC64
+#elif defined(__ppc__) || defined(__powerpc__) || defined(_ARCH_PPC)
+    /*
+     * __ppc__ is defined by gcc, I remember having seen __powerpc__ once,
+     * but can't find it ATM
+     * _ARCH_PPC is used by at least gcc on AIX
+     * As __powerpc__ and _ARCH_PPC are also defined by PPC64 check
+     * for those specifically first before defaulting to ppc
+     */
+    #define NPY_CPU_PPC
+#elif defined(__sparc__) || defined(__sparc)
+    /* __sparc__ is defined by gcc and Forte (e.g. Sun) compilers */
+    #define NPY_CPU_SPARC
+#elif defined(__s390__)
+    #define NPY_CPU_S390
+#elif defined(__ia64)
+    #define NPY_CPU_IA64
+#elif defined(__hppa)
+    #define NPY_CPU_HPPA
+#elif defined(__alpha__)
+    #define NPY_CPU_ALPHA
+#elif defined(__arm__) || defined(__aarch64__) || defined(_M_ARM64)
+    /* _M_ARM64 is defined in MSVC for ARM64 compilation on Windows */
+    #if defined(__ARMEB__) || defined(__AARCH64EB__)
+        #if defined(__ARM_32BIT_STATE)
+            #define NPY_CPU_ARMEB_AARCH32
+        #elif defined(__ARM_64BIT_STATE)
+            #define NPY_CPU_ARMEB_AARCH64
+        #else
+            #define NPY_CPU_ARMEB
+        #endif
+    #elif defined(__ARMEL__) || defined(__AARCH64EL__) || defined(_M_ARM64)
+        #if defined(__ARM_32BIT_STATE)
+            #define NPY_CPU_ARMEL_AARCH32
+        #elif defined(__ARM_64BIT_STATE) || defined(_M_ARM64) || defined(__AARCH64EL__)
+            #define NPY_CPU_ARMEL_AARCH64
+        #else
+            #define NPY_CPU_ARMEL
+        #endif
+    #else
+        # error Unknown ARM CPU, please report this to numpy maintainers with \
+	information about your platform (OS, CPU and compiler)
+    #endif
+#elif defined(__sh__) && defined(__LITTLE_ENDIAN__)
+    #define NPY_CPU_SH_LE
+#elif defined(__sh__) && defined(__BIG_ENDIAN__)
+    #define NPY_CPU_SH_BE
+#elif defined(__MIPSEL__)
+    #define NPY_CPU_MIPSEL
+#elif defined(__MIPSEB__)
+    #define NPY_CPU_MIPSEB
+#elif defined(__or1k__)
+    #define NPY_CPU_OR1K
+#elif defined(__mc68000__)
+    #define NPY_CPU_M68K
+#elif defined(__arc__) && defined(__LITTLE_ENDIAN__)
+    #define NPY_CPU_ARCEL
+#elif defined(__arc__) && defined(__BIG_ENDIAN__)
+    #define NPY_CPU_ARCEB
+#elif defined(__riscv) && defined(__riscv_xlen) && __riscv_xlen == 64
+    #define NPY_CPU_RISCV64
+#elif defined(__loongarch__)
+    #define NPY_CPU_LOONGARCH
+#elif defined(__EMSCRIPTEN__)
+    /* __EMSCRIPTEN__ is defined by emscripten: an LLVM-to-Web compiler */
+    #define NPY_CPU_WASM
+#else
+    #error Unknown CPU, please report this to numpy maintainers with \
+    information about your platform (OS, CPU and compiler)
+#endif
+
+/*
+ * Except for the following architectures, memory access is limited to the natural
+ * alignment of data types otherwise it may lead to bus error or performance regression.
+ * For more details about unaligned access, see https://www.kernel.org/doc/Documentation/unaligned-memory-access.txt.
+*/
+#if defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64) || defined(__aarch64__) || defined(__powerpc64__)
+    #define NPY_ALIGNMENT_REQUIRED 0
+#endif
+#ifndef NPY_ALIGNMENT_REQUIRED
+    #define NPY_ALIGNMENT_REQUIRED 1
+#endif
+
+#endif  /* NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ */

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/npy_no_deprecated_api.h

@@ -0,0 +1,20 @@
+/*
+ * This include file is provided for inclusion in Cython *.pyd files where
+ * one would like to define the NPY_NO_DEPRECATED_API macro. It can be
+ * included by
+ *
+ * cdef extern from "npy_no_deprecated_api.h": pass
+ *
+ */
+#ifndef NPY_NO_DEPRECATED_API
+
+/* put this check here since there may be multiple includes in C extensions. */
+#if defined(NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_) || \
+    defined(NUMPY_CORE_INCLUDE_NUMPY_NPY_DEPRECATED_API_H) || \
+    defined(NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_)
+#error "npy_no_deprecated_api.h" must be first among numpy includes.
+#else
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#endif
+
+#endif  /* NPY_NO_DEPRECATED_API */

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/npy_os.h

@@ -0,0 +1,42 @@
+#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_
+#define NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_
+
+#if defined(linux) || defined(__linux) || defined(__linux__)
+    #define NPY_OS_LINUX
+#elif defined(__FreeBSD__) || defined(__NetBSD__) || \
+            defined(__OpenBSD__) || defined(__DragonFly__)
+    #define NPY_OS_BSD
+    #ifdef __FreeBSD__
+        #define NPY_OS_FREEBSD
+    #elif defined(__NetBSD__)
+        #define NPY_OS_NETBSD
+    #elif defined(__OpenBSD__)
+        #define NPY_OS_OPENBSD
+    #elif defined(__DragonFly__)
+        #define NPY_OS_DRAGONFLY
+    #endif
+#elif defined(sun) || defined(__sun)
+    #define NPY_OS_SOLARIS
+#elif defined(__CYGWIN__)
+    #define NPY_OS_CYGWIN
+/* We are on Windows.*/
+#elif defined(_WIN32)
+  /* We are using MinGW (64-bit or 32-bit)*/
+  #if defined(__MINGW32__) || defined(__MINGW64__)
+    #define NPY_OS_MINGW
+  /* Otherwise, if _WIN64 is defined, we are targeting 64-bit Windows*/
+  #elif defined(_WIN64)
+    #define NPY_OS_WIN64
+  /* Otherwise assume we are targeting 32-bit Windows*/
+  #else
+    #define NPY_OS_WIN32
+  #endif
+#elif defined(__APPLE__)
+    #define NPY_OS_DARWIN
+#elif defined(__HAIKU__)
+    #define NPY_OS_HAIKU
+#else
+    #define NPY_OS_UNKNOWN
+#endif
+
+#endif  /* NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ */

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/random/LICENSE.txt

@@ -0,0 +1,21 @@
+  zlib License
+  ------------
+
+  Copyright (C) 2010 - 2019 ridiculous_fish, <libdivide@ridiculousfish.com>
+  Copyright (C) 2016 - 2019 Kim Walisch, <kim.walisch@gmail.com>
+
+  This software is provided 'as-is', without any express or implied
+  warranty.  In no event will the authors be held liable for any damages
+  arising from the use of this software.
+
+  Permission is granted to anyone to use this software for any purpose,
+  including commercial applications, and to alter it and redistribute it
+  freely, subject to the following restrictions:
+
+  1. The origin of this software must not be misrepresented; you must not
+     claim that you wrote the original software. If you use this software
+     in a product, an acknowledgment in the product documentation would be
+     appreciated but is not required.
+  2. Altered source versions must be plainly marked as such, and must not be
+     misrepresented as being the original software.
+  3. This notice may not be removed or altered from any source distribution.

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/random/bitgen.h

@@ -0,0 +1,20 @@
+#ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_
+#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_
+
+#pragma once
+#include <stddef.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+/* Must match the declaration in numpy/random/<any>.pxd */
+
+typedef struct bitgen {
+  void *state;
+  uint64_t (*next_uint64)(void *st);
+  uint32_t (*next_uint32)(void *st);
+  double (*next_double)(void *st);
+  uint64_t (*next_raw)(void *st);
+} bitgen_t;
+
+
+#endif  /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ */

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/random/libdivide.h

@@ -0,0 +1,2079 @@
+// libdivide.h - Optimized integer division
+// https://libdivide.com
+//
+// Copyright (C) 2010 - 2019 ridiculous_fish, <libdivide@ridiculousfish.com>
+// Copyright (C) 2016 - 2019 Kim Walisch, <kim.walisch@gmail.com>
+//
+// libdivide is dual-licensed under the Boost or zlib licenses.
+// You may use libdivide under the terms of either of these.
+// See LICENSE.txt for more details.
+
+#ifndef NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_
+#define NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_
+
+#define LIBDIVIDE_VERSION "3.0"
+#define LIBDIVIDE_VERSION_MAJOR 3
+#define LIBDIVIDE_VERSION_MINOR 0
+
+#include <stdint.h>
+
+#if defined(__cplusplus)
+    #include <cstdlib>
+    #include <cstdio>
+    #include <type_traits>
+#else
+    #include <stdlib.h>
+    #include <stdio.h>
+#endif
+
+#if defined(LIBDIVIDE_AVX512)
+    #include <immintrin.h>
+#elif defined(LIBDIVIDE_AVX2)
+    #include <immintrin.h>
+#elif defined(LIBDIVIDE_SSE2)
+    #include <emmintrin.h>
+#endif
+
+#if defined(_MSC_VER)
+    #include <intrin.h>
+    // disable warning C4146: unary minus operator applied
+    // to unsigned type, result still unsigned
+    #pragma warning(disable: 4146)
+    #define LIBDIVIDE_VC
+#endif
+
+#if !defined(__has_builtin)
+    #define __has_builtin(x) 0
+#endif
+
+#if defined(__SIZEOF_INT128__)
+    #define HAS_INT128_T
+    // clang-cl on Windows does not yet support 128-bit division
+    #if !(defined(__clang__) && defined(LIBDIVIDE_VC))
+        #define HAS_INT128_DIV
+    #endif
+#endif
+
+#if defined(__x86_64__) || defined(_M_X64)
+    #define LIBDIVIDE_X86_64
+#endif
+
+#if defined(__i386__)
+    #define LIBDIVIDE_i386
+#endif
+
+#if defined(__GNUC__) || defined(__clang__)
+    #define LIBDIVIDE_GCC_STYLE_ASM
+#endif
+
+#if defined(__cplusplus) || defined(LIBDIVIDE_VC)
+    #define LIBDIVIDE_FUNCTION __FUNCTION__
+#else
+    #define LIBDIVIDE_FUNCTION __func__
+#endif
+
+#define LIBDIVIDE_ERROR(msg) \
+    do { \
+        fprintf(stderr, "libdivide.h:%d: %s(): Error: %s\n", \
+            __LINE__, LIBDIVIDE_FUNCTION, msg); \
+        abort(); \
+    } while (0)
+
+#if defined(LIBDIVIDE_ASSERTIONS_ON)
+    #define LIBDIVIDE_ASSERT(x) \
+        do { \
+            if (!(x)) { \
+                fprintf(stderr, "libdivide.h:%d: %s(): Assertion failed: %s\n", \
+                    __LINE__, LIBDIVIDE_FUNCTION, #x); \
+                abort(); \
+            } \
+        } while (0)
+#else
+    #define LIBDIVIDE_ASSERT(x)
+#endif
+
+#ifdef __cplusplus
+namespace libdivide {
+#endif
+
+// pack divider structs to prevent compilers from padding.
+// This reduces memory usage by up to 43% when using a large
+// array of libdivide dividers and improves performance
+// by up to 10% because of reduced memory bandwidth.
+#pragma pack(push, 1)
+
+struct libdivide_u32_t {
+    uint32_t magic;
+    uint8_t more;
+};
+
+struct libdivide_s32_t {
+    int32_t magic;
+    uint8_t more;
+};
+
+struct libdivide_u64_t {
+    uint64_t magic;
+    uint8_t more;
+};
+
+struct libdivide_s64_t {
+    int64_t magic;
+    uint8_t more;
+};
+
+struct libdivide_u32_branchfree_t {
+    uint32_t magic;
+    uint8_t more;
+};
+
+struct libdivide_s32_branchfree_t {
+    int32_t magic;
+    uint8_t more;
+};
+
+struct libdivide_u64_branchfree_t {
+    uint64_t magic;
+    uint8_t more;
+};
+
+struct libdivide_s64_branchfree_t {
+    int64_t magic;
+    uint8_t more;
+};
+
+#pragma pack(pop)
+
+// Explanation of the "more" field:
+//
+// * Bits 0-5 is the shift value (for shift path or mult path).
+// * Bit 6 is the add indicator for mult path.
+// * Bit 7 is set if the divisor is negative. We use bit 7 as the negative
+//   divisor indicator so that we can efficiently use sign extension to
+//   create a bitmask with all bits set to 1 (if the divisor is negative)
+//   or 0 (if the divisor is positive).
+//
+// u32: [0-4] shift value
+//      [5] ignored
+//      [6] add indicator
+//      magic number of 0 indicates shift path
+//
+// s32: [0-4] shift value
+//      [5] ignored
+//      [6] add indicator
+//      [7] indicates negative divisor
+//      magic number of 0 indicates shift path
+//
+// u64: [0-5] shift value
+//      [6] add indicator
+//      magic number of 0 indicates shift path
+//
+// s64: [0-5] shift value
+//      [6] add indicator
+//      [7] indicates negative divisor
+//      magic number of 0 indicates shift path
+//
+// In s32 and s64 branchfree modes, the magic number is negated according to
+// whether the divisor is negated. In branchfree strategy, it is not negated.
+
+enum {
+    LIBDIVIDE_32_SHIFT_MASK = 0x1F,
+    LIBDIVIDE_64_SHIFT_MASK = 0x3F,
+    LIBDIVIDE_ADD_MARKER = 0x40,
+    LIBDIVIDE_NEGATIVE_DIVISOR = 0x80
+};
+
+static inline struct libdivide_s32_t libdivide_s32_gen(int32_t d);
+static inline struct libdivide_u32_t libdivide_u32_gen(uint32_t d);
+static inline struct libdivide_s64_t libdivide_s64_gen(int64_t d);
+static inline struct libdivide_u64_t libdivide_u64_gen(uint64_t d);
+
+static inline struct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d);
+static inline struct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d);
+static inline struct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d);
+static inline struct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d);
+
+static inline int32_t  libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom);
+static inline uint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom);
+static inline int64_t  libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom);
+static inline uint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom);
+
+static inline int32_t  libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom);
+static inline uint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom);
+static inline int64_t  libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom);
+static inline uint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom);
+
+static inline int32_t  libdivide_s32_recover(const struct libdivide_s32_t *denom);
+static inline uint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom);
+static inline int64_t  libdivide_s64_recover(const struct libdivide_s64_t *denom);
+static inline uint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom);
+
+static inline int32_t  libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom);
+static inline uint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom);
+static inline int64_t  libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom);
+static inline uint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom);
+
+//////// Internal Utility Functions
+
+static inline uint32_t libdivide_mullhi_u32(uint32_t x, uint32_t y) {
+    uint64_t xl = x, yl = y;
+    uint64_t rl = xl * yl;
+    return (uint32_t)(rl >> 32);
+}
+
+static inline int32_t libdivide_mullhi_s32(int32_t x, int32_t y) {
+    int64_t xl = x, yl = y;
+    int64_t rl = xl * yl;
+    // needs to be arithmetic shift
+    return (int32_t)(rl >> 32);
+}
+
+static inline uint64_t libdivide_mullhi_u64(uint64_t x, uint64_t y) {
+#if defined(LIBDIVIDE_VC) && \
+    defined(LIBDIVIDE_X86_64)
+    return __umulh(x, y);
+#elif defined(HAS_INT128_T)
+    __uint128_t xl = x, yl = y;
+    __uint128_t rl = xl * yl;
+    return (uint64_t)(rl >> 64);
+#else
+    // full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64)
+    uint32_t mask = 0xFFFFFFFF;
+    uint32_t x0 = (uint32_t)(x & mask);
+    uint32_t x1 = (uint32_t)(x >> 32);
+    uint32_t y0 = (uint32_t)(y & mask);
+    uint32_t y1 = (uint32_t)(y >> 32);
+    uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0);
+    uint64_t x0y1 = x0 * (uint64_t)y1;
+    uint64_t x1y0 = x1 * (uint64_t)y0;
+    uint64_t x1y1 = x1 * (uint64_t)y1;
+    uint64_t temp = x1y0 + x0y0_hi;
+    uint64_t temp_lo = temp & mask;
+    uint64_t temp_hi = temp >> 32;
+
+    return x1y1 + temp_hi + ((temp_lo + x0y1) >> 32);
+#endif
+}
+
+static inline int64_t libdivide_mullhi_s64(int64_t x, int64_t y) {
+#if defined(LIBDIVIDE_VC) && \
+    defined(LIBDIVIDE_X86_64)
+    return __mulh(x, y);
+#elif defined(HAS_INT128_T)
+    __int128_t xl = x, yl = y;
+    __int128_t rl = xl * yl;
+    return (int64_t)(rl >> 64);
+#else
+    // full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64)
+    uint32_t mask = 0xFFFFFFFF;
+    uint32_t x0 = (uint32_t)(x & mask);
+    uint32_t y0 = (uint32_t)(y & mask);
+    int32_t x1 = (int32_t)(x >> 32);
+    int32_t y1 = (int32_t)(y >> 32);
+    uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0);
+    int64_t t = x1 * (int64_t)y0 + x0y0_hi;
+    int64_t w1 = x0 * (int64_t)y1 + (t & mask);
+
+    return x1 * (int64_t)y1 + (t >> 32) + (w1 >> 32);
+#endif
+}
+
+static inline int32_t libdivide_count_leading_zeros32(uint32_t val) {
+#if defined(__GNUC__) || \
+    __has_builtin(__builtin_clz)
+    // Fast way to count leading zeros
+    return __builtin_clz(val);
+#elif defined(LIBDIVIDE_VC)
+    unsigned long result;
+    if (_BitScanReverse(&result, val)) {
+        return 31 - result;
+    }
+    return 0;
+#else
+    if (val == 0)
+        return 32;
+    int32_t result = 8;
+    uint32_t hi = 0xFFU << 24;
+    while ((val & hi) == 0) {
+        hi >>= 8;
+        result += 8;
+    }
+    while (val & hi) {
+        result -= 1;
+        hi <<= 1;
+    }
+    return result;
+#endif
+}
+
+static inline int32_t libdivide_count_leading_zeros64(uint64_t val) {
+#if defined(__GNUC__) || \
+    __has_builtin(__builtin_clzll)
+    // Fast way to count leading zeros
+    return __builtin_clzll(val);
+#elif defined(LIBDIVIDE_VC) && defined(_WIN64)
+    unsigned long result;
+    if (_BitScanReverse64(&result, val)) {
+        return 63 - result;
+    }
+    return 0;
+#else
+    uint32_t hi = val >> 32;
+    uint32_t lo = val & 0xFFFFFFFF;
+    if (hi != 0) return libdivide_count_leading_zeros32(hi);
+    return 32 + libdivide_count_leading_zeros32(lo);
+#endif
+}
+
+// libdivide_64_div_32_to_32: divides a 64-bit uint {u1, u0} by a 32-bit
+// uint {v}. The result must fit in 32 bits.
+// Returns the quotient directly and the remainder in *r
+static inline uint32_t libdivide_64_div_32_to_32(uint32_t u1, uint32_t u0, uint32_t v, uint32_t *r) {
+#if (defined(LIBDIVIDE_i386) || defined(LIBDIVIDE_X86_64)) && \
+     defined(LIBDIVIDE_GCC_STYLE_ASM)
+    uint32_t result;
+    __asm__("divl %[v]"
+            : "=a"(result), "=d"(*r)
+            : [v] "r"(v), "a"(u0), "d"(u1)
+            );
+    return result;
+#else
+    uint64_t n = ((uint64_t)u1 << 32) | u0;
+    uint32_t result = (uint32_t)(n / v);
+    *r = (uint32_t)(n - result * (uint64_t)v);
+    return result;
+#endif
+}
+
+// libdivide_128_div_64_to_64: divides a 128-bit uint {u1, u0} by a 64-bit
+// uint {v}. The result must fit in 64 bits.
+// Returns the quotient directly and the remainder in *r
+static uint64_t libdivide_128_div_64_to_64(uint64_t u1, uint64_t u0, uint64_t v, uint64_t *r) {
+#if defined(LIBDIVIDE_X86_64) && \
+    defined(LIBDIVIDE_GCC_STYLE_ASM)
+    uint64_t result;
+    __asm__("divq %[v]"
+            : "=a"(result), "=d"(*r)
+            : [v] "r"(v), "a"(u0), "d"(u1)
+            );
+    return result;
+#elif defined(HAS_INT128_T) && \
+      defined(HAS_INT128_DIV)
+    __uint128_t n = ((__uint128_t)u1 << 64) | u0;
+    uint64_t result = (uint64_t)(n / v);
+    *r = (uint64_t)(n - result * (__uint128_t)v);
+    return result;
+#else
+    // Code taken from Hacker's Delight:
+    // http://www.hackersdelight.org/HDcode/divlu.c.
+    // License permits inclusion here per:
+    // http://www.hackersdelight.org/permissions.htm
+
+    const uint64_t b = (1ULL << 32); // Number base (32 bits)
+    uint64_t un1, un0; // Norm. dividend LSD's
+    uint64_t vn1, vn0; // Norm. divisor digits
+    uint64_t q1, q0; // Quotient digits
+    uint64_t un64, un21, un10; // Dividend digit pairs
+    uint64_t rhat; // A remainder
+    int32_t s; // Shift amount for norm
+
+    // If overflow, set rem. to an impossible value,
+    // and return the largest possible quotient
+    if (u1 >= v) {
+        *r = (uint64_t) -1;
+        return (uint64_t) -1;
+    }
+
+    // count leading zeros
+    s = libdivide_count_leading_zeros64(v);
+    if (s > 0) {
+        // Normalize divisor
+        v = v << s;
+        un64 = (u1 << s) | (u0 >> (64 - s));
+        un10 = u0 << s; // Shift dividend left
+    } else {
+        // Avoid undefined behavior of (u0 >> 64).
+        // The behavior is undefined if the right operand is
+        // negative, or greater than or equal to the length
+        // in bits of the promoted left operand.
+        un64 = u1;
+        un10 = u0;
+    }
+
+    // Break divisor up into two 32-bit digits
+    vn1 = v >> 32;
+    vn0 = v & 0xFFFFFFFF;
+
+    // Break right half of dividend into two digits
+    un1 = un10 >> 32;
+    un0 = un10 & 0xFFFFFFFF;
+
+    // Compute the first quotient digit, q1
+    q1 = un64 / vn1;
+    rhat = un64 - q1 * vn1;
+
+    while (q1 >= b || q1 * vn0 > b * rhat + un1) {
+        q1 = q1 - 1;
+        rhat = rhat + vn1;
+        if (rhat >= b)
+            break;
+    }
+
+     // Multiply and subtract
+    un21 = un64 * b + un1 - q1 * v;
+
+    // Compute the second quotient digit
+    q0 = un21 / vn1;
+    rhat = un21 - q0 * vn1;
+
+    while (q0 >= b || q0 * vn0 > b * rhat + un0) {
+        q0 = q0 - 1;
+        rhat = rhat + vn1;
+        if (rhat >= b)
+            break;
+    }
+
+    *r = (un21 * b + un0 - q0 * v) >> s;
+    return q1 * b + q0;
+#endif
+}
+
+// Bitshift a u128 in place, left (signed_shift > 0) or right (signed_shift < 0)
+static inline void libdivide_u128_shift(uint64_t *u1, uint64_t *u0, int32_t signed_shift) {
+    if (signed_shift > 0) {
+        uint32_t shift = signed_shift;
+        *u1 <<= shift;
+        *u1 |= *u0 >> (64 - shift);
+        *u0 <<= shift;
+    }
+    else if (signed_shift < 0) {
+        uint32_t shift = -signed_shift;
+        *u0 >>= shift;
+        *u0 |= *u1 << (64 - shift);
+        *u1 >>= shift;
+    }
+}
+
+// Computes a 128 / 128 -> 64 bit division, with a 128 bit remainder.
+static uint64_t libdivide_128_div_128_to_64(uint64_t u_hi, uint64_t u_lo, uint64_t v_hi, uint64_t v_lo, uint64_t *r_hi, uint64_t *r_lo) {
+#if defined(HAS_INT128_T) && \
+    defined(HAS_INT128_DIV)
+    __uint128_t ufull = u_hi;
+    __uint128_t vfull = v_hi;
+    ufull = (ufull << 64) | u_lo;
+    vfull = (vfull << 64) | v_lo;
+    uint64_t res = (uint64_t)(ufull / vfull);
+    __uint128_t remainder = ufull - (vfull * res);
+    *r_lo = (uint64_t)remainder;
+    *r_hi = (uint64_t)(remainder >> 64);
+    return res;
+#else
+    // Adapted from "Unsigned Doubleword Division" in Hacker's Delight
+    // We want to compute u / v
+    typedef struct { uint64_t hi; uint64_t lo; } u128_t;
+    u128_t u = {u_hi, u_lo};
+    u128_t v = {v_hi, v_lo};
+
+    if (v.hi == 0) {
+        // divisor v is a 64 bit value, so we just need one 128/64 division
+        // Note that we are simpler than Hacker's Delight here, because we know
+        // the quotient fits in 64 bits whereas Hacker's Delight demands a full
+        // 128 bit quotient
+        *r_hi = 0;
+        return libdivide_128_div_64_to_64(u.hi, u.lo, v.lo, r_lo);
+    }
+    // Here v >= 2**64
+    // We know that v.hi != 0, so count leading zeros is OK
+    // We have 0 <= n <= 63
+    uint32_t n = libdivide_count_leading_zeros64(v.hi);
+
+    // Normalize the divisor so its MSB is 1
+    u128_t v1t = v;
+    libdivide_u128_shift(&v1t.hi, &v1t.lo, n);
+    uint64_t v1 = v1t.hi; // i.e. v1 = v1t >> 64
+
+    // To ensure no overflow
+    u128_t u1 = u;
+    libdivide_u128_shift(&u1.hi, &u1.lo, -1);
+
+    // Get quotient from divide unsigned insn.
+    uint64_t rem_ignored;
+    uint64_t q1 = libdivide_128_div_64_to_64(u1.hi, u1.lo, v1, &rem_ignored);
+
+    // Undo normalization and division of u by 2.
+    u128_t q0 = {0, q1};
+    libdivide_u128_shift(&q0.hi, &q0.lo, n);
+    libdivide_u128_shift(&q0.hi, &q0.lo, -63);
+
+    // Make q0 correct or too small by 1
+    // Equivalent to `if (q0 != 0) q0 = q0 - 1;`
+    if (q0.hi != 0 || q0.lo != 0) {
+        q0.hi -= (q0.lo == 0); // borrow
+        q0.lo -= 1;
+    }
+
+    // Now q0 is correct.
+    // Compute q0 * v as q0v
+    // = (q0.hi << 64 + q0.lo) * (v.hi << 64 + v.lo)
+    // = (q0.hi * v.hi << 128) + (q0.hi * v.lo << 64) +
+    //   (q0.lo * v.hi <<  64) + q0.lo * v.lo)
+    // Each term is 128 bit
+    // High half of full product (upper 128 bits!) are dropped
+    u128_t q0v = {0, 0};
+    q0v.hi = q0.hi*v.lo + q0.lo*v.hi + libdivide_mullhi_u64(q0.lo, v.lo);
+    q0v.lo = q0.lo*v.lo;
+
+    // Compute u - q0v as u_q0v
+    // This is the remainder
+    u128_t u_q0v = u;
+    u_q0v.hi -= q0v.hi + (u.lo < q0v.lo); // second term is borrow
+    u_q0v.lo -= q0v.lo;
+
+    // Check if u_q0v >= v
+    // This checks if our remainder is larger than the divisor
+    if ((u_q0v.hi > v.hi) ||
+        (u_q0v.hi == v.hi && u_q0v.lo >= v.lo)) {
+        // Increment q0
+        q0.lo += 1;
+        q0.hi += (q0.lo == 0); // carry
+
+        // Subtract v from remainder
+        u_q0v.hi -= v.hi + (u_q0v.lo < v.lo);
+        u_q0v.lo -= v.lo;
+    }
+
+    *r_hi = u_q0v.hi;
+    *r_lo = u_q0v.lo;
+
+    LIBDIVIDE_ASSERT(q0.hi == 0);
+    return q0.lo;
+#endif
+}
+
+////////// UINT32
+
+static inline struct libdivide_u32_t libdivide_internal_u32_gen(uint32_t d, int branchfree) {
+    if (d == 0) {
+        LIBDIVIDE_ERROR("divider must be != 0");
+    }
+
+    struct libdivide_u32_t result;
+    uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(d);
+
+    // Power of 2
+    if ((d & (d - 1)) == 0) {
+        // We need to subtract 1 from the shift value in case of an unsigned
+        // branchfree divider because there is a hardcoded right shift by 1
+        // in its division algorithm. Because of this we also need to add back
+        // 1 in its recovery algorithm.
+        result.magic = 0;
+        result.more = (uint8_t)(floor_log_2_d - (branchfree != 0));
+    } else {
+        uint8_t more;
+        uint32_t rem, proposed_m;
+        proposed_m = libdivide_64_div_32_to_32(1U << floor_log_2_d, 0, d, &rem);
+
+        LIBDIVIDE_ASSERT(rem > 0 && rem < d);
+        const uint32_t e = d - rem;
+
+        // This power works if e < 2**floor_log_2_d.
+        if (!branchfree && (e < (1U << floor_log_2_d))) {
+            // This power works
+            more = floor_log_2_d;
+        } else {
+            // We have to use the general 33-bit algorithm.  We need to compute
+            // (2**power) / d. However, we already have (2**(power-1))/d and
+            // its remainder.  By doubling both, and then correcting the
+            // remainder, we can compute the larger division.
+            // don't care about overflow here - in fact, we expect it
+            proposed_m += proposed_m;
+            const uint32_t twice_rem = rem + rem;
+            if (twice_rem >= d || twice_rem < rem) proposed_m += 1;
+            more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
+        }
+        result.magic = 1 + proposed_m;
+        result.more = more;
+        // result.more's shift should in general be ceil_log_2_d. But if we
+        // used the smaller power, we subtract one from the shift because we're
+        // using the smaller power. If we're using the larger power, we
+        // subtract one from the shift because it's taken care of by the add
+        // indicator. So floor_log_2_d happens to be correct in both cases.
+    }
+    return result;
+}
+
+struct libdivide_u32_t libdivide_u32_gen(uint32_t d) {
+    return libdivide_internal_u32_gen(d, 0);
+}
+
+struct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d) {
+    if (d == 1) {
+        LIBDIVIDE_ERROR("branchfree divider must be != 1");
+    }
+    struct libdivide_u32_t tmp = libdivide_internal_u32_gen(d, 1);
+    struct libdivide_u32_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_32_SHIFT_MASK)};
+    return ret;
+}
+
+uint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return numer >> more;
+    }
+    else {
+        uint32_t q = libdivide_mullhi_u32(denom->magic, numer);
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            uint32_t t = ((numer - q) >> 1) + q;
+            return t >> (more & LIBDIVIDE_32_SHIFT_MASK);
+        }
+        else {
+            // All upper bits are 0,
+            // don't need to mask them off.
+            return q >> more;
+        }
+    }
+}
+
+uint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom) {
+    uint32_t q = libdivide_mullhi_u32(denom->magic, numer);
+    uint32_t t = ((numer - q) >> 1) + q;
+    return t >> denom->more;
+}
+
+uint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+
+    if (!denom->magic) {
+        return 1U << shift;
+    } else if (!(more & LIBDIVIDE_ADD_MARKER)) {
+        // We compute q = n/d = n*m / 2^(32 + shift)
+        // Therefore we have d = 2^(32 + shift) / m
+        // We need to ceil it.
+        // We know d is not a power of 2, so m is not a power of 2,
+        // so we can just add 1 to the floor
+        uint32_t hi_dividend = 1U << shift;
+        uint32_t rem_ignored;
+        return 1 + libdivide_64_div_32_to_32(hi_dividend, 0, denom->magic, &rem_ignored);
+    } else {
+        // Here we wish to compute d = 2^(32+shift+1)/(m+2^32).
+        // Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now
+        // Also note that shift may be as high as 31, so shift + 1 will
+        // overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and
+        // then double the quotient and remainder.
+        uint64_t half_n = 1ULL << (32 + shift);
+        uint64_t d = (1ULL << 32) | denom->magic;
+        // Note that the quotient is guaranteed <= 32 bits, but the remainder
+        // may need 33!
+        uint32_t half_q = (uint32_t)(half_n / d);
+        uint64_t rem = half_n % d;
+        // We computed 2^(32+shift)/(m+2^32)
+        // Need to double it, and then add 1 to the quotient if doubling th
+        // remainder would increase the quotient.
+        // Note that rem<<1 cannot overflow, since rem < d and d is 33 bits
+        uint32_t full_q = half_q + half_q + ((rem<<1) >= d);
+
+        // We rounded down in gen (hence +1)
+        return full_q + 1;
+    }
+}
+
+uint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+
+    if (!denom->magic) {
+        return 1U << (shift + 1);
+    } else {
+        // Here we wish to compute d = 2^(32+shift+1)/(m+2^32).
+        // Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now
+        // Also note that shift may be as high as 31, so shift + 1 will
+        // overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and
+        // then double the quotient and remainder.
+        uint64_t half_n = 1ULL << (32 + shift);
+        uint64_t d = (1ULL << 32) | denom->magic;
+        // Note that the quotient is guaranteed <= 32 bits, but the remainder
+        // may need 33!
+        uint32_t half_q = (uint32_t)(half_n / d);
+        uint64_t rem = half_n % d;
+        // We computed 2^(32+shift)/(m+2^32)
+        // Need to double it, and then add 1 to the quotient if doubling th
+        // remainder would increase the quotient.
+        // Note that rem<<1 cannot overflow, since rem < d and d is 33 bits
+        uint32_t full_q = half_q + half_q + ((rem<<1) >= d);
+
+        // We rounded down in gen (hence +1)
+        return full_q + 1;
+    }
+}
+
+/////////// UINT64
+
+static inline struct libdivide_u64_t libdivide_internal_u64_gen(uint64_t d, int branchfree) {
+    if (d == 0) {
+        LIBDIVIDE_ERROR("divider must be != 0");
+    }
+
+    struct libdivide_u64_t result;
+    uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(d);
+
+    // Power of 2
+    if ((d & (d - 1)) == 0) {
+        // We need to subtract 1 from the shift value in case of an unsigned
+        // branchfree divider because there is a hardcoded right shift by 1
+        // in its division algorithm. Because of this we also need to add back
+        // 1 in its recovery algorithm.
+        result.magic = 0;
+        result.more = (uint8_t)(floor_log_2_d - (branchfree != 0));
+    } else {
+        uint64_t proposed_m, rem;
+        uint8_t more;
+        // (1 << (64 + floor_log_2_d)) / d
+        proposed_m = libdivide_128_div_64_to_64(1ULL << floor_log_2_d, 0, d, &rem);
+
+        LIBDIVIDE_ASSERT(rem > 0 && rem < d);
+        const uint64_t e = d - rem;
+
+        // This power works if e < 2**floor_log_2_d.
+        if (!branchfree && e < (1ULL << floor_log_2_d)) {
+            // This power works
+            more = floor_log_2_d;
+        } else {
+            // We have to use the general 65-bit algorithm.  We need to compute
+            // (2**power) / d. However, we already have (2**(power-1))/d and
+            // its remainder. By doubling both, and then correcting the
+            // remainder, we can compute the larger division.
+            // don't care about overflow here - in fact, we expect it
+            proposed_m += proposed_m;
+            const uint64_t twice_rem = rem + rem;
+            if (twice_rem >= d || twice_rem < rem) proposed_m += 1;
+                more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
+        }
+        result.magic = 1 + proposed_m;
+        result.more = more;
+        // result.more's shift should in general be ceil_log_2_d. But if we
+        // used the smaller power, we subtract one from the shift because we're
+        // using the smaller power. If we're using the larger power, we
+        // subtract one from the shift because it's taken care of by the add
+        // indicator. So floor_log_2_d happens to be correct in both cases,
+        // which is why we do it outside of the if statement.
+    }
+    return result;
+}
+
+struct libdivide_u64_t libdivide_u64_gen(uint64_t d) {
+    return libdivide_internal_u64_gen(d, 0);
+}
+
+struct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d) {
+    if (d == 1) {
+        LIBDIVIDE_ERROR("branchfree divider must be != 1");
+    }
+    struct libdivide_u64_t tmp = libdivide_internal_u64_gen(d, 1);
+    struct libdivide_u64_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_64_SHIFT_MASK)};
+    return ret;
+}
+
+uint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return numer >> more;
+    }
+    else {
+        uint64_t q = libdivide_mullhi_u64(denom->magic, numer);
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            uint64_t t = ((numer - q) >> 1) + q;
+            return t >> (more & LIBDIVIDE_64_SHIFT_MASK);
+        }
+        else {
+             // All upper bits are 0,
+             // don't need to mask them off.
+            return q >> more;
+        }
+    }
+}
+
+uint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom) {
+    uint64_t q = libdivide_mullhi_u64(denom->magic, numer);
+    uint64_t t = ((numer - q) >> 1) + q;
+    return t >> denom->more;
+}
+
+uint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+
+    if (!denom->magic) {
+        return 1ULL << shift;
+    } else if (!(more & LIBDIVIDE_ADD_MARKER)) {
+        // We compute q = n/d = n*m / 2^(64 + shift)
+        // Therefore we have d = 2^(64 + shift) / m
+        // We need to ceil it.
+        // We know d is not a power of 2, so m is not a power of 2,
+        // so we can just add 1 to the floor
+        uint64_t hi_dividend = 1ULL << shift;
+        uint64_t rem_ignored;
+        return 1 + libdivide_128_div_64_to_64(hi_dividend, 0, denom->magic, &rem_ignored);
+    } else {
+        // Here we wish to compute d = 2^(64+shift+1)/(m+2^64).
+        // Notice (m + 2^64) is a 65 bit number. This gets hairy. See
+        // libdivide_u32_recover for more on what we do here.
+        // TODO: do something better than 128 bit math
+
+        // Full n is a (potentially) 129 bit value
+        // half_n is a 128 bit value
+        // Compute the hi half of half_n. Low half is 0.
+        uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0;
+        // d is a 65 bit value. The high bit is always set to 1.
+        const uint64_t d_hi = 1, d_lo = denom->magic;
+        // Note that the quotient is guaranteed <= 64 bits,
+        // but the remainder may need 65!
+        uint64_t r_hi, r_lo;
+        uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo);
+        // We computed 2^(64+shift)/(m+2^64)
+        // Double the remainder ('dr') and check if that is larger than d
+        // Note that d is a 65 bit value, so r1 is small and so r1 + r1
+        // cannot overflow
+        uint64_t dr_lo = r_lo + r_lo;
+        uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry
+        int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo);
+        uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0);
+        return full_q + 1;
+    }
+}
+
+uint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+
+    if (!denom->magic) {
+        return 1ULL << (shift + 1);
+    } else {
+        // Here we wish to compute d = 2^(64+shift+1)/(m+2^64).
+        // Notice (m + 2^64) is a 65 bit number. This gets hairy. See
+        // libdivide_u32_recover for more on what we do here.
+        // TODO: do something better than 128 bit math
+
+        // Full n is a (potentially) 129 bit value
+        // half_n is a 128 bit value
+        // Compute the hi half of half_n. Low half is 0.
+        uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0;
+        // d is a 65 bit value. The high bit is always set to 1.
+        const uint64_t d_hi = 1, d_lo = denom->magic;
+        // Note that the quotient is guaranteed <= 64 bits,
+        // but the remainder may need 65!
+        uint64_t r_hi, r_lo;
+        uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo);
+        // We computed 2^(64+shift)/(m+2^64)
+        // Double the remainder ('dr') and check if that is larger than d
+        // Note that d is a 65 bit value, so r1 is small and so r1 + r1
+        // cannot overflow
+        uint64_t dr_lo = r_lo + r_lo;
+        uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry
+        int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo);
+        uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0);
+        return full_q + 1;
+    }
+}
+
+/////////// SINT32
+
+static inline struct libdivide_s32_t libdivide_internal_s32_gen(int32_t d, int branchfree) {
+    if (d == 0) {
+        LIBDIVIDE_ERROR("divider must be != 0");
+    }
+
+    struct libdivide_s32_t result;
+
+    // If d is a power of 2, or negative a power of 2, we have to use a shift.
+    // This is especially important because the magic algorithm fails for -1.
+    // To check if d is a power of 2 or its inverse, it suffices to check
+    // whether its absolute value has exactly one bit set. This works even for
+    // INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set
+    // and is a power of 2.
+    uint32_t ud = (uint32_t)d;
+    uint32_t absD = (d < 0) ? -ud : ud;
+    uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(absD);
+    // check if exactly one bit is set,
+    // don't care if absD is 0 since that's divide by zero
+    if ((absD & (absD - 1)) == 0) {
+        // Branchfree and normal paths are exactly the same
+        result.magic = 0;
+        result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0);
+    } else {
+        LIBDIVIDE_ASSERT(floor_log_2_d >= 1);
+
+        uint8_t more;
+        // the dividend here is 2**(floor_log_2_d + 31), so the low 32 bit word
+        // is 0 and the high word is floor_log_2_d - 1
+        uint32_t rem, proposed_m;
+        proposed_m = libdivide_64_div_32_to_32(1U << (floor_log_2_d - 1), 0, absD, &rem);
+        const uint32_t e = absD - rem;
+
+        // We are going to start with a power of floor_log_2_d - 1.
+        // This works if works if e < 2**floor_log_2_d.
+        if (!branchfree && e < (1U << floor_log_2_d)) {
+            // This power works
+            more = floor_log_2_d - 1;
+        } else {
+            // We need to go one higher. This should not make proposed_m
+            // overflow, but it will make it negative when interpreted as an
+            // int32_t.
+            proposed_m += proposed_m;
+            const uint32_t twice_rem = rem + rem;
+            if (twice_rem >= absD || twice_rem < rem) proposed_m += 1;
+            more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
+        }
+
+        proposed_m += 1;
+        int32_t magic = (int32_t)proposed_m;
+
+        // Mark if we are negative. Note we only negate the magic number in the
+        // branchfull case.
+        if (d < 0) {
+            more |= LIBDIVIDE_NEGATIVE_DIVISOR;
+            if (!branchfree) {
+                magic = -magic;
+            }
+        }
+
+        result.more = more;
+        result.magic = magic;
+    }
+    return result;
+}
+
+struct libdivide_s32_t libdivide_s32_gen(int32_t d) {
+    return libdivide_internal_s32_gen(d, 0);
+}
+
+struct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d) {
+    struct libdivide_s32_t tmp = libdivide_internal_s32_gen(d, 1);
+    struct libdivide_s32_branchfree_t result = {tmp.magic, tmp.more};
+    return result;
+}
+
+int32_t libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+
+    if (!denom->magic) {
+        uint32_t sign = (int8_t)more >> 7;
+        uint32_t mask = (1U << shift) - 1;
+        uint32_t uq = numer + ((numer >> 31) & mask);
+        int32_t q = (int32_t)uq;
+        q >>= shift;
+        q = (q ^ sign) - sign;
+        return q;
+    } else {
+        uint32_t uq = (uint32_t)libdivide_mullhi_s32(denom->magic, numer);
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // must be arithmetic shift and then sign extend
+            int32_t sign = (int8_t)more >> 7;
+            // q += (more < 0 ? -numer : numer)
+            // cast required to avoid UB
+            uq += ((uint32_t)numer ^ sign) - sign;
+        }
+        int32_t q = (int32_t)uq;
+        q >>= shift;
+        q += (q < 0);
+        return q;
+    }
+}
+
+int32_t libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+    // must be arithmetic shift and then sign extend
+    int32_t sign = (int8_t)more >> 7;
+    int32_t magic = denom->magic;
+    int32_t q = libdivide_mullhi_s32(magic, numer);
+    q += numer;
+
+    // If q is non-negative, we have nothing to do
+    // If q is negative, we want to add either (2**shift)-1 if d is a power of
+    // 2, or (2**shift) if it is not a power of 2
+    uint32_t is_power_of_2 = (magic == 0);
+    uint32_t q_sign = (uint32_t)(q >> 31);
+    q += q_sign & ((1U << shift) - is_power_of_2);
+
+    // Now arithmetic right shift
+    q >>= shift;
+    // Negate if needed
+    q = (q ^ sign) - sign;
+
+    return q;
+}
+
+int32_t libdivide_s32_recover(const struct libdivide_s32_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+    if (!denom->magic) {
+        uint32_t absD = 1U << shift;
+        if (more & LIBDIVIDE_NEGATIVE_DIVISOR) {
+            absD = -absD;
+        }
+        return (int32_t)absD;
+    } else {
+        // Unsigned math is much easier
+        // We negate the magic number only in the branchfull case, and we don't
+        // know which case we're in. However we have enough information to
+        // determine the correct sign of the magic number. The divisor was
+        // negative if LIBDIVIDE_NEGATIVE_DIVISOR is set. If ADD_MARKER is set,
+        // the magic number's sign is opposite that of the divisor.
+        // We want to compute the positive magic number.
+        int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR);
+        int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER)
+            ? denom->magic > 0 : denom->magic < 0;
+
+        // Handle the power of 2 case (including branchfree)
+        if (denom->magic == 0) {
+            int32_t result = 1U << shift;
+            return negative_divisor ? -result : result;
+        }
+
+        uint32_t d = (uint32_t)(magic_was_negated ? -denom->magic : denom->magic);
+        uint64_t n = 1ULL << (32 + shift); // this shift cannot exceed 30
+        uint32_t q = (uint32_t)(n / d);
+        int32_t result = (int32_t)q;
+        result += 1;
+        return negative_divisor ? -result : result;
+    }
+}
+
+int32_t libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom) {
+    return libdivide_s32_recover((const struct libdivide_s32_t *)denom);
+}
+
+///////////// SINT64
+
+static inline struct libdivide_s64_t libdivide_internal_s64_gen(int64_t d, int branchfree) {
+    if (d == 0) {
+        LIBDIVIDE_ERROR("divider must be != 0");
+    }
+
+    struct libdivide_s64_t result;
+
+    // If d is a power of 2, or negative a power of 2, we have to use a shift.
+    // This is especially important because the magic algorithm fails for -1.
+    // To check if d is a power of 2 or its inverse, it suffices to check
+    // whether its absolute value has exactly one bit set.  This works even for
+    // INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set
+    // and is a power of 2.
+    uint64_t ud = (uint64_t)d;
+    uint64_t absD = (d < 0) ? -ud : ud;
+    uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(absD);
+    // check if exactly one bit is set,
+    // don't care if absD is 0 since that's divide by zero
+    if ((absD & (absD - 1)) == 0) {
+        // Branchfree and non-branchfree cases are the same
+        result.magic = 0;
+        result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0);
+    } else {
+        // the dividend here is 2**(floor_log_2_d + 63), so the low 64 bit word
+        // is 0 and the high word is floor_log_2_d - 1
+        uint8_t more;
+        uint64_t rem, proposed_m;
+        proposed_m = libdivide_128_div_64_to_64(1ULL << (floor_log_2_d - 1), 0, absD, &rem);
+        const uint64_t e = absD - rem;
+
+        // We are going to start with a power of floor_log_2_d - 1.
+        // This works if works if e < 2**floor_log_2_d.
+        if (!branchfree && e < (1ULL << floor_log_2_d)) {
+            // This power works
+            more = floor_log_2_d - 1;
+        } else {
+            // We need to go one higher. This should not make proposed_m
+            // overflow, but it will make it negative when interpreted as an
+            // int32_t.
+            proposed_m += proposed_m;
+            const uint64_t twice_rem = rem + rem;
+            if (twice_rem >= absD || twice_rem < rem) proposed_m += 1;
+            // note that we only set the LIBDIVIDE_NEGATIVE_DIVISOR bit if we
+            // also set ADD_MARKER this is an annoying optimization that
+            // enables algorithm #4 to avoid the mask. However we always set it
+            // in the branchfree case
+            more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;
+        }
+        proposed_m += 1;
+        int64_t magic = (int64_t)proposed_m;
+
+        // Mark if we are negative
+        if (d < 0) {
+            more |= LIBDIVIDE_NEGATIVE_DIVISOR;
+            if (!branchfree) {
+                magic = -magic;
+            }
+        }
+
+        result.more = more;
+        result.magic = magic;
+    }
+    return result;
+}
+
+struct libdivide_s64_t libdivide_s64_gen(int64_t d) {
+    return libdivide_internal_s64_gen(d, 0);
+}
+
+struct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d) {
+    struct libdivide_s64_t tmp = libdivide_internal_s64_gen(d, 1);
+    struct libdivide_s64_branchfree_t ret = {tmp.magic, tmp.more};
+    return ret;
+}
+
+int64_t libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+
+    if (!denom->magic) { // shift path
+        uint64_t mask = (1ULL << shift) - 1;
+        uint64_t uq = numer + ((numer >> 63) & mask);
+        int64_t q = (int64_t)uq;
+        q >>= shift;
+        // must be arithmetic shift and then sign-extend
+        int64_t sign = (int8_t)more >> 7;
+        q = (q ^ sign) - sign;
+        return q;
+    } else {
+        uint64_t uq = (uint64_t)libdivide_mullhi_s64(denom->magic, numer);
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // must be arithmetic shift and then sign extend
+            int64_t sign = (int8_t)more >> 7;
+            // q += (more < 0 ? -numer : numer)
+            // cast required to avoid UB
+            uq += ((uint64_t)numer ^ sign) - sign;
+        }
+        int64_t q = (int64_t)uq;
+        q >>= shift;
+        q += (q < 0);
+        return q;
+    }
+}
+
+int64_t libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+    // must be arithmetic shift and then sign extend
+    int64_t sign = (int8_t)more >> 7;
+    int64_t magic = denom->magic;
+    int64_t q = libdivide_mullhi_s64(magic, numer);
+    q += numer;
+
+    // If q is non-negative, we have nothing to do.
+    // If q is negative, we want to add either (2**shift)-1 if d is a power of
+    // 2, or (2**shift) if it is not a power of 2.
+    uint64_t is_power_of_2 = (magic == 0);
+    uint64_t q_sign = (uint64_t)(q >> 63);
+    q += q_sign & ((1ULL << shift) - is_power_of_2);
+
+    // Arithmetic right shift
+    q >>= shift;
+    // Negate if needed
+    q = (q ^ sign) - sign;
+
+    return q;
+}
+
+int64_t libdivide_s64_recover(const struct libdivide_s64_t *denom) {
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+    if (denom->magic == 0) { // shift path
+        uint64_t absD = 1ULL << shift;
+        if (more & LIBDIVIDE_NEGATIVE_DIVISOR) {
+            absD = -absD;
+        }
+        return (int64_t)absD;
+    } else {
+        // Unsigned math is much easier
+        int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR);
+        int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER)
+            ? denom->magic > 0 : denom->magic < 0;
+
+        uint64_t d = (uint64_t)(magic_was_negated ? -denom->magic : denom->magic);
+        uint64_t n_hi = 1ULL << shift, n_lo = 0;
+        uint64_t rem_ignored;
+        uint64_t q = libdivide_128_div_64_to_64(n_hi, n_lo, d, &rem_ignored);
+        int64_t result = (int64_t)(q + 1);
+        if (negative_divisor) {
+            result = -result;
+        }
+        return result;
+    }
+}
+
+int64_t libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom) {
+    return libdivide_s64_recover((const struct libdivide_s64_t *)denom);
+}
+
+#if defined(LIBDIVIDE_AVX512)
+
+static inline __m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom);
+static inline __m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom);
+static inline __m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom);
+static inline __m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom);
+
+static inline __m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom);
+static inline __m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom);
+static inline __m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom);
+static inline __m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom);
+
+//////// Internal Utility Functions
+
+static inline __m512i libdivide_s64_signbits(__m512i v) {;
+    return _mm512_srai_epi64(v, 63);
+}
+
+static inline __m512i libdivide_s64_shift_right_vector(__m512i v, int amt) {
+    return _mm512_srai_epi64(v, amt);
+}
+
+// Here, b is assumed to contain one 32-bit value repeated.
+static inline __m512i libdivide_mullhi_u32_vector(__m512i a, __m512i b) {
+    __m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epu32(a, b), 32);
+    __m512i a1X3X = _mm512_srli_epi64(a, 32);
+    __m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0);
+    __m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epu32(a1X3X, b), mask);
+    return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3);
+}
+
+// b is one 32-bit value repeated.
+static inline __m512i libdivide_mullhi_s32_vector(__m512i a, __m512i b) {
+    __m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epi32(a, b), 32);
+    __m512i a1X3X = _mm512_srli_epi64(a, 32);
+    __m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0);
+    __m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epi32(a1X3X, b), mask);
+    return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3);
+}
+
+// Here, y is assumed to contain one 64-bit value repeated.
+// https://stackoverflow.com/a/28827013
+static inline __m512i libdivide_mullhi_u64_vector(__m512i x, __m512i y) {
+    __m512i lomask = _mm512_set1_epi64(0xffffffff);
+    __m512i xh = _mm512_shuffle_epi32(x, (_MM_PERM_ENUM) 0xB1);
+    __m512i yh = _mm512_shuffle_epi32(y, (_MM_PERM_ENUM) 0xB1);
+    __m512i w0 = _mm512_mul_epu32(x, y);
+    __m512i w1 = _mm512_mul_epu32(x, yh);
+    __m512i w2 = _mm512_mul_epu32(xh, y);
+    __m512i w3 = _mm512_mul_epu32(xh, yh);
+    __m512i w0h = _mm512_srli_epi64(w0, 32);
+    __m512i s1 = _mm512_add_epi64(w1, w0h);
+    __m512i s1l = _mm512_and_si512(s1, lomask);
+    __m512i s1h = _mm512_srli_epi64(s1, 32);
+    __m512i s2 = _mm512_add_epi64(w2, s1l);
+    __m512i s2h = _mm512_srli_epi64(s2, 32);
+    __m512i hi = _mm512_add_epi64(w3, s1h);
+            hi = _mm512_add_epi64(hi, s2h);
+
+    return hi;
+}
+
+// y is one 64-bit value repeated.
+static inline __m512i libdivide_mullhi_s64_vector(__m512i x, __m512i y) {
+    __m512i p = libdivide_mullhi_u64_vector(x, y);
+    __m512i t1 = _mm512_and_si512(libdivide_s64_signbits(x), y);
+    __m512i t2 = _mm512_and_si512(libdivide_s64_signbits(y), x);
+    p = _mm512_sub_epi64(p, t1);
+    p = _mm512_sub_epi64(p, t2);
+    return p;
+}
+
+////////// UINT32
+
+__m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return _mm512_srli_epi32(numers, more);
+    }
+    else {
+        __m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // uint32_t t = ((numer - q) >> 1) + q;
+            // return t >> denom->shift;
+            uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+            __m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q);
+            return _mm512_srli_epi32(t, shift);
+        }
+        else {
+            return _mm512_srli_epi32(q, more);
+        }
+    }
+}
+
+__m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom) {
+    __m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic));
+    __m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q);
+    return _mm512_srli_epi32(t, denom->more);
+}
+
+////////// UINT64
+
+__m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return _mm512_srli_epi64(numers, more);
+    }
+    else {
+        __m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // uint32_t t = ((numer - q) >> 1) + q;
+            // return t >> denom->shift;
+            uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+            __m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q);
+            return _mm512_srli_epi64(t, shift);
+        }
+        else {
+            return _mm512_srli_epi64(q, more);
+        }
+    }
+}
+
+__m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom) {
+    __m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic));
+    __m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q);
+    return _mm512_srli_epi64(t, denom->more);
+}
+
+////////// SINT32
+
+__m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+        uint32_t mask = (1U << shift) - 1;
+        __m512i roundToZeroTweak = _mm512_set1_epi32(mask);
+        // q = numer + ((numer >> 31) & roundToZeroTweak);
+        __m512i q = _mm512_add_epi32(numers, _mm512_and_si512(_mm512_srai_epi32(numers, 31), roundToZeroTweak));
+        q = _mm512_srai_epi32(q, shift);
+        __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
+        // q = (q ^ sign) - sign;
+        q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign);
+        return q;
+    }
+    else {
+        __m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+             // must be arithmetic shift
+            __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
+             // q += ((numer ^ sign) - sign);
+            q = _mm512_add_epi32(q, _mm512_sub_epi32(_mm512_xor_si512(numers, sign), sign));
+        }
+        // q >>= shift
+        q = _mm512_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);
+        q = _mm512_add_epi32(q, _mm512_srli_epi32(q, 31)); // q += (q < 0)
+        return q;
+    }
+}
+
+__m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom) {
+    int32_t magic = denom->magic;
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+     // must be arithmetic shift
+    __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
+    __m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(magic));
+    q = _mm512_add_epi32(q, numers); // q += numers
+
+    // If q is non-negative, we have nothing to do
+    // If q is negative, we want to add either (2**shift)-1 if d is
+    // a power of 2, or (2**shift) if it is not a power of 2
+    uint32_t is_power_of_2 = (magic == 0);
+    __m512i q_sign = _mm512_srai_epi32(q, 31); // q_sign = q >> 31
+    __m512i mask = _mm512_set1_epi32((1U << shift) - is_power_of_2);
+    q = _mm512_add_epi32(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask)
+    q = _mm512_srai_epi32(q, shift); // q >>= shift
+    q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign
+    return q;
+}
+
+////////// SINT64
+
+__m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom) {
+    uint8_t more = denom->more;
+    int64_t magic = denom->magic;
+    if (magic == 0) { // shift path
+        uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+        uint64_t mask = (1ULL << shift) - 1;
+        __m512i roundToZeroTweak = _mm512_set1_epi64(mask);
+        // q = numer + ((numer >> 63) & roundToZeroTweak);
+        __m512i q = _mm512_add_epi64(numers, _mm512_and_si512(libdivide_s64_signbits(numers), roundToZeroTweak));
+        q = libdivide_s64_shift_right_vector(q, shift);
+        __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
+         // q = (q ^ sign) - sign;
+        q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign);
+        return q;
+    }
+    else {
+        __m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // must be arithmetic shift
+            __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
+            // q += ((numer ^ sign) - sign);
+            q = _mm512_add_epi64(q, _mm512_sub_epi64(_mm512_xor_si512(numers, sign), sign));
+        }
+        // q >>= denom->mult_path.shift
+        q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);
+        q = _mm512_add_epi64(q, _mm512_srli_epi64(q, 63)); // q += (q < 0)
+        return q;
+    }
+}
+
+__m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom) {
+    int64_t magic = denom->magic;
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+    // must be arithmetic shift
+    __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);
+
+     // libdivide_mullhi_s64(numers, magic);
+    __m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic));
+    q = _mm512_add_epi64(q, numers); // q += numers
+
+    // If q is non-negative, we have nothing to do.
+    // If q is negative, we want to add either (2**shift)-1 if d is
+    // a power of 2, or (2**shift) if it is not a power of 2.
+    uint32_t is_power_of_2 = (magic == 0);
+    __m512i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63
+    __m512i mask = _mm512_set1_epi64((1ULL << shift) - is_power_of_2);
+    q = _mm512_add_epi64(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask)
+    q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift
+    q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign
+    return q;
+}
+
+#elif defined(LIBDIVIDE_AVX2)
+
+static inline __m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom);
+static inline __m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom);
+static inline __m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom);
+static inline __m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom);
+
+static inline __m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom);
+static inline __m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom);
+static inline __m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom);
+static inline __m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom);
+
+//////// Internal Utility Functions
+
+// Implementation of _mm256_srai_epi64(v, 63) (from AVX512).
+static inline __m256i libdivide_s64_signbits(__m256i v) {
+    __m256i hiBitsDuped = _mm256_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1));
+    __m256i signBits = _mm256_srai_epi32(hiBitsDuped, 31);
+    return signBits;
+}
+
+// Implementation of _mm256_srai_epi64 (from AVX512).
+static inline __m256i libdivide_s64_shift_right_vector(__m256i v, int amt) {
+    const int b = 64 - amt;
+    __m256i m = _mm256_set1_epi64x(1ULL << (b - 1));
+    __m256i x = _mm256_srli_epi64(v, amt);
+    __m256i result = _mm256_sub_epi64(_mm256_xor_si256(x, m), m);
+    return result;
+}
+
+// Here, b is assumed to contain one 32-bit value repeated.
+static inline __m256i libdivide_mullhi_u32_vector(__m256i a, __m256i b) {
+    __m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epu32(a, b), 32);
+    __m256i a1X3X = _mm256_srli_epi64(a, 32);
+    __m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0);
+    __m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epu32(a1X3X, b), mask);
+    return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3);
+}
+
+// b is one 32-bit value repeated.
+static inline __m256i libdivide_mullhi_s32_vector(__m256i a, __m256i b) {
+    __m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epi32(a, b), 32);
+    __m256i a1X3X = _mm256_srli_epi64(a, 32);
+    __m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0);
+    __m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epi32(a1X3X, b), mask);
+    return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3);
+}
+
+// Here, y is assumed to contain one 64-bit value repeated.
+// https://stackoverflow.com/a/28827013
+static inline __m256i libdivide_mullhi_u64_vector(__m256i x, __m256i y) {
+    __m256i lomask = _mm256_set1_epi64x(0xffffffff);
+    __m256i xh = _mm256_shuffle_epi32(x, 0xB1);        // x0l, x0h, x1l, x1h
+    __m256i yh = _mm256_shuffle_epi32(y, 0xB1);        // y0l, y0h, y1l, y1h
+    __m256i w0 = _mm256_mul_epu32(x, y);               // x0l*y0l, x1l*y1l
+    __m256i w1 = _mm256_mul_epu32(x, yh);              // x0l*y0h, x1l*y1h
+    __m256i w2 = _mm256_mul_epu32(xh, y);              // x0h*y0l, x1h*y0l
+    __m256i w3 = _mm256_mul_epu32(xh, yh);             // x0h*y0h, x1h*y1h
+    __m256i w0h = _mm256_srli_epi64(w0, 32);
+    __m256i s1 = _mm256_add_epi64(w1, w0h);
+    __m256i s1l = _mm256_and_si256(s1, lomask);
+    __m256i s1h = _mm256_srli_epi64(s1, 32);
+    __m256i s2 = _mm256_add_epi64(w2, s1l);
+    __m256i s2h = _mm256_srli_epi64(s2, 32);
+    __m256i hi = _mm256_add_epi64(w3, s1h);
+            hi = _mm256_add_epi64(hi, s2h);
+
+    return hi;
+}
+
+// y is one 64-bit value repeated.
+static inline __m256i libdivide_mullhi_s64_vector(__m256i x, __m256i y) {
+    __m256i p = libdivide_mullhi_u64_vector(x, y);
+    __m256i t1 = _mm256_and_si256(libdivide_s64_signbits(x), y);
+    __m256i t2 = _mm256_and_si256(libdivide_s64_signbits(y), x);
+    p = _mm256_sub_epi64(p, t1);
+    p = _mm256_sub_epi64(p, t2);
+    return p;
+}
+
+////////// UINT32
+
+__m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return _mm256_srli_epi32(numers, more);
+    }
+    else {
+        __m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // uint32_t t = ((numer - q) >> 1) + q;
+            // return t >> denom->shift;
+            uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+            __m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q);
+            return _mm256_srli_epi32(t, shift);
+        }
+        else {
+            return _mm256_srli_epi32(q, more);
+        }
+    }
+}
+
+__m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom) {
+    __m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic));
+    __m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q);
+    return _mm256_srli_epi32(t, denom->more);
+}
+
+////////// UINT64
+
+__m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return _mm256_srli_epi64(numers, more);
+    }
+    else {
+        __m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // uint32_t t = ((numer - q) >> 1) + q;
+            // return t >> denom->shift;
+            uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+            __m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q);
+            return _mm256_srli_epi64(t, shift);
+        }
+        else {
+            return _mm256_srli_epi64(q, more);
+        }
+    }
+}
+
+__m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom) {
+    __m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic));
+    __m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q);
+    return _mm256_srli_epi64(t, denom->more);
+}
+
+////////// SINT32
+
+__m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+        uint32_t mask = (1U << shift) - 1;
+        __m256i roundToZeroTweak = _mm256_set1_epi32(mask);
+        // q = numer + ((numer >> 31) & roundToZeroTweak);
+        __m256i q = _mm256_add_epi32(numers, _mm256_and_si256(_mm256_srai_epi32(numers, 31), roundToZeroTweak));
+        q = _mm256_srai_epi32(q, shift);
+        __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
+        // q = (q ^ sign) - sign;
+        q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign);
+        return q;
+    }
+    else {
+        __m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+             // must be arithmetic shift
+            __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
+             // q += ((numer ^ sign) - sign);
+            q = _mm256_add_epi32(q, _mm256_sub_epi32(_mm256_xor_si256(numers, sign), sign));
+        }
+        // q >>= shift
+        q = _mm256_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);
+        q = _mm256_add_epi32(q, _mm256_srli_epi32(q, 31)); // q += (q < 0)
+        return q;
+    }
+}
+
+__m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom) {
+    int32_t magic = denom->magic;
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+     // must be arithmetic shift
+    __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
+    __m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(magic));
+    q = _mm256_add_epi32(q, numers); // q += numers
+
+    // If q is non-negative, we have nothing to do
+    // If q is negative, we want to add either (2**shift)-1 if d is
+    // a power of 2, or (2**shift) if it is not a power of 2
+    uint32_t is_power_of_2 = (magic == 0);
+    __m256i q_sign = _mm256_srai_epi32(q, 31); // q_sign = q >> 31
+    __m256i mask = _mm256_set1_epi32((1U << shift) - is_power_of_2);
+    q = _mm256_add_epi32(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask)
+    q = _mm256_srai_epi32(q, shift); // q >>= shift
+    q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign
+    return q;
+}
+
+////////// SINT64
+
+__m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom) {
+    uint8_t more = denom->more;
+    int64_t magic = denom->magic;
+    if (magic == 0) { // shift path
+        uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+        uint64_t mask = (1ULL << shift) - 1;
+        __m256i roundToZeroTweak = _mm256_set1_epi64x(mask);
+        // q = numer + ((numer >> 63) & roundToZeroTweak);
+        __m256i q = _mm256_add_epi64(numers, _mm256_and_si256(libdivide_s64_signbits(numers), roundToZeroTweak));
+        q = libdivide_s64_shift_right_vector(q, shift);
+        __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
+         // q = (q ^ sign) - sign;
+        q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign);
+        return q;
+    }
+    else {
+        __m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // must be arithmetic shift
+            __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
+            // q += ((numer ^ sign) - sign);
+            q = _mm256_add_epi64(q, _mm256_sub_epi64(_mm256_xor_si256(numers, sign), sign));
+        }
+        // q >>= denom->mult_path.shift
+        q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);
+        q = _mm256_add_epi64(q, _mm256_srli_epi64(q, 63)); // q += (q < 0)
+        return q;
+    }
+}
+
+__m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom) {
+    int64_t magic = denom->magic;
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+    // must be arithmetic shift
+    __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);
+
+     // libdivide_mullhi_s64(numers, magic);
+    __m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic));
+    q = _mm256_add_epi64(q, numers); // q += numers
+
+    // If q is non-negative, we have nothing to do.
+    // If q is negative, we want to add either (2**shift)-1 if d is
+    // a power of 2, or (2**shift) if it is not a power of 2.
+    uint32_t is_power_of_2 = (magic == 0);
+    __m256i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63
+    __m256i mask = _mm256_set1_epi64x((1ULL << shift) - is_power_of_2);
+    q = _mm256_add_epi64(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask)
+    q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift
+    q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign
+    return q;
+}
+
+#elif defined(LIBDIVIDE_SSE2)
+
+static inline __m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom);
+static inline __m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom);
+static inline __m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom);
+static inline __m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom);
+
+static inline __m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom);
+static inline __m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom);
+static inline __m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom);
+static inline __m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom);
+
+//////// Internal Utility Functions
+
+// Implementation of _mm_srai_epi64(v, 63) (from AVX512).
+static inline __m128i libdivide_s64_signbits(__m128i v) {
+    __m128i hiBitsDuped = _mm_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1));
+    __m128i signBits = _mm_srai_epi32(hiBitsDuped, 31);
+    return signBits;
+}
+
+// Implementation of _mm_srai_epi64 (from AVX512).
+static inline __m128i libdivide_s64_shift_right_vector(__m128i v, int amt) {
+    const int b = 64 - amt;
+    __m128i m = _mm_set1_epi64x(1ULL << (b - 1));
+    __m128i x = _mm_srli_epi64(v, amt);
+    __m128i result = _mm_sub_epi64(_mm_xor_si128(x, m), m);
+    return result;
+}
+
+// Here, b is assumed to contain one 32-bit value repeated.
+static inline __m128i libdivide_mullhi_u32_vector(__m128i a, __m128i b) {
+    __m128i hi_product_0Z2Z = _mm_srli_epi64(_mm_mul_epu32(a, b), 32);
+    __m128i a1X3X = _mm_srli_epi64(a, 32);
+    __m128i mask = _mm_set_epi32(-1, 0, -1, 0);
+    __m128i hi_product_Z1Z3 = _mm_and_si128(_mm_mul_epu32(a1X3X, b), mask);
+    return _mm_or_si128(hi_product_0Z2Z, hi_product_Z1Z3);
+}
+
+// SSE2 does not have a signed multiplication instruction, but we can convert
+// unsigned to signed pretty efficiently. Again, b is just a 32 bit value
+// repeated four times.
+static inline __m128i libdivide_mullhi_s32_vector(__m128i a, __m128i b) {
+    __m128i p = libdivide_mullhi_u32_vector(a, b);
+    // t1 = (a >> 31) & y, arithmetic shift
+    __m128i t1 = _mm_and_si128(_mm_srai_epi32(a, 31), b);
+    __m128i t2 = _mm_and_si128(_mm_srai_epi32(b, 31), a);
+    p = _mm_sub_epi32(p, t1);
+    p = _mm_sub_epi32(p, t2);
+    return p;
+}
+
+// Here, y is assumed to contain one 64-bit value repeated.
+// https://stackoverflow.com/a/28827013
+static inline __m128i libdivide_mullhi_u64_vector(__m128i x, __m128i y) {
+    __m128i lomask = _mm_set1_epi64x(0xffffffff);
+    __m128i xh = _mm_shuffle_epi32(x, 0xB1);        // x0l, x0h, x1l, x1h
+    __m128i yh = _mm_shuffle_epi32(y, 0xB1);        // y0l, y0h, y1l, y1h
+    __m128i w0 = _mm_mul_epu32(x, y);               // x0l*y0l, x1l*y1l
+    __m128i w1 = _mm_mul_epu32(x, yh);              // x0l*y0h, x1l*y1h
+    __m128i w2 = _mm_mul_epu32(xh, y);              // x0h*y0l, x1h*y0l
+    __m128i w3 = _mm_mul_epu32(xh, yh);             // x0h*y0h, x1h*y1h
+    __m128i w0h = _mm_srli_epi64(w0, 32);
+    __m128i s1 = _mm_add_epi64(w1, w0h);
+    __m128i s1l = _mm_and_si128(s1, lomask);
+    __m128i s1h = _mm_srli_epi64(s1, 32);
+    __m128i s2 = _mm_add_epi64(w2, s1l);
+    __m128i s2h = _mm_srli_epi64(s2, 32);
+    __m128i hi = _mm_add_epi64(w3, s1h);
+            hi = _mm_add_epi64(hi, s2h);
+
+    return hi;
+}
+
+// y is one 64-bit value repeated.
+static inline __m128i libdivide_mullhi_s64_vector(__m128i x, __m128i y) {
+    __m128i p = libdivide_mullhi_u64_vector(x, y);
+    __m128i t1 = _mm_and_si128(libdivide_s64_signbits(x), y);
+    __m128i t2 = _mm_and_si128(libdivide_s64_signbits(y), x);
+    p = _mm_sub_epi64(p, t1);
+    p = _mm_sub_epi64(p, t2);
+    return p;
+}
+
+////////// UINT32
+
+__m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return _mm_srli_epi32(numers, more);
+    }
+    else {
+        __m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // uint32_t t = ((numer - q) >> 1) + q;
+            // return t >> denom->shift;
+            uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+            __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q);
+            return _mm_srli_epi32(t, shift);
+        }
+        else {
+            return _mm_srli_epi32(q, more);
+        }
+    }
+}
+
+__m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom) {
+    __m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic));
+    __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q);
+    return _mm_srli_epi32(t, denom->more);
+}
+
+////////// UINT64
+
+__m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        return _mm_srli_epi64(numers, more);
+    }
+    else {
+        __m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // uint32_t t = ((numer - q) >> 1) + q;
+            // return t >> denom->shift;
+            uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+            __m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q);
+            return _mm_srli_epi64(t, shift);
+        }
+        else {
+            return _mm_srli_epi64(q, more);
+        }
+    }
+}
+
+__m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom) {
+    __m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic));
+    __m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q);
+    return _mm_srli_epi64(t, denom->more);
+}
+
+////////// SINT32
+
+__m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom) {
+    uint8_t more = denom->more;
+    if (!denom->magic) {
+        uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+        uint32_t mask = (1U << shift) - 1;
+        __m128i roundToZeroTweak = _mm_set1_epi32(mask);
+        // q = numer + ((numer >> 31) & roundToZeroTweak);
+        __m128i q = _mm_add_epi32(numers, _mm_and_si128(_mm_srai_epi32(numers, 31), roundToZeroTweak));
+        q = _mm_srai_epi32(q, shift);
+        __m128i sign = _mm_set1_epi32((int8_t)more >> 7);
+        // q = (q ^ sign) - sign;
+        q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign);
+        return q;
+    }
+    else {
+        __m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(denom->magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+             // must be arithmetic shift
+            __m128i sign = _mm_set1_epi32((int8_t)more >> 7);
+             // q += ((numer ^ sign) - sign);
+            q = _mm_add_epi32(q, _mm_sub_epi32(_mm_xor_si128(numers, sign), sign));
+        }
+        // q >>= shift
+        q = _mm_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);
+        q = _mm_add_epi32(q, _mm_srli_epi32(q, 31)); // q += (q < 0)
+        return q;
+    }
+}
+
+__m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom) {
+    int32_t magic = denom->magic;
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;
+     // must be arithmetic shift
+    __m128i sign = _mm_set1_epi32((int8_t)more >> 7);
+    __m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(magic));
+    q = _mm_add_epi32(q, numers); // q += numers
+
+    // If q is non-negative, we have nothing to do
+    // If q is negative, we want to add either (2**shift)-1 if d is
+    // a power of 2, or (2**shift) if it is not a power of 2
+    uint32_t is_power_of_2 = (magic == 0);
+    __m128i q_sign = _mm_srai_epi32(q, 31); // q_sign = q >> 31
+    __m128i mask = _mm_set1_epi32((1U << shift) - is_power_of_2);
+    q = _mm_add_epi32(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask)
+    q = _mm_srai_epi32(q, shift); // q >>= shift
+    q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign
+    return q;
+}
+
+////////// SINT64
+
+__m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom) {
+    uint8_t more = denom->more;
+    int64_t magic = denom->magic;
+    if (magic == 0) { // shift path
+        uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+        uint64_t mask = (1ULL << shift) - 1;
+        __m128i roundToZeroTweak = _mm_set1_epi64x(mask);
+        // q = numer + ((numer >> 63) & roundToZeroTweak);
+        __m128i q = _mm_add_epi64(numers, _mm_and_si128(libdivide_s64_signbits(numers), roundToZeroTweak));
+        q = libdivide_s64_shift_right_vector(q, shift);
+        __m128i sign = _mm_set1_epi32((int8_t)more >> 7);
+         // q = (q ^ sign) - sign;
+        q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign);
+        return q;
+    }
+    else {
+        __m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic));
+        if (more & LIBDIVIDE_ADD_MARKER) {
+            // must be arithmetic shift
+            __m128i sign = _mm_set1_epi32((int8_t)more >> 7);
+            // q += ((numer ^ sign) - sign);
+            q = _mm_add_epi64(q, _mm_sub_epi64(_mm_xor_si128(numers, sign), sign));
+        }
+        // q >>= denom->mult_path.shift
+        q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);
+        q = _mm_add_epi64(q, _mm_srli_epi64(q, 63)); // q += (q < 0)
+        return q;
+    }
+}
+
+__m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom) {
+    int64_t magic = denom->magic;
+    uint8_t more = denom->more;
+    uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;
+    // must be arithmetic shift
+    __m128i sign = _mm_set1_epi32((int8_t)more >> 7);
+
+     // libdivide_mullhi_s64(numers, magic);
+    __m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic));
+    q = _mm_add_epi64(q, numers); // q += numers
+
+    // If q is non-negative, we have nothing to do.
+    // If q is negative, we want to add either (2**shift)-1 if d is
+    // a power of 2, or (2**shift) if it is not a power of 2.
+    uint32_t is_power_of_2 = (magic == 0);
+    __m128i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63
+    __m128i mask = _mm_set1_epi64x((1ULL << shift) - is_power_of_2);
+    q = _mm_add_epi64(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask)
+    q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift
+    q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign
+    return q;
+}
+
+#endif
+
+/////////// C++ stuff
+
+#ifdef __cplusplus
+
+// The C++ divider class is templated on both an integer type
+// (like uint64_t) and an algorithm type.
+// * BRANCHFULL is the default algorithm type.
+// * BRANCHFREE is the branchfree algorithm type.
+enum {
+    BRANCHFULL,
+    BRANCHFREE
+};
+
+#if defined(LIBDIVIDE_AVX512)
+    #define LIBDIVIDE_VECTOR_TYPE __m512i
+#elif defined(LIBDIVIDE_AVX2)
+    #define LIBDIVIDE_VECTOR_TYPE __m256i
+#elif defined(LIBDIVIDE_SSE2)
+    #define LIBDIVIDE_VECTOR_TYPE __m128i
+#endif
+
+#if !defined(LIBDIVIDE_VECTOR_TYPE)
+    #define LIBDIVIDE_DIVIDE_VECTOR(ALGO)
+#else
+    #define LIBDIVIDE_DIVIDE_VECTOR(ALGO) \
+        LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const { \
+            return libdivide_##ALGO##_do_vector(n, &denom); \
+        }
+#endif
+
+// The DISPATCHER_GEN() macro generates C++ methods (for the given integer
+// and algorithm types) that redirect to libdivide's C API.
+#define DISPATCHER_GEN(T, ALGO) \
+    libdivide_##ALGO##_t denom; \
+    dispatcher() { } \
+    dispatcher(T d) \
+        : denom(libdivide_##ALGO##_gen(d)) \
+    { } \
+    T divide(T n) const { \
+        return libdivide_##ALGO##_do(n, &denom); \
+    } \
+    LIBDIVIDE_DIVIDE_VECTOR(ALGO) \
+    T recover() const { \
+        return libdivide_##ALGO##_recover(&denom); \
+    }
+
+// The dispatcher selects a specific division algorithm for a given
+// type and ALGO using partial template specialization.
+template<bool IS_INTEGRAL, bool IS_SIGNED, int SIZEOF, int ALGO> struct dispatcher { };
+
+template<> struct dispatcher<true, true, sizeof(int32_t), BRANCHFULL> { DISPATCHER_GEN(int32_t, s32) };
+template<> struct dispatcher<true, true, sizeof(int32_t), BRANCHFREE> { DISPATCHER_GEN(int32_t, s32_branchfree) };
+template<> struct dispatcher<true, false, sizeof(uint32_t), BRANCHFULL> { DISPATCHER_GEN(uint32_t, u32) };
+template<> struct dispatcher<true, false, sizeof(uint32_t), BRANCHFREE> { DISPATCHER_GEN(uint32_t, u32_branchfree) };
+template<> struct dispatcher<true, true, sizeof(int64_t), BRANCHFULL> { DISPATCHER_GEN(int64_t, s64) };
+template<> struct dispatcher<true, true, sizeof(int64_t), BRANCHFREE> { DISPATCHER_GEN(int64_t, s64_branchfree) };
+template<> struct dispatcher<true, false, sizeof(uint64_t), BRANCHFULL> { DISPATCHER_GEN(uint64_t, u64) };
+template<> struct dispatcher<true, false, sizeof(uint64_t), BRANCHFREE> { DISPATCHER_GEN(uint64_t, u64_branchfree) };
+
+// This is the main divider class for use by the user (C++ API).
+// The actual division algorithm is selected using the dispatcher struct
+// based on the integer and algorithm template parameters.
+template<typename T, int ALGO = BRANCHFULL>
+class divider {
+public:
+    // We leave the default constructor empty so that creating
+    // an array of dividers and then initializing them
+    // later doesn't slow us down.
+    divider() { }
+
+    // Constructor that takes the divisor as a parameter
+    divider(T d) : div(d) { }
+
+    // Divides n by the divisor
+    T divide(T n) const {
+        return div.divide(n);
+    }
+
+    // Recovers the divisor, returns the value that was
+    // used to initialize this divider object.
+    T recover() const {
+        return div.recover();
+    }
+
+    bool operator==(const divider<T, ALGO>& other) const {
+        return div.denom.magic == other.denom.magic &&
+               div.denom.more == other.denom.more;
+    }
+
+    bool operator!=(const divider<T, ALGO>& other) const {
+        return !(*this == other);
+    }
+
+#if defined(LIBDIVIDE_VECTOR_TYPE)
+    // Treats the vector as packed integer values with the same type as
+    // the divider (e.g. s32, u32, s64, u64) and divides each of
+    // them by the divider, returning the packed quotients.
+    LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const {
+        return div.divide(n);
+    }
+#endif
+
+private:
+    // Storage for the actual divisor
+    dispatcher<std::is_integral<T>::value,
+               std::is_signed<T>::value, sizeof(T), ALGO> div;
+};
+
+// Overload of operator / for scalar division
+template<typename T, int ALGO>
+T operator/(T n, const divider<T, ALGO>& div) {
+    return div.divide(n);
+}
+
+// Overload of operator /= for scalar division
+template<typename T, int ALGO>
+T& operator/=(T& n, const divider<T, ALGO>& div) {
+    n = div.divide(n);
+    return n;
+}
+
+#if defined(LIBDIVIDE_VECTOR_TYPE)
+    // Overload of operator / for vector division
+    template<typename T, int ALGO>
+    LIBDIVIDE_VECTOR_TYPE operator/(LIBDIVIDE_VECTOR_TYPE n, const divider<T, ALGO>& div) {
+        return div.divide(n);
+    }
+    // Overload of operator /= for vector division
+    template<typename T, int ALGO>
+    LIBDIVIDE_VECTOR_TYPE& operator/=(LIBDIVIDE_VECTOR_TYPE& n, const divider<T, ALGO>& div) {
+        n = div.divide(n);
+        return n;
+    }
+#endif
+
+// libdivdie::branchfree_divider<T>
+template <typename T>
+using branchfree_divider = divider<T, BRANCHFREE>;
+
+}  // namespace libdivide
+
+#endif  // __cplusplus
+
+#endif  // NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_

pllava/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h

@@ -0,0 +1,359 @@
+#ifndef NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_
+#define NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_
+
+#include <numpy/npy_math.h>
+#include <numpy/npy_common.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/*
+ * The legacy generic inner loop for a standard element-wise or
+ * generalized ufunc.
+ */
+typedef void (*PyUFuncGenericFunction)
+            (char **args,
+             npy_intp const *dimensions,
+             npy_intp const *strides,
+             void *innerloopdata);
+
+/*
+ * The most generic one-dimensional inner loop for
+ * a masked standard element-wise ufunc. "Masked" here means that it skips
+ * doing calculations on any items for which the maskptr array has a true
+ * value.
+ */
+typedef void (PyUFunc_MaskedStridedInnerLoopFunc)(
+                char **dataptrs, npy_intp *strides,
+                char *maskptr, npy_intp mask_stride,
+                npy_intp count,
+                NpyAuxData *innerloopdata);
+
+/* Forward declaration for the type resolver and loop selector typedefs */
+struct _tagPyUFuncObject;
+
+/*
+ * Given the operands for calling a ufunc, should determine the
+ * calculation input and output data types and return an inner loop function.
+ * This function should validate that the casting rule is being followed,
+ * and fail if it is not.
+ *
+ * For backwards compatibility, the regular type resolution function does not
+ * support auxiliary data with object semantics. The type resolution call
+ * which returns a masked generic function returns a standard NpyAuxData
+ * object, for which the NPY_AUXDATA_FREE and NPY_AUXDATA_CLONE macros
+ * work.
+ *
+ * ufunc:             The ufunc object.
+ * casting:           The 'casting' parameter provided to the ufunc.
+ * operands:          An array of length (ufunc->nin + ufunc->nout),
+ *                    with the output parameters possibly NULL.
+ * type_tup:          Either NULL, or the type_tup passed to the ufunc.
+ * out_dtypes:        An array which should be populated with new
+ *                    references to (ufunc->nin + ufunc->nout) new
+ *                    dtypes, one for each input and output. These
+ *                    dtypes should all be in native-endian format.
+ *
+ * Should return 0 on success, -1 on failure (with exception set),
+ * or -2 if Py_NotImplemented should be returned.
+ */
+typedef int (PyUFunc_TypeResolutionFunc)(
+                                struct _tagPyUFuncObject *ufunc,
+                                NPY_CASTING casting,
+                                PyArrayObject **operands,
+                                PyObject *type_tup,
+                                PyArray_Descr **out_dtypes);
+
+/*
+ * Legacy loop selector. (This should NOT normally be used and we can expect
+ * that only the `PyUFunc_DefaultLegacyInnerLoopSelector` is ever set).
+ * However, unlike the masked version, it probably still works.
+ *
+ * ufunc:             The ufunc object.
+ * dtypes:            An array which has been populated with dtypes,
+ *                    in most cases by the type resolution function
+ *                    for the same ufunc.
+ * out_innerloop:     Should be populated with the correct ufunc inner
+ *                    loop for the given type.
+ * out_innerloopdata: Should be populated with the void* data to
+ *                    be passed into the out_innerloop function.
+ * out_needs_api:     If the inner loop needs to use the Python API,
+ *                    should set the to 1, otherwise should leave
+ *                    this untouched.
+ */
+typedef int (PyUFunc_LegacyInnerLoopSelectionFunc)(
+                            struct _tagPyUFuncObject *ufunc,
+                            PyArray_Descr **dtypes,
+                            PyUFuncGenericFunction *out_innerloop,
+                            void **out_innerloopdata,
+                            int *out_needs_api);
+
+
+typedef struct _tagPyUFuncObject {
+        PyObject_HEAD
+        /*
+         * nin: Number of inputs
+         * nout: Number of outputs
+         * nargs: Always nin + nout (Why is it stored?)
+         */
+        int nin, nout, nargs;
+
+        /*
+         * Identity for reduction, any of PyUFunc_One, PyUFunc_Zero
+         * PyUFunc_MinusOne, PyUFunc_None, PyUFunc_ReorderableNone,
+         * PyUFunc_IdentityValue.
+         */
+        int identity;
+
+        /* Array of one-dimensional core loops */
+        PyUFuncGenericFunction *functions;
+        /* Array of funcdata that gets passed into the functions */
+        void **data;
+        /* The number of elements in 'functions' and 'data' */
+        int ntypes;
+
+        /* Used to be unused field 'check_return' */
+        int reserved1;
+
+        /* The name of the ufunc */
+        const char *name;
+
+        /* Array of type numbers, of size ('nargs' * 'ntypes') */
+        char *types;
+
+        /* Documentation string */
+        const char *doc;
+
+        void *ptr;
+        PyObject *obj;
+        PyObject *userloops;
+
+        /* generalized ufunc parameters */
+
+        /* 0 for scalar ufunc; 1 for generalized ufunc */
+        int core_enabled;
+        /* number of distinct dimension names in signature */
+        int core_num_dim_ix;
+
+        /*
+         * dimension indices of input/output argument k are stored in
+         * core_dim_ixs[core_offsets[k]..core_offsets[k]+core_num_dims[k]-1]
+         */
+
+        /* numbers of core dimensions of each argument */
+        int *core_num_dims;
+        /*
+         * dimension indices in a flatted form; indices
+         * are in the range of [0,core_num_dim_ix)
+         */
+        int *core_dim_ixs;
+        /*
+         * positions of 1st core dimensions of each
+         * argument in core_dim_ixs, equivalent to cumsum(core_num_dims)
+         */
+        int *core_offsets;
+        /* signature string for printing purpose */
+        char *core_signature;
+
+        /*
+         * A function which resolves the types and fills an array
+         * with the dtypes for the inputs and outputs.
+         */
+        PyUFunc_TypeResolutionFunc *type_resolver;
+        /*
+         * A function which returns an inner loop written for
+         * NumPy 1.6 and earlier ufuncs. This is for backwards
+         * compatibility, and may be NULL if inner_loop_selector
+         * is specified.
+         */
+        PyUFunc_LegacyInnerLoopSelectionFunc *legacy_inner_loop_selector;
+        /*
+         * This was blocked off to be the "new" inner loop selector in 1.7,
+         * but this was never implemented. (This is also why the above
+         * selector is called the "legacy" selector.)
+         */
+        #ifndef Py_LIMITED_API
+            vectorcallfunc vectorcall;
+        #else
+            void *vectorcall;
+        #endif
+
+        /* Was previously the `PyUFunc_MaskedInnerLoopSelectionFunc` */
+        void *_always_null_previously_masked_innerloop_selector;
+
+        /*
+         * List of flags for each operand when ufunc is called by nditer object.
+         * These flags will be used in addition to the default flags for each
+         * operand set by nditer object.
+         */
+        npy_uint32 *op_flags;
+
+        /*
+         * List of global flags used when ufunc is called by nditer object.
+         * These flags will be used in addition to the default global flags
+         * set by nditer object.
+         */
+        npy_uint32 iter_flags;
+
+        /* New in NPY_API_VERSION 0x0000000D and above */
+    #if NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION
+        /*
+         * for each core_num_dim_ix distinct dimension names,
+         * the possible "frozen" size (-1 if not frozen).
+         */
+        npy_intp *core_dim_sizes;
+
+        /*
+         * for each distinct core dimension, a set of UFUNC_CORE_DIM* flags
+         */
+        npy_uint32 *core_dim_flags;
+
+        /* Identity for reduction, when identity == PyUFunc_IdentityValue */
+        PyObject *identity_value;
+    #endif  /* NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION */
+
+        /* New in NPY_API_VERSION 0x0000000F and above */
+    #if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
+        /* New private fields related to dispatching */
+        void *_dispatch_cache;
+        /* A PyListObject of `(tuple of DTypes, ArrayMethod/Promoter)` */
+        PyObject *_loops;
+    #endif
+} PyUFuncObject;
+
+#include "arrayobject.h"
+/* Generalized ufunc; 0x0001 reserved for possible use as CORE_ENABLED */
+/* the core dimension's size will be determined by the operands. */
+#define UFUNC_CORE_DIM_SIZE_INFERRED 0x0002
+/* the core dimension may be absent */
+#define UFUNC_CORE_DIM_CAN_IGNORE 0x0004
+/* flags inferred during execution */
+#define UFUNC_CORE_DIM_MISSING 0x00040000
+
+#define UFUNC_ERR_IGNORE 0
+#define UFUNC_ERR_WARN   1
+#define UFUNC_ERR_RAISE  2
+#define UFUNC_ERR_CALL   3
+#define UFUNC_ERR_PRINT  4
+#define UFUNC_ERR_LOG    5
+
+        /* Python side integer mask */
+
+#define UFUNC_MASK_DIVIDEBYZERO 0x07
+#define UFUNC_MASK_OVERFLOW 0x3f
+#define UFUNC_MASK_UNDERFLOW 0x1ff
+#define UFUNC_MASK_INVALID 0xfff
+
+#define UFUNC_SHIFT_DIVIDEBYZERO 0
+#define UFUNC_SHIFT_OVERFLOW     3
+#define UFUNC_SHIFT_UNDERFLOW    6
+#define UFUNC_SHIFT_INVALID      9
+
+
+#define UFUNC_OBJ_ISOBJECT      1
+#define UFUNC_OBJ_NEEDS_API     2
+
+   /* Default user error mode */
+#define UFUNC_ERR_DEFAULT                               \
+        (UFUNC_ERR_WARN << UFUNC_SHIFT_DIVIDEBYZERO) +  \
+        (UFUNC_ERR_WARN << UFUNC_SHIFT_OVERFLOW) +      \
+        (UFUNC_ERR_WARN << UFUNC_SHIFT_INVALID)
+
+#if NPY_ALLOW_THREADS
+#define NPY_LOOP_BEGIN_THREADS do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) _save = PyEval_SaveThread();} while (0);
+#define NPY_LOOP_END_THREADS   do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) PyEval_RestoreThread(_save);} while (0);
+#else
+#define NPY_LOOP_BEGIN_THREADS
+#define NPY_LOOP_END_THREADS
+#endif
+
+/*
+ * UFunc has unit of 0, and the order of operations can be reordered
+ * This case allows reduction with multiple axes at once.
+ */
+#define PyUFunc_Zero 0
+/*
+ * UFunc has unit of 1, and the order of operations can be reordered
+ * This case allows reduction with multiple axes at once.
+ */
+#define PyUFunc_One 1
+/*
+ * UFunc has unit of -1, and the order of operations can be reordered
+ * This case allows reduction with multiple axes at once. Intended for
+ * bitwise_and reduction.
+ */
+#define PyUFunc_MinusOne 2
+/*
+ * UFunc has no unit, and the order of operations cannot be reordered.
+ * This case does not allow reduction with multiple axes at once.
+ */
+#define PyUFunc_None -1
+/*
+ * UFunc has no unit, and the order of operations can be reordered
+ * This case allows reduction with multiple axes at once.
+ */
+#define PyUFunc_ReorderableNone -2
+/*
+ * UFunc unit is an identity_value, and the order of operations can be reordered
+ * This case allows reduction with multiple axes at once.
+ */
+#define PyUFunc_IdentityValue -3
+
+
+#define UFUNC_REDUCE 0
+#define UFUNC_ACCUMULATE 1
+#define UFUNC_REDUCEAT 2
+#define UFUNC_OUTER 3
+
+
+typedef struct {
+        int nin;
+        int nout;
+        PyObject *callable;
+} PyUFunc_PyFuncData;
+
+/* A linked-list of function information for
+   user-defined 1-d loops.
+ */
+typedef struct _loop1d_info {
+        PyUFuncGenericFunction func;
+        void *data;
+        int *arg_types;
+        struct _loop1d_info *next;
+        int nargs;
+        PyArray_Descr **arg_dtypes;
+} PyUFunc_Loop1d;
+
+
+#include "__ufunc_api.h"
+
+#define UFUNC_PYVALS_NAME "UFUNC_PYVALS"
+
+/*
+ * THESE MACROS ARE DEPRECATED.
+ * Use npy_set_floatstatus_* in the npymath library.
+ */
+#define UFUNC_FPE_DIVIDEBYZERO  NPY_FPE_DIVIDEBYZERO
+#define UFUNC_FPE_OVERFLOW      NPY_FPE_OVERFLOW
+#define UFUNC_FPE_UNDERFLOW     NPY_FPE_UNDERFLOW
+#define UFUNC_FPE_INVALID       NPY_FPE_INVALID
+
+#define generate_divbyzero_error() npy_set_floatstatus_divbyzero()
+#define generate_overflow_error() npy_set_floatstatus_overflow()
+
+  /* Make sure it gets defined if it isn't already */
+#ifndef UFUNC_NOFPE
+/* Clear the floating point exception default of Borland C++ */
+#if defined(__BORLANDC__)
+#define UFUNC_NOFPE _control87(MCW_EM, MCW_EM);
+#else
+#define UFUNC_NOFPE
+#endif
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif  /* NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ */

pllava/lib/python3.10/site-packages/numpy/core/tests/data/numpy_2_0_array.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:561d36b5dc82ca96bc35be10cdd5619c30225c43b6590adcc1cbce7430c5179c
+size 718

pllava/lib/python3.10/site-packages/numpy/fft/__init__.py

@@ -0,0 +1,212 @@
+"""
+Discrete Fourier Transform (:mod:`numpy.fft`)
+=============================================
+
+.. currentmodule:: numpy.fft
+
+The SciPy module `scipy.fft` is a more comprehensive superset
+of ``numpy.fft``, which includes only a basic set of routines.
+
+Standard FFTs
+-------------
+
+.. autosummary::
+   :toctree: generated/
+
+   fft       Discrete Fourier transform.
+   ifft      Inverse discrete Fourier transform.
+   fft2      Discrete Fourier transform in two dimensions.
+   ifft2     Inverse discrete Fourier transform in two dimensions.
+   fftn      Discrete Fourier transform in N-dimensions.
+   ifftn     Inverse discrete Fourier transform in N dimensions.
+
+Real FFTs
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   rfft      Real discrete Fourier transform.
+   irfft     Inverse real discrete Fourier transform.
+   rfft2     Real discrete Fourier transform in two dimensions.
+   irfft2    Inverse real discrete Fourier transform in two dimensions.
+   rfftn     Real discrete Fourier transform in N dimensions.
+   irfftn    Inverse real discrete Fourier transform in N dimensions.
+
+Hermitian FFTs
+--------------
+
+.. autosummary::
+   :toctree: generated/
+
+   hfft      Hermitian discrete Fourier transform.
+   ihfft     Inverse Hermitian discrete Fourier transform.
+
+Helper routines
+---------------
+
+.. autosummary::
+   :toctree: generated/
+
+   fftfreq   Discrete Fourier Transform sample frequencies.
+   rfftfreq  DFT sample frequencies (for usage with rfft, irfft).
+   fftshift  Shift zero-frequency component to center of spectrum.
+   ifftshift Inverse of fftshift.
+
+
+Background information
+----------------------
+
+Fourier analysis is fundamentally a method for expressing a function as a
+sum of periodic components, and for recovering the function from those
+components.  When both the function and its Fourier transform are
+replaced with discretized counterparts, it is called the discrete Fourier
+transform (DFT).  The DFT has become a mainstay of numerical computing in
+part because of a very fast algorithm for computing it, called the Fast
+Fourier Transform (FFT), which was known to Gauss (1805) and was brought
+to light in its current form by Cooley and Tukey [CT]_.  Press et al. [NR]_
+provide an accessible introduction to Fourier analysis and its
+applications.
+
+Because the discrete Fourier transform separates its input into
+components that contribute at discrete frequencies, it has a great number
+of applications in digital signal processing, e.g., for filtering, and in
+this context the discretized input to the transform is customarily
+referred to as a *signal*, which exists in the *time domain*.  The output
+is called a *spectrum* or *transform* and exists in the *frequency
+domain*.
+
+Implementation details
+----------------------
+
+There are many ways to define the DFT, varying in the sign of the
+exponent, normalization, etc.  In this implementation, the DFT is defined
+as
+
+.. math::
+   A_k =  \\sum_{m=0}^{n-1} a_m \\exp\\left\\{-2\\pi i{mk \\over n}\\right\\}
+   \\qquad k = 0,\\ldots,n-1.
+
+The DFT is in general defined for complex inputs and outputs, and a
+single-frequency component at linear frequency :math:`f` is
+represented by a complex exponential
+:math:`a_m = \\exp\\{2\\pi i\\,f m\\Delta t\\}`, where :math:`\\Delta t`
+is the sampling interval.
+
+The values in the result follow so-called "standard" order: If ``A =
+fft(a, n)``, then ``A[0]`` contains the zero-frequency term (the sum of
+the signal), which is always purely real for real inputs. Then ``A[1:n/2]``
+contains the positive-frequency terms, and ``A[n/2+1:]`` contains the
+negative-frequency terms, in order of decreasingly negative frequency.
+For an even number of input points, ``A[n/2]`` represents both positive and
+negative Nyquist frequency, and is also purely real for real input.  For
+an odd number of input points, ``A[(n-1)/2]`` contains the largest positive
+frequency, while ``A[(n+1)/2]`` contains the largest negative frequency.
+The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies
+of corresponding elements in the output.  The routine
+``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the
+zero-frequency components in the middle, and ``np.fft.ifftshift(A)`` undoes
+that shift.
+
+When the input `a` is a time-domain signal and ``A = fft(a)``, ``np.abs(A)``
+is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum.
+The phase spectrum is obtained by ``np.angle(A)``.
+
+The inverse DFT is defined as
+
+.. math::
+   a_m = \\frac{1}{n}\\sum_{k=0}^{n-1}A_k\\exp\\left\\{2\\pi i{mk\\over n}\\right\\}
+   \\qquad m = 0,\\ldots,n-1.
+
+It differs from the forward transform by the sign of the exponential
+argument and the default normalization by :math:`1/n`.
+
+Type Promotion
+--------------
+
+`numpy.fft` promotes ``float32`` and ``complex64`` arrays to ``float64`` and
+``complex128`` arrays respectively. For an FFT implementation that does not
+promote input arrays, see `scipy.fftpack`.
+
+Normalization
+-------------
+
+The argument ``norm`` indicates which direction of the pair of direct/inverse
+transforms is scaled and with what normalization factor.
+The default normalization (``"backward"``) has the direct (forward) transforms
+unscaled and the inverse (backward) transforms scaled by :math:`1/n`. It is
+possible to obtain unitary transforms by setting the keyword argument ``norm``
+to ``"ortho"`` so that both direct and inverse transforms are scaled by
+:math:`1/\\sqrt{n}`. Finally, setting the keyword argument ``norm`` to
+``"forward"`` has the direct transforms scaled by :math:`1/n` and the inverse
+transforms unscaled (i.e. exactly opposite to the default ``"backward"``).
+`None` is an alias of the default option ``"backward"`` for backward
+compatibility.
+
+Real and Hermitian transforms
+-----------------------------
+
+When the input is purely real, its transform is Hermitian, i.e., the
+component at frequency :math:`f_k` is the complex conjugate of the
+component at frequency :math:`-f_k`, which means that for real
+inputs there is no information in the negative frequency components that
+is not already available from the positive frequency components.
+The family of `rfft` functions is
+designed to operate on real inputs, and exploits this symmetry by
+computing only the positive frequency components, up to and including the
+Nyquist frequency.  Thus, ``n`` input points produce ``n/2+1`` complex
+output points.  The inverses of this family assumes the same symmetry of
+its input, and for an output of ``n`` points uses ``n/2+1`` input points.
+
+Correspondingly, when the spectrum is purely real, the signal is
+Hermitian.  The `hfft` family of functions exploits this symmetry by
+using ``n/2+1`` complex points in the input (time) domain for ``n`` real
+points in the frequency domain.
+
+In higher dimensions, FFTs are used, e.g., for image analysis and
+filtering.  The computational efficiency of the FFT means that it can
+also be a faster way to compute large convolutions, using the property
+that a convolution in the time domain is equivalent to a point-by-point
+multiplication in the frequency domain.
+
+Higher dimensions
+-----------------
+
+In two dimensions, the DFT is defined as
+
+.. math::
+   A_{kl} =  \\sum_{m=0}^{M-1} \\sum_{n=0}^{N-1}
+   a_{mn}\\exp\\left\\{-2\\pi i \\left({mk\\over M}+{nl\\over N}\\right)\\right\\}
+   \\qquad k = 0, \\ldots, M-1;\\quad l = 0, \\ldots, N-1,
+
+which extends in the obvious way to higher dimensions, and the inverses
+in higher dimensions also extend in the same way.
+
+References
+----------
+
+.. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
+        machine calculation of complex Fourier series," *Math. Comput.*
+        19: 297-301.
+
+.. [NR] Press, W., Teukolsky, S., Vetterline, W.T., and Flannery, B.P.,
+        2007, *Numerical Recipes: The Art of Scientific Computing*, ch.
+        12-13.  Cambridge Univ. Press, Cambridge, UK.
+
+Examples
+--------
+
+For examples, see the various functions.
+
+"""
+
+from . import _pocketfft, helper
+from ._pocketfft import *
+from .helper import *
+
+__all__ = _pocketfft.__all__.copy()
+__all__ += helper.__all__
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester

pllava/lib/python3.10/site-packages/numpy/fft/__init__.pyi

@@ -0,0 +1,29 @@
+from numpy._pytesttester import PytestTester
+
+from numpy.fft._pocketfft import (
+    fft as fft,
+    ifft as ifft,
+    rfft as rfft,
+    irfft as irfft,
+    hfft as hfft,
+    ihfft as ihfft,
+    rfftn as rfftn,
+    irfftn as irfftn,
+    rfft2 as rfft2,
+    irfft2 as irfft2,
+    fft2 as fft2,
+    ifft2 as ifft2,
+    fftn as fftn,
+    ifftn as ifftn,
+)
+
+from numpy.fft.helper import (
+    fftshift as fftshift,
+    ifftshift as ifftshift,
+    fftfreq as fftfreq,
+    rfftfreq as rfftfreq,
+)
+
+__all__: list[str]
+__path__: list[str]
+test: PytestTester

pllava/lib/python3.10/site-packages/numpy/fft/_pocketfft.pyi

@@ -0,0 +1,108 @@
+from collections.abc import Sequence
+from typing import Literal as L
+
+from numpy import complex128, float64
+from numpy._typing import ArrayLike, NDArray, _ArrayLikeNumber_co
+
+_NormKind = L[None, "backward", "ortho", "forward"]
+
+__all__: list[str]
+
+def fft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def ifft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def rfft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def irfft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+) -> NDArray[float64]: ...
+
+# Input array must be compatible with `np.conjugate`
+def hfft(
+    a: _ArrayLikeNumber_co,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+) -> NDArray[float64]: ...
+
+def ihfft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def fftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def ifftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def rfftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def irfftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[float64]: ...
+
+def fft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def ifft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def rfft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[complex128]: ...
+
+def irfft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+) -> NDArray[float64]: ...

pllava/lib/python3.10/site-packages/numpy/fft/helper.py

@@ -0,0 +1,221 @@
+"""
+Discrete Fourier Transforms - helper.py
+
+"""
+from numpy.core import integer, empty, arange, asarray, roll
+from numpy.core.overrides import array_function_dispatch, set_module
+
+# Created by Pearu Peterson, September 2002
+
+__all__ = ['fftshift', 'ifftshift', 'fftfreq', 'rfftfreq']
+
+integer_types = (int, integer)
+
+
+def _fftshift_dispatcher(x, axes=None):
+    return (x,)
+
+
+@array_function_dispatch(_fftshift_dispatcher, module='numpy.fft')
+def fftshift(x, axes=None):
+    """
+    Shift the zero-frequency component to the center of the spectrum.
+
+    This function swaps half-spaces for all axes listed (defaults to all).
+    Note that ``y[0]`` is the Nyquist component only if ``len(x)`` is even.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    axes : int or shape tuple, optional
+        Axes over which to shift.  Default is None, which shifts all axes.
+
+    Returns
+    -------
+    y : ndarray
+        The shifted array.
+
+    See Also
+    --------
+    ifftshift : The inverse of `fftshift`.
+
+    Examples
+    --------
+    >>> freqs = np.fft.fftfreq(10, 0.1)
+    >>> freqs
+    array([ 0.,  1.,  2., ..., -3., -2., -1.])
+    >>> np.fft.fftshift(freqs)
+    array([-5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+
+    Shift the zero-frequency component only along the second axis:
+
+    >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
+    >>> freqs
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4., -4.],
+           [-3., -2., -1.]])
+    >>> np.fft.fftshift(freqs, axes=(1,))
+    array([[ 2.,  0.,  1.],
+           [-4.,  3.,  4.],
+           [-1., -3., -2.]])
+
+    """
+    x = asarray(x)
+    if axes is None:
+        axes = tuple(range(x.ndim))
+        shift = [dim // 2 for dim in x.shape]
+    elif isinstance(axes, integer_types):
+        shift = x.shape[axes] // 2
+    else:
+        shift = [x.shape[ax] // 2 for ax in axes]
+
+    return roll(x, shift, axes)
+
+
+@array_function_dispatch(_fftshift_dispatcher, module='numpy.fft')
+def ifftshift(x, axes=None):
+    """
+    The inverse of `fftshift`. Although identical for even-length `x`, the
+    functions differ by one sample for odd-length `x`.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    axes : int or shape tuple, optional
+        Axes over which to calculate.  Defaults to None, which shifts all axes.
+
+    Returns
+    -------
+    y : ndarray
+        The shifted array.
+
+    See Also
+    --------
+    fftshift : Shift zero-frequency component to the center of the spectrum.
+
+    Examples
+    --------
+    >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
+    >>> freqs
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4., -4.],
+           [-3., -2., -1.]])
+    >>> np.fft.ifftshift(np.fft.fftshift(freqs))
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4., -4.],
+           [-3., -2., -1.]])
+
+    """
+    x = asarray(x)
+    if axes is None:
+        axes = tuple(range(x.ndim))
+        shift = [-(dim // 2) for dim in x.shape]
+    elif isinstance(axes, integer_types):
+        shift = -(x.shape[axes] // 2)
+    else:
+        shift = [-(x.shape[ax] // 2) for ax in axes]
+
+    return roll(x, shift, axes)
+
+
+@set_module('numpy.fft')
+def fftfreq(n, d=1.0):
+    """
+    Return the Discrete Fourier Transform sample frequencies.
+
+    The returned float array `f` contains the frequency bin centers in cycles
+    per unit of the sample spacing (with zero at the start).  For instance, if
+    the sample spacing is in seconds, then the frequency unit is cycles/second.
+
+    Given a window length `n` and a sample spacing `d`::
+
+      f = [0, 1, ...,   n/2-1,     -n/2, ..., -1] / (d*n)   if n is even
+      f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n)   if n is odd
+
+    Parameters
+    ----------
+    n : int
+        Window length.
+    d : scalar, optional
+        Sample spacing (inverse of the sampling rate). Defaults to 1.
+
+    Returns
+    -------
+    f : ndarray
+        Array of length `n` containing the sample frequencies.
+
+    Examples
+    --------
+    >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)
+    >>> fourier = np.fft.fft(signal)
+    >>> n = signal.size
+    >>> timestep = 0.1
+    >>> freq = np.fft.fftfreq(n, d=timestep)
+    >>> freq
+    array([ 0.  ,  1.25,  2.5 , ..., -3.75, -2.5 , -1.25])
+
+    """
+    if not isinstance(n, integer_types):
+        raise ValueError("n should be an integer")
+    val = 1.0 / (n * d)
+    results = empty(n, int)
+    N = (n-1)//2 + 1
+    p1 = arange(0, N, dtype=int)
+    results[:N] = p1
+    p2 = arange(-(n//2), 0, dtype=int)
+    results[N:] = p2
+    return results * val
+
+
+@set_module('numpy.fft')
+def rfftfreq(n, d=1.0):
+    """
+    Return the Discrete Fourier Transform sample frequencies
+    (for usage with rfft, irfft).
+
+    The returned float array `f` contains the frequency bin centers in cycles
+    per unit of the sample spacing (with zero at the start).  For instance, if
+    the sample spacing is in seconds, then the frequency unit is cycles/second.
+
+    Given a window length `n` and a sample spacing `d`::
+
+      f = [0, 1, ...,     n/2-1,     n/2] / (d*n)   if n is even
+      f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n)   if n is odd
+
+    Unlike `fftfreq` (but like `scipy.fftpack.rfftfreq`)
+    the Nyquist frequency component is considered to be positive.
+
+    Parameters
+    ----------
+    n : int
+        Window length.
+    d : scalar, optional
+        Sample spacing (inverse of the sampling rate). Defaults to 1.
+
+    Returns
+    -------
+    f : ndarray
+        Array of length ``n//2 + 1`` containing the sample frequencies.
+
+    Examples
+    --------
+    >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5, -3, 4], dtype=float)
+    >>> fourier = np.fft.rfft(signal)
+    >>> n = signal.size
+    >>> sample_rate = 100
+    >>> freq = np.fft.fftfreq(n, d=1./sample_rate)
+    >>> freq
+    array([  0.,  10.,  20., ..., -30., -20., -10.])
+    >>> freq = np.fft.rfftfreq(n, d=1./sample_rate)
+    >>> freq
+    array([  0.,  10.,  20.,  30.,  40.,  50.])
+
+    """
+    if not isinstance(n, integer_types):
+        raise ValueError("n should be an integer")
+    val = 1.0/(n*d)
+    N = n//2 + 1
+    results = arange(0, N, dtype=int)
+    return results * val