--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.9/site-packages/networkx/convert.py	Mon Mar 22 18:12:50 2021 +0000
@@ -0,0 +1,406 @@
+"""Functions to convert NetworkX graphs to and from other formats.
+
+The preferred way of converting data to a NetworkX graph is through the
+graph constructor.  The constructor calls the to_networkx_graph() function
+which attempts to guess the input type and convert it automatically.
+
+Examples
+--------
+Create a graph with a single edge from a dictionary of dictionaries
+
+>>> d = {0: {1: 1}}  # dict-of-dicts single edge (0,1)
+>>> G = nx.Graph(d)
+
+See Also
+--------
+nx_agraph, nx_pydot
+"""
+import warnings
+import networkx as nx
+from collections.abc import Collection, Generator, Iterator
+
+__all__ = [
+    "to_networkx_graph",
+    "from_dict_of_dicts",
+    "to_dict_of_dicts",
+    "from_dict_of_lists",
+    "to_dict_of_lists",
+    "from_edgelist",
+    "to_edgelist",
+]
+
+
+def to_networkx_graph(data, create_using=None, multigraph_input=False):
+    """Make a NetworkX graph from a known data structure.
+
+    The preferred way to call this is automatically
+    from the class constructor
+
+    >>> d = {0: {1: {"weight": 1}}}  # dict-of-dicts single edge (0,1)
+    >>> G = nx.Graph(d)
+
+    instead of the equivalent
+
+    >>> G = nx.from_dict_of_dicts(d)
+
+    Parameters
+    ----------
+    data : object to be converted
+
+        Current known types are:
+         any NetworkX graph
+         dict-of-dicts
+         dict-of-lists
+         container (e.g. set, list, tuple) of edges
+         iterator (e.g. itertools.chain) that produces edges
+         generator of edges
+         Pandas DataFrame (row per edge)
+         numpy matrix
+         numpy ndarray
+         scipy sparse matrix
+         pygraphviz agraph
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    multigraph_input : bool (default False)
+        If True and  data is a dict_of_dicts,
+        try to create a multigraph assuming dict_of_dict_of_lists.
+        If data and create_using are both multigraphs then create
+        a multigraph from a multigraph.
+
+    """
+    # NX graph
+    if hasattr(data, "adj"):
+        try:
+            result = from_dict_of_dicts(
+                data.adj,
+                create_using=create_using,
+                multigraph_input=data.is_multigraph(),
+            )
+            if hasattr(data, "graph"):  # data.graph should be dict-like
+                result.graph.update(data.graph)
+            if hasattr(data, "nodes"):  # data.nodes should be dict-like
+                # result.add_node_from(data.nodes.items()) possible but
+                # for custom node_attr_dict_factory which may be hashable
+                # will be unexpected behavior
+                for n, dd in data.nodes.items():
+                    result._node[n].update(dd)
+            return result
+        except Exception as e:
+            raise nx.NetworkXError("Input is not a correct NetworkX graph.") from e
+
+    # pygraphviz  agraph
+    if hasattr(data, "is_strict"):
+        try:
+            return nx.nx_agraph.from_agraph(data, create_using=create_using)
+        except Exception as e:
+            raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from e
+
+    # dict of dicts/lists
+    if isinstance(data, dict):
+        try:
+            return from_dict_of_dicts(
+                data, create_using=create_using, multigraph_input=multigraph_input
+            )
+        except:
+            try:
+                return from_dict_of_lists(data, create_using=create_using)
+            except Exception as e:
+                raise TypeError("Input is not known type.") from e
+
+    # Pandas DataFrame
+    try:
+        import pandas as pd
+
+        if isinstance(data, pd.DataFrame):
+            if data.shape[0] == data.shape[1]:
+                try:
+                    return nx.from_pandas_adjacency(data, create_using=create_using)
+                except Exception as e:
+                    msg = "Input is not a correct Pandas DataFrame adjacency matrix."
+                    raise nx.NetworkXError(msg) from e
+            else:
+                try:
+                    return nx.from_pandas_edgelist(
+                        data, edge_attr=True, create_using=create_using
+                    )
+                except Exception as e:
+                    msg = "Input is not a correct Pandas DataFrame edge-list."
+                    raise nx.NetworkXError(msg) from e
+    except ImportError:
+        msg = "pandas not found, skipping conversion test."
+        warnings.warn(msg, ImportWarning)
+
+    # numpy matrix or ndarray
+    try:
+        import numpy
+
+        if isinstance(data, (numpy.matrix, numpy.ndarray)):
+            try:
+                return nx.from_numpy_matrix(data, create_using=create_using)
+            except Exception as e:
+                raise nx.NetworkXError(
+                    "Input is not a correct numpy matrix or array."
+                ) from e
+    except ImportError:
+        warnings.warn("numpy not found, skipping conversion test.", ImportWarning)
+
+    # scipy sparse matrix - any format
+    try:
+        import scipy
+
+        if hasattr(data, "format"):
+            try:
+                return nx.from_scipy_sparse_matrix(data, create_using=create_using)
+            except Exception as e:
+                raise nx.NetworkXError(
+                    "Input is not a correct scipy sparse matrix type."
+                ) from e
+    except ImportError:
+        warnings.warn("scipy not found, skipping conversion test.", ImportWarning)
+
+    # Note: most general check - should remain last in order of execution
+    # Includes containers (e.g. list, set, dict, etc.), generators, and
+    # iterators (e.g. itertools.chain) of edges
+
+    if isinstance(data, (Collection, Generator, Iterator)):
+        try:
+            return from_edgelist(data, create_using=create_using)
+        except Exception as e:
+            raise nx.NetworkXError("Input is not a valid edge list") from e
+
+    raise nx.NetworkXError("Input is not a known data type for conversion.")
+
+
+def to_dict_of_lists(G, nodelist=None):
+    """Returns adjacency representation of graph as a dictionary of lists.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list
+       Use only nodes specified in nodelist
+
+    Notes
+    -----
+    Completely ignores edge data for MultiGraph and MultiDiGraph.
+
+    """
+    if nodelist is None:
+        nodelist = G
+
+    d = {}
+    for n in nodelist:
+        d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
+    return d
+
+
+def from_dict_of_lists(d, create_using=None):
+    """Returns a graph from a dictionary of lists.
+
+    Parameters
+    ----------
+    d : dictionary of lists
+      A dictionary of lists adjacency representation.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    >>> dol = {0: [1]}  # single edge (0,1)
+    >>> G = nx.from_dict_of_lists(dol)
+
+    or
+
+    >>> G = nx.Graph(dol)  # use Graph constructor
+
+    """
+    G = nx.empty_graph(0, create_using)
+    G.add_nodes_from(d)
+    if G.is_multigraph() and not G.is_directed():
+        # a dict_of_lists can't show multiedges.  BUT for undirected graphs,
+        # each edge shows up twice in the dict_of_lists.
+        # So we need to treat this case separately.
+        seen = {}
+        for node, nbrlist in d.items():
+            for nbr in nbrlist:
+                if nbr not in seen:
+                    G.add_edge(node, nbr)
+            seen[node] = 1  # don't allow reverse edge to show up
+    else:
+        G.add_edges_from(
+            ((node, nbr) for node, nbrlist in d.items() for nbr in nbrlist)
+        )
+    return G
+
+
+def to_dict_of_dicts(G, nodelist=None, edge_data=None):
+    """Returns adjacency representation of graph as a dictionary of dictionaries.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list
+       Use only nodes specified in nodelist
+
+    edge_data : list, optional
+       If provided,  the value of the dictionary will be
+       set to edge_data for all edges.  This is useful to make
+       an adjacency matrix type representation with 1 as the edge data.
+       If edgedata is None, the edgedata in G is used to fill the values.
+       If G is a multigraph, the edgedata is a dict for each pair (u,v).
+    """
+    dod = {}
+    if nodelist is None:
+        if edge_data is None:
+            for u, nbrdict in G.adjacency():
+                dod[u] = nbrdict.copy()
+        else:  # edge_data is not None
+            for u, nbrdict in G.adjacency():
+                dod[u] = dod.fromkeys(nbrdict, edge_data)
+    else:  # nodelist is not None
+        if edge_data is None:
+            for u in nodelist:
+                dod[u] = {}
+                for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
+                    dod[u][v] = data
+        else:  # nodelist and edge_data are not None
+            for u in nodelist:
+                dod[u] = {}
+                for v in (v for v in G[u] if v in nodelist):
+                    dod[u][v] = edge_data
+    return dod
+
+
+def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
+    """Returns a graph from a dictionary of dictionaries.
+
+    Parameters
+    ----------
+    d : dictionary of dictionaries
+      A dictionary of dictionaries adjacency representation.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    multigraph_input : bool (default False)
+       When True, the values of the inner dict are assumed
+       to be containers of edge data for multiple edges.
+       Otherwise this routine assumes the edge data are singletons.
+
+    Examples
+    --------
+    >>> dod = {0: {1: {"weight": 1}}}  # single edge (0,1)
+    >>> G = nx.from_dict_of_dicts(dod)
+
+    or
+
+    >>> G = nx.Graph(dod)  # use Graph constructor
+
+    """
+    G = nx.empty_graph(0, create_using)
+    G.add_nodes_from(d)
+    # is dict a MultiGraph or MultiDiGraph?
+    if multigraph_input:
+        # make a copy of the list of edge data (but not the edge data)
+        if G.is_directed():
+            if G.is_multigraph():
+                G.add_edges_from(
+                    (u, v, key, data)
+                    for u, nbrs in d.items()
+                    for v, datadict in nbrs.items()
+                    for key, data in datadict.items()
+                )
+            else:
+                G.add_edges_from(
+                    (u, v, data)
+                    for u, nbrs in d.items()
+                    for v, datadict in nbrs.items()
+                    for key, data in datadict.items()
+                )
+        else:  # Undirected
+            if G.is_multigraph():
+                seen = set()  # don't add both directions of undirected graph
+                for u, nbrs in d.items():
+                    for v, datadict in nbrs.items():
+                        if (u, v) not in seen:
+                            G.add_edges_from(
+                                (u, v, key, data) for key, data in datadict.items()
+                            )
+                            seen.add((v, u))
+            else:
+                seen = set()  # don't add both directions of undirected graph
+                for u, nbrs in d.items():
+                    for v, datadict in nbrs.items():
+                        if (u, v) not in seen:
+                            G.add_edges_from(
+                                (u, v, data) for key, data in datadict.items()
+                            )
+                            seen.add((v, u))
+
+    else:  # not a multigraph to multigraph transfer
+        if G.is_multigraph() and not G.is_directed():
+            # d can have both representations u-v, v-u in dict.  Only add one.
+            # We don't need this check for digraphs since we add both directions,
+            # or for Graph() since it is done implicitly (parallel edges not allowed)
+            seen = set()
+            for u, nbrs in d.items():
+                for v, data in nbrs.items():
+                    if (u, v) not in seen:
+                        G.add_edge(u, v, key=0)
+                        G[u][v][0].update(data)
+                    seen.add((v, u))
+        else:
+            G.add_edges_from(
+                ((u, v, data) for u, nbrs in d.items() for v, data in nbrs.items())
+            )
+    return G
+
+
+def to_edgelist(G, nodelist=None):
+    """Returns a list of edges in the graph.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list
+       Use only nodes specified in nodelist
+
+    """
+    if nodelist is None:
+        return G.edges(data=True)
+    return G.edges(nodelist, data=True)
+
+
+def from_edgelist(edgelist, create_using=None):
+    """Returns a graph from a list of edges.
+
+    Parameters
+    ----------
+    edgelist : list or iterator
+      Edge tuples
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    >>> edgelist = [(0, 1)]  # single edge (0,1)
+    >>> G = nx.from_edgelist(edgelist)
+
+    or
+
+    >>> G = nx.Graph(edgelist)  # use Graph constructor
+
+    """
+    G = nx.empty_graph(0, create_using)
+    G.add_edges_from(edgelist)
+    return G