--- a/env/lib/python3.7/site-packages/networkx/convert.py	Thu May 14 16:47:39 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,391 +0,0 @@
-"""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
-"""
-#    Copyright (C) 2006-2013 by
-#    Aric Hagberg <hagberg@lanl.gov>
-#    Dan Schult <dschult@colgate.edu>
-#    Pieter Swart <swart@lanl.gov>
-#    All rights reserved.
-#    BSD license.
-import warnings
-import networkx as nx
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
-                            'Pieter Swart (swart@lanl.gov)',
-                            'Dan Schult(dschult@colgate.edu)'])
-__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
-         list 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:
-            raise nx.NetworkXError("Input is not a correct NetworkX graph.")
-
-    # pygraphviz  agraph
-    if hasattr(data, "is_strict"):
-        try:
-            return nx.nx_agraph.from_agraph(data, create_using=create_using)
-        except:
-            raise nx.NetworkXError("Input is not a correct pygraphviz graph.")
-
-    # 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:
-                raise TypeError("Input is not known type.")
-
-    # list or generator of edges
-
-    if (isinstance(data, (list, tuple)) or
-            any(hasattr(data, attr) for attr in ['_adjdict', 'next', '__next__'])):
-        try:
-            return from_edgelist(data, create_using=create_using)
-        except:
-            raise nx.NetworkXError("Input is not a valid edge list")
-
-    # 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:
-                    msg = "Input is not a correct Pandas DataFrame adjacency matrix."
-                    raise nx.NetworkXError(msg)
-            else:
-                try:
-                    return nx.from_pandas_edgelist(data, edge_attr=True, create_using=create_using)
-                except:
-                    msg = "Input is not a correct Pandas DataFrame edge-list."
-                    raise nx.NetworkXError(msg)
-    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:
-                raise nx.NetworkXError(
-                    "Input is not a correct numpy matrix or array.")
-    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:
-                raise nx.NetworkXError(
-                    "Input is not a correct scipy sparse matrix type.")
-    except ImportError:
-        warnings.warn('scipy not found, skipping conversion test.',
-                      ImportWarning)
-
-    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