--- a/env/lib/python3.7/site-packages/networkx/linalg/graphmatrix.py	Thu May 14 16:47:39 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,172 +0,0 @@
-"""
-Adjacency matrix and incidence matrix of graphs.
-"""
-#    Copyright (C) 2004-2019 by
-#    Aric Hagberg <hagberg@lanl.gov>
-#    Dan Schult <dschult@colgate.edu>
-#    Pieter Swart <swart@lanl.gov>
-#    All rights reserved.
-#    BSD license.
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
-                        'Pieter Swart (swart@lanl.gov)',
-                        'Dan Schult(dschult@colgate.edu)'])
-
-__all__ = ['incidence_matrix',
-           'adj_matrix', 'adjacency_matrix',
-           ]
-
-
-def incidence_matrix(G, nodelist=None, edgelist=None,
-                     oriented=False, weight=None):
-    """Returns incidence matrix of G.
-
-    The incidence matrix assigns each row to a node and each column to an edge.
-    For a standard incidence matrix a 1 appears wherever a row's node is
-    incident on the column's edge.  For an oriented incidence matrix each
-    edge is assigned an orientation (arbitrarily for undirected and aligning to
-    direction for directed).  A -1 appears for the tail of an edge and 1
-    for the head of the edge.  The elements are zero otherwise.
-
-    Parameters
-    ----------
-    G : graph
-       A NetworkX graph
-
-    nodelist : list, optional   (default= all nodes in G)
-       The rows are ordered according to the nodes in nodelist.
-       If nodelist is None, then the ordering is produced by G.nodes().
-
-    edgelist : list, optional (default= all edges in G)
-       The columns are ordered according to the edges in edgelist.
-       If edgelist is None, then the ordering is produced by G.edges().
-
-    oriented: bool, optional (default=False)
-       If True, matrix elements are +1 or -1 for the head or tail node
-       respectively of each edge.  If False, +1 occurs at both nodes.
-
-    weight : string or None, optional (default=None)
-       The edge data key used to provide each value in the matrix.
-       If None, then each edge has weight 1.  Edge weights, if used,
-       should be positive so that the orientation can provide the sign.
-
-    Returns
-    -------
-    A : SciPy sparse matrix
-      The incidence matrix of G.
-
-    Notes
-    -----
-    For MultiGraph/MultiDiGraph, the edges in edgelist should be
-    (u,v,key) 3-tuples.
-
-    "Networks are the best discrete model for so many problems in
-    applied mathematics" [1]_.
-
-    References
-    ----------
-    .. [1] Gil Strang, Network applications: A = incidence matrix,
-       http://academicearth.org/lectures/network-applications-incidence-matrix
-    """
-    import scipy.sparse
-    if nodelist is None:
-        nodelist = list(G)
-    if edgelist is None:
-        if G.is_multigraph():
-            edgelist = list(G.edges(keys=True))
-        else:
-            edgelist = list(G.edges())
-    A = scipy.sparse.lil_matrix((len(nodelist), len(edgelist)))
-    node_index = dict((node, i) for i, node in enumerate(nodelist))
-    for ei, e in enumerate(edgelist):
-        (u, v) = e[:2]
-        if u == v:
-            continue  # self loops give zero column
-        try:
-            ui = node_index[u]
-            vi = node_index[v]
-        except KeyError:
-            raise nx.NetworkXError('node %s or %s in edgelist '
-                                   'but not in nodelist' % (u, v))
-        if weight is None:
-            wt = 1
-        else:
-            if G.is_multigraph():
-                ekey = e[2]
-                wt = G[u][v][ekey].get(weight, 1)
-            else:
-                wt = G[u][v].get(weight, 1)
-        if oriented:
-            A[ui, ei] = -wt
-            A[vi, ei] = wt
-        else:
-            A[ui, ei] = wt
-            A[vi, ei] = wt
-    return A.asformat('csc')
-
-
-def adjacency_matrix(G, nodelist=None, weight='weight'):
-    """Returns adjacency matrix of G.
-
-    Parameters
-    ----------
-    G : graph
-       A NetworkX graph
-
-    nodelist : list, optional
-       The rows and columns are ordered according to the nodes in nodelist.
-       If nodelist is None, then the ordering is produced by G.nodes().
-
-    weight : string or None, optional (default='weight')
-       The edge data key used to provide each value in the matrix.
-       If None, then each edge has weight 1.
-
-    Returns
-    -------
-    A : SciPy sparse matrix
-      Adjacency matrix representation of G.
-
-    Notes
-    -----
-    For directed graphs, entry i,j corresponds to an edge from i to j.
-
-    If you want a pure Python adjacency matrix representation try
-    networkx.convert.to_dict_of_dicts which will return a
-    dictionary-of-dictionaries format that can be addressed as a
-    sparse matrix.
-
-    For MultiGraph/MultiDiGraph with parallel edges the weights are summed.
-    See to_numpy_matrix for other options.
-
-    The convention used for self-loop edges in graphs is to assign the
-    diagonal matrix entry value to the edge weight attribute
-    (or the number 1 if the edge has no weight attribute).  If the
-    alternate convention of doubling the edge weight is desired the
-    resulting Scipy sparse matrix can be modified as follows:
-
-    >>> import scipy as sp
-    >>> G = nx.Graph([(1,1)])
-    >>> A = nx.adjacency_matrix(G)
-    >>> print(A.todense())
-    [[1]]
-    >>> A.setdiag(A.diagonal()*2)
-    >>> print(A.todense())
-    [[2]]
-
-    See Also
-    --------
-    to_numpy_matrix
-    to_scipy_sparse_matrix
-    to_dict_of_dicts
-    adjacency_spectrum
-    """
-    return nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight)
-
-
-adj_matrix = adjacency_matrix
-
-
-# fixture for pytest
-def setup_module(module):
-    import pytest
-    scipy = pytest.importorskip('scipy')