Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.convert_matrix

#    Copyright (C) 2006-2019 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
"""Functions to convert NetworkX graphs to and from numpy/scipy matrices.

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 10 node random graph from a numpy matrix

>>> import numpy as np
>>> a = np.random.randint(0, 2, size=(10, 10))
>>> D = nx.DiGraph(a)

or equivalently

>>> D = nx.to_networkx_graph(a, create_using=nx.DiGraph)

--------
nx_agraph, nx_pydot
"""

import itertools
import networkx as nx
from networkx.utils import not_implemented_for

__all__ = ['from_numpy_matrix', 'to_numpy_matrix',
'from_pandas_edgelist', 'to_pandas_edgelist',
'to_numpy_recarray',
'from_scipy_sparse_matrix', 'to_scipy_sparse_matrix',
'from_numpy_array', 'to_numpy_array']

multigraph_weight=sum, weight='weight', nonedge=0.0):
"""Returns the graph adjacency matrix as a Pandas DataFrame.

Parameters
----------
G : graph
The NetworkX graph used to construct the Pandas DataFrame.

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().

multigraph_weight : {sum, min, max}, optional
An operator that determines how weights in multigraphs are handled.
The default is to sum the weights of the multiple edges.

weight : string or None, optional
The edge attribute that holds the numerical value used for
the edge weight.  If an edge does not have that attribute, then the

nonedge : float, optional
The matrix values corresponding to nonedges are typically set to zero.
However, this could be undesirable if there are matrix values
corresponding to actual edges that also have the value zero. If so,
one might prefer nonedges to have some other value, such as nan.

Returns
-------
df : Pandas DataFrame

Notes
-----
The DataFrame entries are assigned to the weight edge attribute. When
an edge does not have a weight attribute, the value of the entry is set to
the number 1.  For multiple (parallel) edges, the values of the entries
are determined by the 'multigraph_weight' parameter.  The default is to
sum the weight attributes for each of the parallel edges.

When nodelist does not contain every node in G, the matrix is built
from the subgraph of G that is induced by the nodes in nodelist.

The convention used for self-loop edges in graphs is to assign the
diagonal matrix entry value to the weight attribute of the edge
(or the number 1 if the edge has no weight attribute).  If the
alternate convention of doubling the edge weight is desired the
resulting Pandas DataFrame can be modified as follows:

>>> import pandas as pd
>>> pd.options.display.max_columns = 20
>>> import numpy as np
>>> G = nx.Graph([(1, 1)])
>>> df
1
1  1
>>> df.values[np.diag_indices_from(df)] *= 2
>>> df
1
1  2

Examples
--------
>>> G = nx.MultiDiGraph()
0
0
0
1
>>> nx.to_pandas_adjacency(G, nodelist=[0, 1, 2], dtype=int)
0  1  2
0  0  2  0
1  1  0  0
2  0  0  4

"""
import pandas as pd
M = to_numpy_matrix(G, nodelist=nodelist, dtype=dtype, order=order,
multigraph_weight=multigraph_weight, weight=weight,
nonedge=nonedge)
if nodelist is None:
nodelist = list(G)
return pd.DataFrame(data=M, index=nodelist, columns=nodelist)

r"""Returns a graph from Pandas DataFrame.

The Pandas DataFrame is interpreted as an adjacency matrix for the graph.

Parameters
----------
df : Pandas DataFrame
An adjacency matrix representation of a graph

create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.

Notes
-----
If the numpy matrix has a single data type for each matrix entry it
will be converted to an appropriate Python data type.

If the numpy matrix has a user-specified compound data type the names
of the data fields will be used as attribute keys in the resulting
NetworkX graph.

--------

Examples
--------
Simple integer weights on edges:

>>> import pandas as pd
>>> pd.options.display.max_columns = 20
>>> df = pd.DataFrame([[1, 1], [2, 1]])
>>> df
0  1
0  1  1
1  2  1
>>> G.name = 'Graph from pandas adjacency matrix'
>>> print(nx.info(G))
Name: Graph from pandas adjacency matrix
Type: Graph
Number of nodes: 2
Number of edges: 3
Average degree:   3.0000

"""

try:
df = df[df.index]
except:
msg = "%s not in columns"
missing = list(set(df.index).difference(set(df.columns)))
raise nx.NetworkXError("Columns must match Indices.", msg % missing)

A = df.values
G = from_numpy_matrix(A, create_using=create_using)

nx.relabel.relabel_nodes(G, dict(enumerate(df.columns)), copy=False)
return G

[docs]def to_pandas_edgelist(G, source='source', target='target', nodelist=None,
dtype=None, order=None):
"""Returns the graph edge list as a Pandas DataFrame.

Parameters
----------
G : graph
The NetworkX graph used to construct the Pandas DataFrame.

source : str or int, optional
A valid column name (string or integer) for the source nodes (for the
directed case).

target : str or int, optional
A valid column name (string or integer) for the target nodes (for the
directed case).

nodelist : list, optional
Use only nodes specified in nodelist

Returns
-------
df : Pandas DataFrame
Graph edge list

Examples
--------
>>> G = nx.Graph([('A', 'B', {'cost': 1, 'weight': 7}),
...               ('C', 'E', {'cost': 9, 'weight': 10})])
>>> df = nx.to_pandas_edgelist(G, nodelist=['A', 'C'])
>>> df[['source', 'target', 'cost', 'weight']]
source target  cost  weight
0      A      B     1       7
1      C      E     9      10

"""
import pandas as pd
if nodelist is None:
edgelist = G.edges(data=True)
else:
edgelist = G.edges(nodelist, data=True)
source_nodes = [s for s, t, d in edgelist]
target_nodes = [t for s, t, d in edgelist]
all_keys = set().union(*(d.keys() for s, t, d in edgelist))
edge_attr = {k: [d.get(k, float("nan")) for s, t, d in edgelist]
for k in all_keys}
edgelistdict = {source: source_nodes, target: target_nodes}
edgelistdict.update(edge_attr)
return pd.DataFrame(edgelistdict)

[docs]def from_pandas_edgelist(df, source='source', target='target', edge_attr=None,
create_using=None):
"""Returns a graph from Pandas DataFrame containing an edge list.

The Pandas DataFrame should contain at least two columns of node names and
zero or more columns of edge attributes. Each row will be processed as one
edge instance.

Note: This function iterates over DataFrame.values, which is not
guaranteed to retain the data type across columns in the row. This is only
a problem if your row is entirely numeric and a mix of ints and floats. In
that case, all values will be returned as floats. See the
DataFrame.iterrows documentation for an example.

Parameters
----------
df : Pandas DataFrame
An edge list representation of a graph

source : str or int
A valid column name (string or integer) for the source nodes (for the
directed case).

target : str or int
A valid column name (string or integer) for the target nodes (for the
directed case).

edge_attr : str or int, iterable, True
A valid column name (str or integer) or list of column names that will
be used to retrieve items from the row and add them to the graph as
edge attributes. If True, all of the remaining columns will be added.

create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.

--------
to_pandas_edgelist

Examples
--------
Simple integer weights on edges:

>>> import pandas as pd
>>> pd.options.display.max_columns = 20
>>> import numpy as np
>>> rng = np.random.RandomState(seed=5)
>>> ints = rng.randint(1, 11, size=(3,2))
>>> a = ['A', 'B', 'C']
>>> b = ['D', 'A', 'E']
>>> df = pd.DataFrame(ints, columns=['weight', 'cost'])
>>> df[0] = a
>>> df['b'] = b
>>> df[['weight', 'cost', 0, 'b']]
weight  cost  0  b
0       4     7  A  D
1       7     1  B  A
2      10     9  C  E
>>> G = nx.from_pandas_edgelist(df, 0, 'b', ['weight', 'cost'])
>>> G['E']['C']['weight']
10
>>> G['E']['C']['cost']
9
>>> edges = pd.DataFrame({'source': [0, 1, 2],
...                       'target': [2, 2, 3],
...                       'weight': [3, 4, 5],
...                       'color': ['red', 'blue', 'blue']})
>>> G = nx.from_pandas_edgelist(edges, edge_attr=True)
>>> G[0][2]['color']
'red'

"""
g = nx.empty_graph(0, create_using)

if edge_attr is None:
return g

if edge_attr is True:
cols = [c for c in df.columns if c is not source and c is not target]
elif isinstance(edge_attr, (list, tuple)):
cols = edge_attr
else:
cols = [edge_attr]

try:
eattrs = zip(*[df[col] for col in cols])
except (KeyError, TypeError) as e:
msg = "Invalid edge_attr argument: %s" % edge_attr
raise nx.NetworkXError(msg)
for s, t, attrs in zip(df[source], df[target], eattrs):

if g.is_multigraph():
key = max(g[s][t])  # default keys just count so max is most recent
g[s][t][key].update((attr, val) for attr, val in zip(cols, attrs))
else:
g[s][t].update((attr, val) for attr, val in zip(cols, attrs))

return g

[docs]def to_numpy_matrix(G, nodelist=None, dtype=None, order=None,
multigraph_weight=sum, weight='weight', nonedge=0.0):
"""Returns the graph adjacency matrix as a NumPy matrix.

Parameters
----------
G : graph
The NetworkX graph used to construct the NumPy matrix.

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().

dtype : NumPy data type, optional
A valid single NumPy data type used to initialize the array.
This must be a simple type such as int or numpy.float64 and
not a compound data type (see to_numpy_recarray)
If None, then 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. If None, then the NumPy default
is used.

multigraph_weight : {sum, min, max}, optional
An operator that determines how weights in multigraphs are handled.
The default is to sum the weights of the multiple edges.

weight : string or None optional (default = 'weight')
The edge attribute that holds the numerical value used for
the edge weight. If an edge does not have that attribute, then the

nonedge : float (default = 0.0)
The matrix values corresponding to nonedges are typically set to zero.
However, this could be undesirable if there are matrix values
corresponding to actual edges that also have the value zero. If so,
one might prefer nonedges to have some other value, such as nan.

Returns
-------
M : NumPy matrix

--------
to_numpy_recarray, from_numpy_matrix

Notes
-----
The matrix entries are assigned to the weight edge attribute. When
an edge does not have a weight attribute, the value of the entry is set to
the number 1.  For multiple (parallel) edges, the values of the entries
are determined by the multigraph_weight parameter.  The default is to
sum the weight attributes for each of the parallel edges.

When nodelist does not contain every node in G, the matrix is built
from the subgraph of G that is induced by the nodes in nodelist.

The convention used for self-loop edges in graphs is to assign the
diagonal matrix entry value to the weight attribute of the edge
(or the number 1 if the edge has no weight attribute).  If the
alternate convention of doubling the edge weight is desired the
resulting Numpy matrix can be modified as follows:

>>> import numpy as np
>>> G = nx.Graph([(1, 1)])
>>> A = nx.to_numpy_matrix(G)
>>> A
matrix([[1.]])
>>> A.A[np.diag_indices_from(A)] *= 2
>>> A
matrix([[2.]])

Examples
--------
>>> G = nx.MultiDiGraph()
0
0
0
1
>>> nx.to_numpy_matrix(G, nodelist=[0, 1, 2])
matrix([[0., 2., 0.],
[1., 0., 0.],
[0., 0., 4.]])

"""
import numpy as np

A = to_numpy_array(G, nodelist=nodelist, dtype=dtype, order=order,
multigraph_weight=multigraph_weight, weight=weight,
nonedge=nonedge)
M = np.asmatrix(A, dtype=dtype)
return M

[docs]def from_numpy_matrix(A, parallel_edges=False, create_using=None):
"""Returns a graph from numpy matrix.

The numpy matrix is interpreted as an adjacency matrix for the graph.

Parameters
----------
A : numpy matrix
An adjacency matrix representation of a graph

parallel_edges : Boolean
If True, create_using is a multigraph, and A is an
integer matrix, then entry *(i, j)* in the matrix is interpreted as the
number of parallel edges joining vertices *i* and *j* in the graph.
If False, then the entries in the adjacency matrix are interpreted as
the weight of a single edge joining the vertices.

create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.

Notes
-----
If create_using is :class:networkx.MultiGraph or
:class:networkx.MultiDiGraph, parallel_edges is True, and the
entries of A are of type :class:int, then this function returns a
multigraph (constructed from create_using) with parallel edges.

If create_using indicates an undirected multigraph, then only the edges
indicated by the upper triangle of the matrix A will be added to the
graph.

If the numpy matrix has a single data type for each matrix entry it
will be converted to an appropriate Python data type.

If the numpy matrix has a user-specified compound data type the names
of the data fields will be used as attribute keys in the resulting
NetworkX graph.

--------
to_numpy_matrix, to_numpy_recarray

Examples
--------
Simple integer weights on edges:

>>> import numpy as np
>>> A = np.matrix([[1, 1], [2, 1]])
>>> G = nx.from_numpy_matrix(A)

If create_using indicates a multigraph and the matrix has only integer
entries and parallel_edges is False, then the entries will be treated
as weights for edges joining the nodes (without creating parallel edges):

>>> A = np.matrix([[1, 1], [1, 2]])
>>> G = nx.from_numpy_matrix(A, create_using=nx.MultiGraph)
>>> G[1][1]
AtlasView({0: {'weight': 2}})

If create_using indicates a multigraph and the matrix has only integer
entries and parallel_edges is True, then the entries will be treated
as the number of parallel edges joining those two vertices:

>>> A = np.matrix([[1, 1], [1, 2]])
>>> temp = nx.MultiGraph()
>>> G = nx.from_numpy_matrix(A, parallel_edges=True, create_using=temp)
>>> G[1][1]
AtlasView({0: {'weight': 1}, 1: {'weight': 1}})

User defined compound data type on edges:

>>> dt = [('weight', float), ('cost', int)]
>>> A = np.matrix([[(1.0, 2)]], dtype=dt)
>>> G = nx.from_numpy_matrix(A)
>>> list(G.edges())
[(0, 0)]
>>> G[0][0]['cost']
2
>>> G[0][0]['weight']
1.0

"""
# This should never fail if you have created a numpy matrix with numpy...
import numpy as np
kind_to_python_type = {'f': float,
'i': int,
'u': int,
'b': bool,
'c': complex,
'S': str,
'V': 'void'}
try:  # Python 3.x
blurb = chr(1245)  # just to trigger the exception
kind_to_python_type['U'] = str
except ValueError:  # Python 2.7
kind_to_python_type['U'] = unicode
G = nx.empty_graph(0, create_using)
n, m = A.shape
if n != m:
raise nx.NetworkXError("Adjacency matrix is not square.",
"nx,ny=%s" % (A.shape,))
dt = A.dtype
try:
python_type = kind_to_python_type[dt.kind]
except:
raise TypeError("Unknown numpy data type: %s" % dt)

# Make sure we get even the isolated nodes of the graph.
# Get a list of all the entries in the matrix with nonzero entries. These
# coordinates will become the edges in the graph.
edges = map(lambda e: (int(e[0]), int(e[1])),
zip(*(np.asarray(A).nonzero())))
# handle numpy constructed data type
if python_type is 'void':
# Sort the fields by their offset, then by dtype, then by name.
fields = sorted((offset, dtype, name) for name, (dtype, offset) in
A.dtype.fields.items())
triples = ((u, v, {name: kind_to_python_type[dtype.kind](val)
for (_, dtype, name), val in zip(fields, A[u, v])})
for u, v in edges)
# If the entries in the adjacency matrix are integers, the graph is a
# multigraph, and parallel_edges is True, then create parallel edges, each
# with weight 1, for each entry in the adjacency matrix. Otherwise, create
# one edge for each positive entry in the adjacency matrix and set the
# weight of that edge to be the entry in the matrix.
elif python_type is int and G.is_multigraph() and parallel_edges:
chain = itertools.chain.from_iterable
# The following line is equivalent to:
#
#     for (u, v) in edges:
#         for d in range(A[u, v]):
#
triples = chain(((u, v, dict(weight=1)) for d in range(A[u, v]))
for (u, v) in edges)
else:  # basic data type
triples = ((u, v, dict(weight=python_type(A[u, v])))
for u, v in edges)
# If we are creating an undirected multigraph, only add the edges from the
# upper triangle of the matrix. Otherwise, add all the edges. This relies
# on the fact that the vertices created in the
# _generated_weighted_edges() function are actually the row/column
# indices for the matrix A.
#
# Without this check, we run into a problem where each edge is added twice
# when G.add_edges_from() is invoked below.
if G.is_multigraph() and not G.is_directed():
triples = ((u, v, d) for u, v, d in triples if u <= v)
return G

[docs]@not_implemented_for('multigraph')
def to_numpy_recarray(G, nodelist=None, dtype=None, order=None):
"""Returns the graph adjacency matrix as a NumPy recarray.

Parameters
----------
G : graph
The NetworkX graph used to construct the NumPy matrix.

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().

dtype : NumPy data-type, optional
A valid NumPy named dtype used to initialize the NumPy recarray.
The data type names are assumed to be keys in the graph edge attribute
dictionary.

order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. If None, then the NumPy default
is used.

Returns
-------
M : NumPy recarray
The graph with specified edge data as a Numpy recarray

Notes
-----
When nodelist does not contain every node in G, the matrix is built
from the subgraph of G that is induced by the nodes in nodelist.

Examples
--------
>>> G = nx.Graph()
>>> A = nx.to_numpy_recarray(G, dtype=[('weight', float), ('cost', int)])
>>> print(A.weight)
[[0. 7.]
[7. 0.]]
>>> print(A.cost)
[[0 5]
[5 0]]

"""
if dtype is None:
dtype = [('weight', float)]
import numpy as np
if nodelist is None:
nodelist = list(G)
nodeset = set(nodelist)
if len(nodelist) != len(nodeset):
msg = "Ambiguous ordering: nodelist contained duplicates."
raise nx.NetworkXError(msg)
nlen = len(nodelist)
undirected = not G.is_directed()
index = dict(zip(nodelist, range(nlen)))
M = np.zeros((nlen, nlen), dtype=dtype, order=order)

names = M.dtype.names
for u, v, attrs in G.edges(data=True):
if (u in nodeset) and (v in nodeset):
i, j = index[u], index[v]
values = tuple([attrs[n] for n in names])
M[i, j] = values
if undirected:
M[j, i] = M[i, j]

return M.view(np.recarray)

[docs]def to_scipy_sparse_matrix(G, nodelist=None, dtype=None,
weight='weight', format='csr'):
"""Returns the graph adjacency matrix as a SciPy sparse matrix.

Parameters
----------
G : graph
The NetworkX graph used to construct the NumPy matrix.

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().

dtype : NumPy data-type, optional
A valid NumPy dtype used to initialize the array. If None, then the
NumPy default is used.

weight : string or None   optional (default='weight')
The edge attribute that holds the numerical value used for
the edge weight.  If None then all edge weights are 1.

format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
The type of the matrix to be returned (default 'csr').  For
some algorithms different implementations of sparse matrices
can perform better.  See [1]_ for details.

Returns
-------
M : SciPy sparse matrix

Notes
-----
The matrix entries are populated using the edge attribute held in
parameter weight. When an edge does not have that attribute, the
value of the entry is 1.

For multiple edges the matrix values are the sums of the edge weights.

When nodelist does not contain every node in G, the matrix is built
from the subgraph of G that is induced by the nodes in nodelist.

Uses coo_matrix format. To convert to other formats specify the
format= keyword.

The convention used for self-loop edges in graphs is to assign the
diagonal matrix entry value to the weight attribute of the edge
(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.to_scipy_sparse_matrix(G)
>>> print(A.todense())
[[1]]
>>> A.setdiag(A.diagonal() * 2)
>>> print(A.todense())
[[2]]

Examples
--------
>>> G = nx.MultiDiGraph()
0
0
0
1
>>> S = nx.to_scipy_sparse_matrix(G, nodelist=[0, 1, 2])
>>> print(S.todense())
[[0 2 0]
[1 0 0]
[0 0 4]]

References
----------
.. [1] Scipy Dev. References, "Sparse Matrices",
https://docs.scipy.org/doc/scipy/reference/sparse.html
"""
from scipy import sparse
if nodelist is None:
nodelist = list(G)
nlen = len(nodelist)
if nlen == 0:
raise nx.NetworkXError("Graph has no nodes or edges")

if len(nodelist) != len(set(nodelist)):
msg = "Ambiguous ordering: nodelist contained duplicates."
raise nx.NetworkXError(msg)

index = dict(zip(nodelist, range(nlen)))
coefficients = zip(*((index[u], index[v], d.get(weight, 1))
for u, v, d in G.edges(nodelist, data=True)
if u in index and v in index))
try:
row, col, data = coefficients
except ValueError:
# there is no edge in the subgraph
row, col, data = [], [], []

if G.is_directed():
M = sparse.coo_matrix((data, (row, col)),
shape=(nlen, nlen), dtype=dtype)
else:
# symmetrize matrix
d = data + data
r = row + col
c = col + row
# selfloop entries get double counted when symmetrizing
# so we subtract the data on the diagonal
selfloops = list(nx.selfloop_edges(G, data=True))
if selfloops:
diag_index, diag_data = zip(*((index[u], -d.get(weight, 1))
for u, v, d in selfloops
if u in index and v in index))
d += diag_data
r += diag_index
c += diag_index
M = sparse.coo_matrix((d, (r, c)), shape=(nlen, nlen), dtype=dtype)
try:
return M.asformat(format)
# From Scipy 1.1.0, asformat will throw a ValueError instead of an
# AttributeError if the format if not recognized.
except (AttributeError, ValueError):
raise nx.NetworkXError("Unknown sparse matrix format: %s" % format)

def _csr_gen_triples(A):
"""Converts a SciPy sparse matrix in **Compressed Sparse Row** format to
an iterable of weighted edge triples.

"""
nrows = A.shape[0]
data, indices, indptr = A.data, A.indices, A.indptr
for i in range(nrows):
for j in range(indptr[i], indptr[i + 1]):
yield i, indices[j], data[j]

def _csc_gen_triples(A):
"""Converts a SciPy sparse matrix in **Compressed Sparse Column** format to
an iterable of weighted edge triples.

"""
ncols = A.shape[1]
data, indices, indptr = A.data, A.indices, A.indptr
for i in range(ncols):
for j in range(indptr[i], indptr[i + 1]):
yield indices[j], i, data[j]

def _coo_gen_triples(A):
"""Converts a SciPy sparse matrix in **Coordinate** format to an iterable
of weighted edge triples.

"""
row, col, data = A.row, A.col, A.data
return zip(row, col, data)

def _dok_gen_triples(A):
"""Converts a SciPy sparse matrix in **Dictionary of Keys** format to an
iterable of weighted edge triples.

"""
for (r, c), v in A.items():
yield r, c, v

def _generate_weighted_edges(A):
"""Returns an iterable over (u, v, w) triples, where u and v are adjacent
vertices and w is the weight of the edge joining u and v.

A is a SciPy sparse matrix (in any format).

"""
if A.format == 'csr':
return _csr_gen_triples(A)
if A.format == 'csc':
return _csc_gen_triples(A)
if A.format == 'dok':
return _dok_gen_triples(A)
# If A is in any other format (including COO), convert it to COO format.
return _coo_gen_triples(A.tocoo())

[docs]def from_scipy_sparse_matrix(A, parallel_edges=False, create_using=None,
edge_attribute='weight'):
"""Creates a new graph from an adjacency matrix given as a SciPy sparse
matrix.

Parameters
----------
A: scipy sparse matrix
An adjacency matrix representation of a graph

parallel_edges : Boolean
If this is True, create_using is a multigraph, and A is an
integer matrix, then entry *(i, j)* in the matrix is interpreted as the
number of parallel edges joining vertices *i* and *j* in the graph.
If it is False, then the entries in the matrix are interpreted as
the weight of a single edge joining the vertices.

create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.

edge_attribute: string
Name of edge attribute to store matrix numeric value. The data will
have the same type as the matrix entry (int, float, (real,imag)).

Notes
-----

If create_using is :class:networkx.MultiGraph or
:class:networkx.MultiDiGraph, parallel_edges is True, and the
entries of A are of type :class:int, then this function returns a
multigraph (constructed from create_using) with parallel edges.
In this case, edge_attribute will be ignored.

If create_using indicates an undirected multigraph, then only the edges
indicated by the upper triangle of the matrix A will be added to the
graph.

Examples
--------
>>> import scipy as sp
>>> A = sp.sparse.eye(2, 2, 1)
>>> G = nx.from_scipy_sparse_matrix(A)

If create_using indicates a multigraph and the matrix has only integer
entries and parallel_edges is False, then the entries will be treated
as weights for edges joining the nodes (without creating parallel edges):

>>> A = sp.sparse.csr_matrix([[1, 1], [1, 2]])
>>> G = nx.from_scipy_sparse_matrix(A, create_using=nx.MultiGraph)
>>> G[1][1]
AtlasView({0: {'weight': 2}})

If create_using indicates a multigraph and the matrix has only integer
entries and parallel_edges is True, then the entries will be treated
as the number of parallel edges joining those two vertices:

>>> A = sp.sparse.csr_matrix([[1, 1], [1, 2]])
>>> G = nx.from_scipy_sparse_matrix(A, parallel_edges=True,
...                                 create_using=nx.MultiGraph)
>>> G[1][1]
AtlasView({0: {'weight': 1}, 1: {'weight': 1}})

"""
G = nx.empty_graph(0, create_using)
n, m = A.shape
if n != m:
raise nx.NetworkXError(
"Adjacency matrix is not square. nx,ny=%s" % (A.shape,))
# Make sure we get even the isolated nodes of the graph.
# Create an iterable over (u, v, w) triples and for each triple, add an
# edge from u to v with weight w.
triples = _generate_weighted_edges(A)
# If the entries in the adjacency matrix are integers, the graph is a
# multigraph, and parallel_edges is True, then create parallel edges, each
# with weight 1, for each entry in the adjacency matrix. Otherwise, create
# one edge for each positive entry in the adjacency matrix and set the
# weight of that edge to be the entry in the matrix.
if A.dtype.kind in ('i', 'u') and G.is_multigraph() and parallel_edges:
chain = itertools.chain.from_iterable
# The following line is equivalent to:
#
#     for (u, v) in edges:
#         for d in range(A[u, v]):
#
triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
# If we are creating an undirected multigraph, only add the edges from the
# upper triangle of the matrix. Otherwise, add all the edges. This relies
# on the fact that the vertices created in the
# _generated_weighted_edges() function are actually the row/column
# indices for the matrix A.
#
# Without this check, we run into a problem where each edge is added twice
# when G.add_weighted_edges_from() is invoked below.
if G.is_multigraph() and not G.is_directed():
triples = ((u, v, d) for u, v, d in triples if u <= v)
return G

[docs]def to_numpy_array(G, nodelist=None, dtype=None, order=None,
multigraph_weight=sum, weight='weight', nonedge=0.0):
"""Returns the graph adjacency matrix as a NumPy array.

Parameters
----------
G : graph
The NetworkX graph used to construct the NumPy array.

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().

dtype : NumPy data type, optional
A valid single NumPy data type used to initialize the array.
This must be a simple type such as int or numpy.float64 and
not a compound data type (see to_numpy_recarray)
If None, then 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. If None, then the NumPy default
is used.

multigraph_weight : {sum, min, max}, optional
An operator that determines how weights in multigraphs are handled.
The default is to sum the weights of the multiple edges.

weight : string or None optional (default = 'weight')
The edge attribute that holds the numerical value used for
the edge weight. If an edge does not have that attribute, then the

nonedge : float (default = 0.0)
The array values corresponding to nonedges are typically set to zero.
However, this could be undesirable if there are array values
corresponding to actual edges that also have the value zero. If so,
one might prefer nonedges to have some other value, such as nan.

Returns
-------
A : NumPy ndarray

--------
from_numpy_array

Notes
-----
Entries in the adjacency matrix are assigned to the weight edge attribute.
When an edge does not have a weight attribute, the value of the entry is
set to the number 1.  For multiple (parallel) edges, the values of the
entries are determined by the multigraph_weight parameter. The default is
to sum the weight attributes for each of the parallel edges.

When nodelist does not contain every node in G, the adjacency matrix is
built from the subgraph of G that is induced by the nodes in nodelist.

The convention used for self-loop edges in graphs is to assign the
diagonal array entry value to the weight attribute of the edge
(or the number 1 if the edge has no weight attribute). If the
alternate convention of doubling the edge weight is desired the
resulting NumPy array can be modified as follows:

>>> import numpy as np
>>> G = nx.Graph([(1, 1)])
>>> A = nx.to_numpy_array(G)
>>> A
array([[1.]])
>>> A[np.diag_indices_from(A)] *= 2
>>> A
array([[2.]])

Examples
--------
>>> G = nx.MultiDiGraph()
0
0
0
1
>>> nx.to_numpy_array(G, nodelist=[0, 1, 2])
array([[0., 2., 0.],
[1., 0., 0.],
[0., 0., 4.]])

"""
import numpy as np

if nodelist is None:
nodelist = list(G)
nodeset = set(nodelist)
if len(nodelist) != len(nodeset):
msg = "Ambiguous ordering: nodelist contained duplicates."
raise nx.NetworkXError(msg)

nlen = len(nodelist)
undirected = not G.is_directed()
index = dict(zip(nodelist, range(nlen)))

# Initially, we start with an array of nans.  Then we populate the array
# using data from the graph.  Afterwards, any leftover nans will be
# converted to the value of nonedge.  Note, we use nans initially,
# instead of zero, for two reasons:
#
#   1) It can be important to distinguish a real edge with the value 0
#      from a nonedge with the value 0.
#
#   2) When working with multi(di)graphs, we must combine the values of all
#      edges between any two nodes in some manner.  This often takes the
#      form of a sum, min, or max.  Using the value 0 for a nonedge would
#      have undesirable effects with min and max, but using nanmin and
#      nanmax with initially nan values is not problematic at all.
#
# That said, there are still some drawbacks to this approach. Namely, if
# a real edge is nan, then that value is a) not distinguishable from
# nonedges and b) is ignored by the default combinator (nansum, nanmin,
# nanmax) functions used for multi(di)graphs. If this becomes an issue,
# an alternative approach is to use masked arrays.  Initially, every
# element is masked and set to some initial value. As we populate the
# graph, elements are unmasked (automatically) when we combine the initial
# value with the values given by real edges.  At the end, we convert all
# masked values to nonedge. Using masked arrays fully addresses reason 1,
# but for reason 2, we would still have the issue with min and max if the
# initial values were 0.0.  Note: an initial value of +inf is appropriate
# for min, while an initial value of -inf is appropriate for max. When
# working with sum, an initial value of zero is appropriate. Ideally then,
# we'd want to allow users to specify both a value for nonedges and also
# an initial value.  For multi(di)graphs, the choice of the initial value
# will, in general, depend on the combinator function---sensible defaults
# can be provided.

if G.is_multigraph():
# Handle MultiGraphs and MultiDiGraphs
A = np.full((nlen, nlen), np.nan, order=order)
# use numpy nan-aware operations
operator = {sum: np.nansum, min: np.nanmin, max: np.nanmax}
try:
op = operator[multigraph_weight]
except:
raise ValueError('multigraph_weight must be sum, min, or max')

for u, v, attrs in G.edges(data=True):
if (u in nodeset) and (v in nodeset):
i, j = index[u], index[v]
e_weight = attrs.get(weight, 1)
A[i, j] = op([e_weight, A[i, j]])
if undirected:
A[j, i] = A[i, j]
else:
# Graph or DiGraph, this is much faster than above
A = np.full((nlen, nlen), np.nan, order=order)
for v, d in nbrdict.items():
try:
A[index[u], index[v]] = d.get(weight, 1)
except KeyError:
# This occurs when there are fewer desired nodes than
# there are nodes in the graph: len(nodelist) < len(G)
pass

A[np.isnan(A)] = nonedge
A = np.asarray(A, dtype=dtype)
return A

[docs]def from_numpy_array(A, parallel_edges=False, create_using=None):
"""Returns a graph from NumPy array.

The NumPy array is interpreted as an adjacency matrix for the graph.

Parameters
----------
A : NumPy ndarray
An adjacency matrix representation of a graph

parallel_edges : Boolean
If this is True, create_using is a multigraph, and A is an
integer array, then entry *(i, j)* in the array is interpreted as the
number of parallel edges joining vertices *i* and *j* in the graph.
If it is False, then the entries in the array are interpreted as
the weight of a single edge joining the vertices.

create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.

Notes
-----
If create_using is :class:networkx.MultiGraph or
:class:networkx.MultiDiGraph, parallel_edges is True, and the
entries of A are of type :class:int, then this function returns a
multigraph (of the same type as create_using) with parallel edges.

If create_using indicates an undirected multigraph, then only the edges
indicated by the upper triangle of the array A will be added to the
graph.

If the NumPy array has a single data type for each array entry it
will be converted to an appropriate Python data type.

If the NumPy array has a user-specified compound data type the names
of the data fields will be used as attribute keys in the resulting
NetworkX graph.

--------
to_numpy_array

Examples
--------
Simple integer weights on edges:

>>> import numpy as np
>>> A = np.array([[1, 1], [2, 1]])
>>> G = nx.from_numpy_array(A)
>>> G.edges(data=True)
EdgeDataView([(0, 0, {'weight': 1}), (0, 1, {'weight': 2}), (1, 1, {'weight': 1})])

If create_using indicates a multigraph and the array has only integer
entries and parallel_edges is False, then the entries will be treated
as weights for edges joining the nodes (without creating parallel edges):

>>> A = np.array([[1, 1], [1, 2]])
>>> G = nx.from_numpy_array(A, create_using=nx.MultiGraph)
>>> G[1][1]
AtlasView({0: {'weight': 2}})

If create_using indicates a multigraph and the array has only integer
entries and parallel_edges is True, then the entries will be treated
as the number of parallel edges joining those two vertices:

>>> A = np.array([[1, 1], [1, 2]])
>>> temp = nx.MultiGraph()
>>> G = nx.from_numpy_array(A, parallel_edges=True, create_using=temp)
>>> G[1][1]
AtlasView({0: {'weight': 1}, 1: {'weight': 1}})

User defined compound data type on edges:

>>> dt = [('weight', float), ('cost', int)]
>>> A = np.array([[(1.0, 2)]], dtype=dt)
>>> G = nx.from_numpy_array(A)
>>> G.edges()
EdgeView([(0, 0)])
>>> G[0][0]['cost']
2
>>> G[0][0]['weight']
1.0

"""
return from_numpy_matrix(A, parallel_edges=parallel_edges,
create_using=create_using)

# fixture for nose tests
def setup_module(module):
from nose import SkipTest
try:
import numpy
except:
raise SkipTest("NumPy not available")
try:
import scipy
except:
raise SkipTest("SciPy not available")
try:
import pandas
except:
raise SkipTest("Pandas not available")