def from_scipy_sparse_matrix(A, create_using=None):
"""Return a graph from scipy sparse matrix adjacency list.
Parameters
----------
A : scipy sparse matrix
An adjacency matrix representation of a graph
create_using : NetworkX graph
Use specified graph for result. The default is Graph()
Examples
--------
>>> import scipy.sparse
>>> A=scipy.sparse.eye(2,2,1)
>>> G=nx.from_scipy_sparse_matrix(A)
"""
G = _prep_create_using(create_using)
# convert all formats to lil - not the most efficient way
AA = A.tolil()
n, m = AA.shape
if n != m:
raise nx.NetworkXError(\
"Adjacency matrix is not square. nx,ny=%s"%(A.shape,))
G.add_nodes_from(range(n)) # make sure we get isolated nodes
for i, row in enumerate(AA.rows):
for pos, j in enumerate(row):
G.add_edge(i, j, **{'weight': AA.data[i][pos]})
return G
def _require(f, *args, **kwargs):
for package in reversed(packages):
try:
__import__(package)
except:
msg = "{0} requires {1}"
raise nx.NetworkXError(msg.format(f.__name__, package))
return f(*args, **kwargs)
def _open_file(func, *args, **kwargs):
# Note that since we have used @decorator, *args, and **kwargs have
# already been resolved to match the function signature of func. This
# means default values have been propagated. For example, the function
# func(x, y, a=1, b=2, **kwargs) if called as func(0,1,b=5,c=10) would
# have args=(0,1,1,5) and kwargs={'c':10}.
# First we parse the arguments of the decorator. The path_arg could
# be an positional argument or a keyword argument. Even if it is
try:
# path_arg is a required positional argument
# This works precisely because we are using @decorator
path = args[path_arg]
except TypeError:
# path_arg is a keyword argument. It is "required" in the sense
# that it must exist, according to the decorator specification,
# It can exist in `kwargs` by a developer specified default value
# or it could have been explicitly set by the user.
try:
path = kwargs[path_arg]
except KeyError:
# Could not find the keyword. Thus, no default was specified
# in the function signature and the user did not provide it.
msg = 'Missing required keyword argument: {0}'
raise nx.NetworkXError(msg.format(path_arg))
else:
is_kwarg = True
except IndexError:
# A "required" argument was missing. This can only happen if
# the decorator of the function was incorrectly specified.
# So this probably is not a user error, but a developer error.
msg = "path_arg of open_file decorator is incorrect"
raise nx.NetworkXError(msg)
else:
is_kwarg = False
# Now we have the path_arg. There are two types of input to consider:
# 1) string representing a path that should be opened
# 2) an already opened file object
if is_string_like(path):
ext = splitext(path)[1]
fobj = _dispatch_dict[ext](path, mode=mode)
close_fobj = True
elif hasattr(path, 'read'):
# path is already a file-like object
fobj = path
close_fobj = False
else:
# could be None, in which case the algorithm will deal with it
fobj = path
close_fobj = False
# Insert file object into args or kwargs.
if is_kwarg:
new_args = args
kwargs[path_arg] = fobj
else:
# args is a tuple, so we must convert to list before modifying it.
new_args = list(args)
new_args[path_arg] = fobj
# Finally, we call the original function, making sure to close the fobj.
try:
result = func(*new_args, **kwargs)
finally:
if close_fobj:
fobj.close()
return result
def to_numpy_recarray(G, nodelist=None,
dtype=[('weight', float)],
order=None):
"""Return 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()
>>> G.add_edge(1,2,weight=7.0,cost=5)
>>> 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]]
"""
try:
import numpy as np
except ImportError:
raise ImportError( \
"to_numpy_matrix() requires numpy: http://scipy.org/ ")
if G.is_multigraph():
raise nx.NetworkXError("Not implemented for multigraphs.")
if nodelist is None:
nodelist = G.nodes()
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_iter(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)
def to_scipy_sparse_matrix(G,
nodelist=None,
dtype=None,
weight='weight',
format='csr'):
"""Return 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
Graph adjacency 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.
Examples
--------
>>> G = nx.MultiDiGraph()
>>> G.add_edge(0,1,weight=2)
>>> G.add_edge(1,0)
>>> G.add_edge(2,2,weight=3)
>>> G.add_edge(2,2)
>>> 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",
http://docs.scipy.org/doc/scipy/reference/sparse.html
"""
try:
from scipy import sparse
except ImportError:
raise ImportError(\
"to_scipy_sparse_matrix() requires scipy: http://scipy.org/ ")
if nodelist is None:
nodelist = 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)))
if G.number_of_edges() == 0:
row, col, data = [], [], []
else:
row, col, data = zip(*((index[u], index[v], d.get(weight, 1))
for u, v, d in G.edges_iter(nodelist, data=True)
if u in index and v in index))
if G.is_directed():
M = sparse.coo_matrix((data, (row, col)),
shape=(nlen, nlen),
dtype=dtype)
else:
# symmetrize matrix
M = sparse.coo_matrix((data + data, (row + col, col + row)),
shape=(nlen, nlen),
dtype=dtype)
try:
return M.asformat(format)
except AttributeError:
raise nx.NetworkXError("Unknown sparse matrix format: %s" % format)