def generate_pajek_(G):
import warnings
from networkx.utils import is_string_like, open_file, make_str
if G.name == '':
name = 'NetworkX'
else:
name = G.name
# Apparently many Pajek format readers can't process this line
# So we'll leave it out for now.
# yield '*network %s'%name
# write nodes with attributes
yield '*vertices %s' % (G.order())
nodes = list(G)
# make dictionary mapping nodes to integers
nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
for n in nodes:
# copy node attributes and pop mandatory attributes
# to avoid duplication.
na = G.nodes.get(n, {}).copy()
x = na.pop('x', 0.0)
y = na.pop('y', 0.0)
id = int(na.pop('id', nodenumber[n]))
nodenumber[n] = id
shape = na.pop('shape', 'ellipse')
s = ' '.join(map(make_qstr, (id, n, x, y, shape)))
# only optional attributes are left in na.
for k, v in na.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
else:
warnings.warn('Node attribute %s is not processed. %s.' %
(k,
'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
s = s.replace('\n', ' ')
yield s
# write edges with attributes
if G.is_directed():
yield '*arcs'
else:
yield '*edges'
for u, v, edgedata in G.edges(data=True):
d = edgedata.copy()
value = d.pop('weight', 1.0) # use 1 as default edge value
s = ' '.join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
for k, v in d.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
s += ' '
else:
warnings.warn('Edge attribute %s is not processed. %s.' %
(k,
'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
yield s
def generate_pajek(G):
"""Generate lines in Pajek graph format.
Parameters
----------
G : graph
A Networkx graph
References
----------
See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
for format information.
"""
if G.name=='':
name='NetworkX'
else:
name=G.name
yield '*network %s'%name
# write nodes with attributes
yield '*vertices %s'%(G.order())
nodes = G.nodes()
# make dictionary mapping nodes to integers
nodenumber=dict(zip(nodes,range(1,len(nodes)+1)))
for n in nodes:
na=G.node.get(n,{})
x=na.get('x',0.0)
y=na.get('y',0.0)
id=int(na.get('id',nodenumber[n]))
nodenumber[n]=id
shape=na.get('shape','ellipse')
s=' '.join(map(make_str,(id,n,x,y,shape)))
for k,v in na.items():
if is_string_like(v):
# add quotes to any values with a blank space
if " " in v:
v="\"%s\""%v
s+=' %s %s'%(k,v)
yield s
# write edges with attributes
if G.is_directed():
yield '*arcs'
else:
yield '*edges'
for u,v,edgedata in G.edges(data=True):
d=edgedata.copy()
value=d.pop('weight',1.0) # use 1 as default edge value
s=' '.join(map(make_str,(nodenumber[u],nodenumber[v],value)))
for k,v in d.items():
if is_string_like(v):
# add quotes to any values with a blank space
if " " in v:
v="\"%s\""%v
s+=' %s %s'%(k,v)
yield s
def generate_pajek(G):
"""Generate lines in Pajek graph format.
Parameters
----------
G : graph
A Networkx graph
References
----------
See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
for format information.
"""
if G.name == '':
name = 'NetworkX'
else:
name = G.name
yield '*network %s' % name
# write nodes with attributes
yield '*vertices %s' % (G.order())
nodes = G.nodes()
# make dictionary mapping nodes to integers
nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
for n in nodes:
na = G.node.get(n, {})
x = na.get('x', 0.0)
y = na.get('y', 0.0)
id = int(na.get('id', nodenumber[n]))
nodenumber[n] = id
shape = na.get('shape', 'ellipse')
s = ' '.join(map(make_str, (id, n, x, y, shape)))
for k, v in na.items():
if is_string_like(v):
# add quotes to any values with a blank space
if " " in v:
v = "\"%s\"" % v
s += ' %s %s' % (k, v)
yield s
# write edges with attributes
if G.is_directed():
yield '*arcs'
else:
yield '*edges'
for u, v, edgedata in G.edges(data=True):
d = edgedata.copy()
value = d.pop('weight', 1.0) # use 1 as default edge value
s = ' '.join(map(make_str, (nodenumber[u], nodenumber[v], value)))
for k, v in d.items():
if is_string_like(v):
# add quotes to any values with a blank space
if " " in v:
v = "\"%s\"" % v
s += ' %s %s' % (k, v)
yield s
def generate_pajek_(G):
import warnings
from networkx.utils import is_string_like, open_file, make_str
if G.name == '':
name = 'NetworkX'
else:
name = G.name
# Apparently many Pajek format readers can't process this line
# So we'll leave it out for now.
# yield '*network %s'%name
# write nodes with attributes
yield '*vertices %s' % (G.order())
nodes = list(G)
# make dictionary mapping nodes to integers
nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
for n in nodes:
# copy node attributes and pop mandatory attributes
# to avoid duplication.
na = G.nodes.get(n, {}).copy()
x = na.pop('x', 0.0)
y = na.pop('y', 0.0)
id = int(na.pop('id', nodenumber[n]))
nodenumber[n] = id
shape = na.pop('shape', 'ellipse')
s = ' '.join(map(make_qstr, (id, n, x, y, shape)))
# only optional attributes are left in na.
for k, v in na.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
else:
warnings.warn('Node attribute %s is not processed. %s.' %
(k, 'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
s = s.replace('\n', ' ')
yield s
# write edges with attributes
if G.is_directed():
yield '*arcs'
else:
yield '*edges'
for u, v, edgedata in G.edges(data=True):
d = edgedata.copy()
value = d.pop('weight', 1.0) # use 1 as default edge value
s = ' '.join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
for k, v in d.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
s += ' '
else:
warnings.warn('Edge attribute %s is not processed. %s.' %
(k, 'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
yield s
def add_prefix1(x):
prefix = rename[1]
if is_string_like(x):
name = prefix + x
else:
name = prefix + repr(x)
return name
def add_prefix1(x):
prefix = rename[1]
if is_string_like(x):
name = prefix + x
else:
name = prefix + repr(x)
return name
def parse_p2g(lines):
"""Parse p2g format graph from string or iterable.
Returns an MultiDiGraph.
"""
if is_string_like(lines): lines=iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
description = lines.next()
# are multiedges (parallel edges) allowed?
G=networkx.MultiDiGraph(name=description,selfloops=True)
nnodes,nedges=map(int,lines.next().split())
nodelabel={}
nbrs={}
# loop over the nodes keeping track of node labels and out neighbors
# defer adding edges until all node labels are known
for i in range(nnodes):
n=lines.next()
nodelabel[i]=n
G.add_node(n)
nbrs[n]=map(int,lines.next().split())
# now we know all of the node labels so we can add the edges
# with the correct labels
for n in G:
for nbr in nbrs[n]:
G.add_edge(n,nodelabel[nbr])
return G
def parse_p2g(lines):
"""Parse p2g format graph from string or iterable. Returns an XDiGraph."""
if is_string_like(lines): lines = iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
description = lines.next()
# are multiedges (parallel edges) allowed?
G = networkx.XDiGraph(name=description, selfloops=True)
nnodes, nedges = map(int, lines.next().split())
nodelabel = {}
nbrs = {}
# loop over the nodes keeping track of node labels and out neighbors
# defer adding edges until all node labels are known
for i in range(nnodes):
n = lines.next()
nodelabel[i] = n
G.add_node(n)
nbrs[n] = map(int, lines.next().split())
# now we know all of the node labels so we can add the edges
# with the correct labels
for n in G:
for nbr in nbrs[n]:
G.add_edge(n, nodelabel[nbr])
return G
def write_pajek(G, path):
"""Write in Pajek format to path.
Parameters
----------
G : graph
A networkx graph
path : file or string
File or filename to write.
Filenames ending in .gz or .bz2 will be compressed.
Examples
--------
>>> G=nx.path_graph(4)
>>> nx.write_pajek(G, "test.net")
"""
fh=_get_fh(path,mode='w')
if G.name=='':
name="NetworkX"
else:
name=G.name
fh.write("*network %s\n"%name)
# write nodes with attributes
fh.write("*vertices %s\n"%(G.order()))
nodes = G.nodes()
# make dictionary mapping nodes to integers
nodenumber=dict(zip(nodes,range(1,len(nodes)+1)))
for n in nodes:
na=G.node[n].copy()
x=na.pop('x',0.0)
y=na.pop('y',0.0)
id=int(na.pop('id',nodenumber[n]))
nodenumber[n]=id
shape=na.pop('shape','ellipse')
fh.write("%d \"%s\" %f %f %s "%(id,n,float(x),float(y),shape))
for k,v in na.items():
fh.write("%s %s "%(k,v))
fh.write("\n")
# write edges with attributes
if G.is_directed():
fh.write("*arcs\n")
else:
fh.write("*edges\n")
for u,v,edgedata in G.edges(data=True):
d=edgedata.copy()
value=d.pop('weight',1.0) # use 1 as default edge value
fh.write("%d %d %f "%(nodenumber[u],nodenumber[v],float(value)))
for k,v in d.items():
if is_string_like(v):
# add quotes to any values with a blank space
if " " in v:
v="\"%s\""%v
fh.write("%s %s "%(k,v))
fh.write("\n")
def string_item(k,v,indent):
# try to make a string of the data
if type(v)==dict:
v=listify(v,indent,2)
elif is_string_like(v):
v='"%s"'%v
elif type(v)==bool:
v=int(v)
return "%s %s"%(k,v)
def parse_leda(lines):
"""Read graph in LEDA format from string or iterable.
Parameters
----------
lines : string or iterable
Data in LEDA format.
Returns
-------
G : NetworkX graph
Examples
--------
G=nx.parse_leda(string)
References
----------
.. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
"""
if is_string_like(lines):
lines = iter(lines.split('\n'))
lines = iter([
line.rstrip('\n') for line in lines
if not (line.startswith('#') or line.startswith('\n') or line == '')
])
for i in range(3):
next(lines)
# Graph
du = int(next(lines)) # -1=directed, -2=undirected
if du == -1:
G = nx.DiGraph()
else:
G = nx.Graph()
# Nodes
n = int(next(lines)) # number of nodes
node = {}
for i in range(1, n + 1): # LEDA counts from 1 to n
symbol = next(lines).rstrip().strip('|{}| ')
if symbol == "":
symbol = str(i) # use int if no label - could be trouble
node[i] = symbol
G.add_nodes_from([s for i, s in node.items()])
# Edges
m = int(next(lines)) # number of edges
for i in range(m):
try:
s, t, reversal, label = next(lines).split()
except:
raise NetworkXError('Too few fields in LEDA.GRAPH edge %d' %
(i + 1))
# BEWARE: no handling of reversal edges
G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
return G
def make_qstr(t):
"""Return the string representation of t.
Add outer double-quotes if the string has a space.
"""
if not is_string_like(t):
t = str(t)
if " " in t:
t=r'"%s"'%t
return t
def make_qstr(t):
"""Return the string representation of t.
Add outer double-quotes if the string has a space.
"""
if not is_string_like(t):
t = str(t)
if " " in t:
t = r'"%s"' % t
return t
def string_item(k, v, indent):
# try to make a string of the data
if type(v) == dict:
v = listify(v, indent, 2)
elif is_string_like(v):
v = '"%s"' % v
elif type(v) == bool:
v = int(v)
return "%s %s" % (k, v)
def string_item(k,v,indent):
# try to make a string of the data
if type(v) == dict:
v = listify(v, indent, 2)
elif is_string_like(v):
v = '"{0}"'.format(escape(v, quote=True))
#v = '"{0}"'.format(v, quote=True)
elif type(v) == bool:
v = int(v)
return "{0} {1}".format(k,v)
def string_item(k, v, indent):
# try to make a string of the data
if type(v) == dict:
v = listify(v, indent, 2)
elif is_string_like(v):
v = '"{0}"'.format(escape(v, quote=True))
#v = '"{0}"'.format(v, quote=True)
elif type(v) == bool:
v = int(v)
return "{0} {1}".format(k, v)
def parse_leda(lines):
"""Read graph in LEDA format from string or iterable.
Parameters
----------
lines : string or iterable
Data in LEDA format.
Returns
-------
G : NetworkX graph
Examples
--------
G=nx.parse_leda(string)
References
----------
.. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
"""
if is_string_like(lines):
lines = iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines
if not (line.startswith('#') or line.startswith('\n') or line == '')])
for i in range(3):
next(lines)
# Graph
du = int(next(lines)) # -1=directed, -2=undirected
if du == -1:
G = nx.DiGraph()
else:
G = nx.Graph()
# Nodes
n = int(next(lines)) # number of nodes
node = {}
for i in range(1, n + 1): # LEDA counts from 1 to n
symbol = next(lines).rstrip().strip('|{}| ')
if symbol == "":
symbol = str(i) # use int if no label - could be trouble
node[i] = symbol
G.add_nodes_from([s for i, s in node.items()])
# Edges
m = int(next(lines)) # number of edges
for i in range(m):
try:
s, t, reversal, label = next(lines).split()
except:
raise NetworkXError('Too few fields in LEDA.GRAPH edge %d' % (i + 1))
# BEWARE: no handling of reversal edges
G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
return G
def graphml_datatype(val):
if val is None:
return "string"
if is_string_like(val):
return "string"
if type(val) == type(1):
return "int"
if type(val) == type(1.0):
return "double"
if type(val) == type(True):
return "boolean"
if type(val) == type(1L):
return "long"
def graphml_datatype(val):
if val is None:
return "string"
if is_string_like(val):
return "string"
if type(val) == type(1):
return "int"
if type(val) == type(1.0):
return "double"
if type(val) == type(True):
return "boolean"
if type(val) == type(1L):
return "long"
def parse_pajek(lines):
"""Parse pajek format graph from string or iterable.."""
import shlex
if is_string_like(lines): lines=iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G=networkx.XDiGraph(selfloops=True) # are multiedges allowed in Pajek?
G.node_attr={} # dictionary to hold node attributes
directed=True # assume this is a directed network for now
while lines:
try:
l=lines.next()
except: #EOF
break
if l.startswith("*network"):
label,name=l.split()
G.name=name
if l.startswith("*vertices"):
nodelabels={}
l,nnodes=l.split()
for i in range(int(nnodes)):
splitline=shlex.split(lines.next())
id,label,x,y,shape=splitline[0:5]
G.add_node(label)
nodelabels[id]=label
G.node_attr[label]={'id':id,'x':x,'y':y,'shape':shape}
extra_attr=zip(splitline[5::2],splitline[6::2])
G.node_attr[label].update(extra_attr)
if l.startswith("*edges") or l.startswith("*arcs"):
if l.startswith("*edge"):
G=networkx.XGraph(G) # switch from digraph to graph
for l in lines:
splitline=shlex.split(l)
ui,vi,w=splitline[0:3]
u=nodelabels.get(ui,ui)
v=nodelabels.get(vi,vi)
edge_data={'value':float(w)}
extra_attr=zip(splitline[3::2],splitline[4::2])
edge_data.update(extra_attr)
G.add_edge(u,v,edge_data)
return G
def parse_pajek(lines):
"""Parse pajek format graph from string or iterable.."""
import shlex
if is_string_like(lines): lines = iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G = networkx.XDiGraph(selfloops=True) # are multiedges allowed in Pajek?
G.node_attr = {} # dictionary to hold node attributes
directed = True # assume this is a directed network for now
while lines:
try:
l = lines.next()
except: #EOF
break
if l.startswith("*network"):
label, name = l.split()
G.name = name
if l.startswith("*vertices"):
nodelabels = {}
l, nnodes = l.split()
for i in range(int(nnodes)):
splitline = shlex.split(lines.next())
id, label, x, y, shape = splitline[0:5]
G.add_node(label)
nodelabels[id] = label
G.node_attr[label] = {'id': id, 'x': x, 'y': y, 'shape': shape}
extra_attr = zip(splitline[5::2], splitline[6::2])
G.node_attr[label].update(extra_attr)
if l.startswith("*edges") or l.startswith("*arcs"):
if l.startswith("*edge"):
G = networkx.XGraph(G) # switch from digraph to graph
for l in lines:
splitline = shlex.split(l)
ui, vi, w = splitline[0:3]
u = nodelabels.get(ui, ui)
v = nodelabels.get(vi, vi)
edge_data = {'value': float(w)}
extra_attr = zip(splitline[3::2], splitline[4::2])
edge_data.update(extra_attr)
G.add_edge(u, v, edge_data)
return G
def parse_leda(lines):
"""Parse LEDA.GRAPH format from string or iterable.
Returns an Graph or DiGraph."""
if is_string_like(lines): lines = iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines \
if not (line.startswith('#') or line.startswith('\n') or line=='')])
for i in range(3):
lines.next()
# Graph
du = int(lines.next()) # -1 directed, -2 undirected
if du == -1:
G = networkx.DiGraph()
else:
G = networkx.Graph()
# Nodes
n = int(lines.next()) # number of vertices
node = {}
for i in range(1, n + 1): # LEDA counts from 1 to n
symbol = lines.next()[2:-3] # strip out data from |{data}|
if symbol == "":
symbol = str(i) # use int if no label - could be trouble
node[i] = symbol
G.add_nodes_from([s for i, s in node.items()])
# Edges
m = int(lines.next()) # number of edges
for i in range(m):
try:
s, t, reversal, label = lines.next().split()
except:
raise NetworkXError,\
'Too few fields in LEDA.GRAPH edge %d' % (i+1)
# BEWARE: no handling of reversal edges
G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
return G
def parse_leda(lines):
"""Parse LEDA.GRAPH format from string or iterable.
Returns an Graph or DiGraph."""
if is_string_like(lines): lines=iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines \
if not (line.startswith('#') or line.startswith('\n') or line=='')])
for i in range(3):
lines.next()
# Graph
du = int(lines.next()) # -1 directed, -2 undirected
if du==-1:
G = networkx.DiGraph()
else:
G = networkx.Graph()
# Nodes
n =int(lines.next()) # number of vertices
node={}
for i in range(1,n+1): # LEDA counts from 1 to n
symbol=lines.next()[2:-3] # strip out data from |{data}|
if symbol=="": symbol=str(i) # use int if no label - could be trouble
node[i]=symbol
G.add_nodes_from([s for i,s in node.items()])
# Edges
m = int(lines.next()) # number of edges
for i in range(m):
try:
s,t,reversal,label=lines.next().split()
except:
raise NetworkXError,\
'Too few fields in LEDA.GRAPH edge %d' % (i+1)
# BEWARE: no handling of reversal edges
G.add_edge(node[int(s)],node[int(t)],label=label[2:-2])
return G
def parse_pajek(lines):
"""Parse Pajek format graph from string or iterable.
Parameters
----------
lines : string or iterable
Data in Pajek format.
Returns
-------
G : NetworkX graph
See Also
--------
read_pajek()
"""
import shlex
# multigraph=False
if is_string_like(lines): lines = iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G = nx.MultiDiGraph() # are multiedges allowed in Pajek? assume yes
while lines:
try:
l = next(lines)
except: #EOF
break
if l.lower().startswith("*network"):
try:
label, name = l.split(None, 1)
except ValueError:
# Line was not of the form: *network NAME
pass
else:
G.graph['name'] = name
elif l.lower().startswith("*vertices"):
nodelabels = {}
l, nnodes = l.split()
for i in range(int(nnodes)):
l = next(lines)
try:
splitline = [
x.decode('utf-8')
for x in shlex.split(make_str(l).encode('utf-8'))
]
except AttributeError:
splitline = shlex.split(str(l))
id, label = splitline[0:2]
G.add_node(label)
nodelabels[id] = label
G.node[label] = {'id': id}
try:
x, y, shape = splitline[2:5]
G.node[label].update({
'x': float(x),
'y': float(y),
'shape': shape
})
except:
pass
extra_attr = zip(splitline[5::2], splitline[6::2])
G.node[label].update(extra_attr)
elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
if l.lower().startswith("*edge"):
# switch from multidigraph to multigraph
G = nx.MultiGraph(G)
if l.lower().startswith("*arcs"):
# switch to directed with multiple arcs for each existing edge
G = G.to_directed()
for l in lines:
try:
splitline = [
x.decode('utf-8')
for x in shlex.split(make_str(l).encode('utf-8'))
]
except AttributeError:
splitline = shlex.split(str(l))
if len(splitline) < 2:
continue
ui, vi = splitline[0:2]
u = nodelabels.get(ui, ui)
v = nodelabels.get(vi, vi)
# parse the data attached to this edge and put in a dictionary
edge_data = {}
try:
# there should always be a single value on the edge?
w = splitline[2:3]
edge_data.update({'weight': float(w[0])})
except:
pass
# if there isn't, just assign a 1
# edge_data.update({'value':1})
extra_attr = zip(splitline[3::2], splitline[4::2])
edge_data.update(extra_attr)
# if G.has_edge(u,v):
# multigraph=True
G.add_edge(u, v, **edge_data)
return G
def parse_pajek(lines,edge_attr=True):
"""Parse pajek format graph from string or iterable.
Primarily used as a helper for read_pajek().
See Also
--------
read_pajek()
"""
import shlex
if is_string_like(lines): lines=iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G=nx.MultiDiGraph() # are multiedges allowed in Pajek?
directed=True # assume this is a directed network for now
while lines:
try:
l=lines.next()
except: #EOF
break
if l.lower().startswith("*network"):
label,name=l.split()
G.name=name
if l.lower().startswith("*vertices"):
nodelabels={}
l,nnodes=l.split()
for i in range(int(nnodes)):
splitline=shlex.split(lines.next())
id,label=splitline[0:2]
G.add_node(label)
nodelabels[id]=label
G.node[label]={'id':id}
try:
x,y,shape=splitline[2:5]
G.node[label].update({'x':x,'y':y,'shape':shape})
except:
pass
extra_attr=zip(splitline[5::2],splitline[6::2])
G.node[label].update(extra_attr)
if l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
if l.lower().startswith("*edge"):
# switch from digraph to graph
G=nx.MultiGraph(G)
for l in lines:
splitline=shlex.split(l)
ui,vi=splitline[0:2]
u=nodelabels.get(ui,ui)
v=nodelabels.get(vi,vi)
# parse the data attached to this edge and
# put it in a dictionary
edge_data={}
try:
# there should always be a single value on the edge?
w=splitline[2:3]
edge_data.update({'weight':float(w[0])})
except:
pass
# if there isn't, just assign a 1
# edge_data.update({'value':1})
extra_attr=zip(splitline[3::2],splitline[4::2])
edge_data.update(extra_attr)
G.add_edge(u,v,**edge_data)
return G
def generate_gml(G):
"""Generate a single entry of the graph G in GML format.
Parameters
----------
G : NetworkX graph
Returns
-------
lines: string
Lines in GML format.
Notes
-----
This implementation does not support all Python data types as GML
data. Nodes, node attributes, edge attributes, and graph
attributes must be either dictionaries or single stings or
numbers. If they are not an attempt is made to represent them as
strings. For example, a list as edge data
G[1][2]['somedata']=[1,2,3], will be represented in the GML file
as::
edge [
source 1
target 2
somedata "[1, 2, 3]"
]
"""
# recursively make dicts into gml brackets
def listify(d, indent, indentlevel):
result = '[ \n'
for k, v in d.items():
result += (indentlevel+1)*indent + \
string_item(k,v,indentlevel*indent)+'\n'
return result + indentlevel * indent + "]"
def string_item(k, v, indent):
# try to make a string of the data
if type(v) == dict:
v = listify(v, indent, 2)
elif is_string_like(v):
v = '"%s"' % v
elif type(v) == bool:
v = int(v)
return "%s %s" % (k, v)
# check for attributes or assign empty dict
if hasattr(G, 'graph_attr'):
graph_attr = G.graph_attr
else:
graph_attr = {}
if hasattr(G, 'node_attr'):
node_attr = G.node_attr
else:
node_attr = {}
indent = 2 * ' '
count = iter(range(len(G)))
node_id = {}
yield "graph ["
if G.is_directed():
yield indent + "directed 1"
# write graph attributes
for k, v in G.graph.items():
if k == 'directed':
continue
yield indent + string_item(k, v, indent)
# write nodes
for n in G:
yield indent + "node ["
# get id or assign number
nid = G.node[n].get('id', next(count))
node_id[n] = nid
yield 2 * indent + "id %s" % nid
label = G.node[n].get('label', n)
if is_string_like(label):
label = '"%s"' % label
yield 2 * indent + 'label %s' % label
if n in G:
for k, v in G.node[n].items():
if k == 'id' or k == 'label': continue
yield 2 * indent + string_item(k, v, indent)
yield indent + "]"
# write edges
for u, v, edgedata in G.edges_iter(data=True):
yield indent + "edge ["
yield 2 * indent + "source %s" % node_id[u]
yield 2 * indent + "target %s" % node_id[v]
for k, v in edgedata.items():
if k == 'source': continue
if k == 'target': continue
yield 2 * indent + string_item(k, v, indent)
yield indent + "]"
yield "]"
def write_gml(G, path):
"""
Write the graph G in GML format to the file or file handle path.
Examples
---------
>>> G=nx.path_graph(4)
>>> nx.write_gml(G,"test.gml")
path can be a filehandle or a string with the name of the file.
>>> fh=open("test.gml",'w')
>>> nx.write_gml(G,fh)
Filenames ending in .gz or .bz2 will be compressed.
>>> nx.write_gml(G,"test.gml.gz")
The output file will use the default text encoding on your system.
It is possible to write files in other encodings by opening
the file with the codecs module. See doc/examples/unicode.py
for hints.
>>> import codecs
>>> fh=codecs.open("test.gml",'w',encoding='iso8859-1')# use iso8859-1
>>> nx.write_gml(G,fh)
GML specifications indicate that the file should only use
7bit ASCII text encoding.iso8859-1 (latin-1).
Only a single level of attributes for graphs, nodes, and edges,
is supported.
"""
fh=_get_fh(path,mode='w')
# comments="#"
# pargs=comments+" "+' '.join(sys.argv)
# fh.write("%s\n" % (pargs))
# fh.write(comments+" GMT %s\n" % (time.asctime(time.gmtime())))
# fh.write(comments+" %s\n" % (G.name))
# check for attributes or assign empty dict
if hasattr(G,'graph_attr'):
graph_attr=G.graph_attr
else:
graph_attr={}
if hasattr(G,'node_attr'):
node_attr=G.node_attr
else:
node_attr={}
indent=2*' '
count=iter(range(G.number_of_nodes()))
node_id={}
fh.write("graph [\n")
# write graph attributes
for k,v in graph_attr.items():
if is_string_like(v):
v='"'+v+'"'
fh.write(indent+"%s %s\n"%(k,v))
# write nodes
for n in G:
fh.write(indent+"node [\n")
# get id or assign number
if n in node_attr:
nid=node_attr[n].get('id',count.next())
else:
nid=count.next()
node_id[n]=nid
fh.write(2*indent+"id %s\n"%nid)
fh.write(2*indent+"label \"%s\"\n"%n)
if n in node_attr:
for k,v in node_attr[n].items():
if is_string_like(v): v='"'+v+'"'
if k=='id': continue
fh.write(2*indent+"%s %s\n"%(k,v))
fh.write(indent+"]\n")
# write edges
for e in G.edges_iter():
if len(e)==3:
u,v,d=e
# try to guess what is on the edge and do something reasonable
edgedata={}
if d is None: # no data
pass
elif hasattr(d,'iteritems'): # try dict-like
edgedata=dict(d.iteritems())
elif hasattr(d,'__iter__'): # some kind of container
edgedata['data']=d.__repr__()
else: # something else - string, number
edgedata['data']=d
else:
u,v=e
edgedata={}
fh.write(indent+"edge [\n")
fh.write(2*indent+"source %s\n"%node_id[u])
fh.write(2*indent+"target %s\n"%node_id[v])
for k,v in edgedata.items():
if k=='source': continue
if k=='target': continue
if is_string_like(v): v='"'+v+'"'
fh.write(2*indent+"%s %s\n"%(k,v))
fh.write(indent+"]\n")
fh.write("]\n")
def parse_pajek_(lines):
import shlex
import networkx as nx
from networkx.utils import is_string_like, open_file, make_str
# multigraph=False
if is_string_like(lines):
lines = iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G = nx.MultiDiGraph() # are multiedges allowed in Pajek? assume yes
labels = [] # in the order of the file, needed for matrix
while lines:
try:
l = next(lines)
except: # EOF
break
if l.lower().startswith("*network"):
try:
label, name = l.split(None, 1)
except ValueError:
# Line was not of the form: *network NAME
pass
else:
G.graph['name'] = name
elif l.lower().startswith("*vertices"):
nodelabels = {}
l, nnodes = l.split()
for i in range(int(nnodes)):
l = next(lines)
try:
splitline = [
x.decode('utf-8')
for x in shlex.split(make_str(l).encode('utf-8'))
]
except AttributeError:
splitline = shlex.split(str(l))
id, label = splitline[0:2]
labels.append(label)
G.add_node(label)
nodelabels[id] = label
G.nodes[label]['id'] = id
try:
x, y, shape = splitline[2:5]
G.nodes[label].update({
'x': float(x),
'y': float(y),
'shape': shape
})
except:
pass
extra_attr = zip(splitline[5::2], splitline[6::2])
G.nodes[label].update(extra_attr)
elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
if l.lower().startswith("*edge"):
# switch from multidigraph to multigraph
G = nx.MultiGraph(G)
if l.lower().startswith("*arcs"):
# switch to directed with multiple arcs for each existing edge
G = G.to_directed()
for l in lines:
try:
splitline = [
x.decode('utf-8')
for x in shlex.split(make_str(l).encode('utf-8'))
]
except AttributeError:
splitline = shlex.split(str(l))
if len(splitline) < 2:
continue
ui, vi = splitline[0:2]
u = nodelabels.get(ui, ui)
v = nodelabels.get(vi, vi)
# parse the data attached to this edge and put in a dictionary
edge_data = {}
try:
# there should always be a single value on the edge?
w = splitline[2:3]
edge_data.update({'weight': float(w[0])})
except:
pass
# if there isn't, just assign a 1
# edge_data.update({'value':1})
extra_attr = zip(splitline[3::2], splitline[4::2])
edge_data.update(extra_attr)
# if G.has_edge(u,v):
# multigraph=True
G.add_edge(u, v, **edge_data)
elif l.lower().startswith("*matrix"):
G = nx.DiGraph(G)
adj_list = ((labels[row], labels[col], {
'weight': int(data)
}) for (row, line) in enumerate(lines)
for (col, data) in enumerate(line.split())
if int(data) != 0)
G.add_edges_from(adj_list)
return G
def union(G, H, create_using=None, rename=False, name=None):
""" Return the union of graphs G and H.
Graphs G and H must be disjoint, otherwise an exception is raised.
Parameters
----------
G,H : graph
A NetworkX graph
create_using : NetworkX graph
Use specified graph for result. Otherwise a new graph is created.
rename : bool (default=False)
Node names of G and H can be changed be specifying the tuple
rename=('G-','H-') (for example).
Node u in G is then renamed "G-u" and v in H is renamed "H-v".
name : string
Specify name for union graph
Notes
-----
To force a disjoint union with node relabeling, use
disjoint_union(G,H) or convert_node_labels_to integers().
"""
if name is None:
name = "union( %s, %s )" % (G.name, H.name)
if create_using is None:
R = G.__class__()
else:
R = create_using
R.clear()
R.name = name
# rename graph to obtain disjoint node labels
# note that for objects w/o succinct __name__,
# the new labels can be quite verbose
# See also disjoint_union
if rename: # create new string labels
Gname = {}
Hname = {}
for v in G:
if is_string_like(v):
Gname[v] = rename[0] + v
else:
Gname[v] = rename[0] + repr(v)
for v in H:
if is_string_like(v):
Hname[v] = rename[1] + v
else:
Hname[v] = rename[1] + repr(v)
else:
Gname = dict(((v, v) for v in G))
Hname = dict(((v, v) for v in H))
# node name check
Hnames = set(Hname.values())
Gnames = set(Gname.values())
if Gnames & Hnames:
raise networkx.NetworkXError,\
"node sets of G and H are not disjoint. Use appropriate rename=('Gprefix','Hprefix')"
# node names OK, now build union
R.add_nodes_from(Gnames)
R.add_edges_from(
((Gname[u], Gname[v], x) for u, v, x in G.edges_iter(data=True)))
R.add_nodes_from(Hnames)
R.add_edges_from(
((Hname[u], Hname[v], x) for u, v, x in H.edges_iter(data=True)))
return R
def label(x):
if is_string_like(x):
name = prefix + x
else:
name = prefix + repr(x)
return name
def test_is_string_like():
assert is_string_like("aaaa")
assert not is_string_like(None)
assert not is_string_like(123)
def draw_networkx_edge_labels(G, pos,
edge_labels=None,
label_pos=0.5,
font_size=10,
font_color='k',
font_family='sans-serif',
font_weight='normal',
alpha=1.0,
bbox=None,
ax=None,
rotate=True,
**kwds):
"""Draw edge labels.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
alpha : float
The text transparency (default=1.0)
edge_labels : dictionary
Edge labels in a dictionary keyed by edge two-tuple of text
labels (default=None). Only labels for the keys in the dictionary
are drawn.
label_pos : float
Position of edge label along edge (0=head, 0.5=center, 1=tail)
font_size : int
Font size for text labels (default=12)
font_color : string
Font color string (default='k' black)
font_weight : string
Font weight (default='normal')
font_family : string
Font family (default='sans-serif')
bbox : Matplotlib bbox
Specify text box shape and colors.
clip_on : bool
Turn on clipping at axis boundaries (default=True)
Returns
-------
dict
`dict` of labels keyed on the edges
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_labels()
"""
try:
import matplotlib.pyplot as plt
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edge_labels is None:
labels = {(u, v): d for u, v, d in G.edges(data=True)}
else:
labels = edge_labels
text_items = {}
for (n1, n2), label in labels.items():
(x1, y1) = pos[n1]
(x2, y2) = pos[n2]
(x, y) = (x1 * label_pos + x2 * (1.0 - label_pos),
y1 * label_pos + y2 * (1.0 - label_pos))
if rotate:
# in degrees
angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360
# make label orientation "right-side-up"
if angle > 90:
angle -= 180
if angle < - 90:
angle += 180
# transform data coordinate angle to screen coordinate angle
xy = np.array((x, y))
trans_angle = ax.transData.transform_angles(np.array((angle,)),
xy.reshape((1, 2)))[0]
else:
trans_angle = 0.0
# use default box of white with white border
if bbox is None:
bbox = dict(boxstyle='round',
ec=(1.0, 1.0, 1.0),
fc=(1.0, 1.0, 1.0),
)
if not is_string_like(label):
label = str(label) # this makes "1" and 1 labeled the same
# set optional alignment
horizontalalignment = kwds.get('horizontalalignment', 'center')
verticalalignment = kwds.get('verticalalignment', 'center')
t = ax.text(x, y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
alpha=alpha,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
rotation=trans_angle,
transform=ax.transData,
bbox=bbox,
zorder=1,
clip_on=True,
)
text_items[(n1, n2)] = t
plt.tick_params(
axis='both',
which='both',
bottom=False,
left=False,
labelbottom=False,
labelleft=False)
return text_items
def parse_pajek(lines):
"""Parse pajek format graph from string or iterable.."""
import shlex
if is_string_like(lines): lines=iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G=networkx.DiGraph() # are multiedges allowed in Pajek?
G.node_attr={} # dictionary to hold node attributes
directed=True # assume this is a directed network for now
while lines:
try:
l=lines.next()
l=l.lower()
except: #EOF
break
if l.startswith("#"):
continue
if l.startswith("*network"):
label,name=l.split()
G.name=name
if l.startswith("*vertices"):
nodelabels={}
l,nnodes=l.split()
while not l.startswith("*arcs"):
if l.startswith('#'):
l = lines.next()
l = l.lower()
continue
if l.startswith('*'):
l = lines.next()
l = l.lower()
continue
splitline=shlex.split(l)
#print splitline
id, label = splitline[0:2]
G.add_node(label)
nodelabels[id]=label
G.node_attr[label]={'id':id}
if len(splitline) > 2:
id,label,x,y=splitline[0:4]
G.node_attr[label]={'id':id,'x':x,'y':y}
extra_attr=zip(splitline[4::2],splitline[5::2])
#print extra_attr
G.node_attr[label].update(extra_attr)
l = lines.next()
l = l.lower()
if l.startswith("*arcs"):
for l in lines:
if not l: break
if l.startswith('#'): continue
splitline=shlex.split(l)
ui,vi,w=splitline[0:3]
u=nodelabels.get(ui,ui)
v=nodelabels.get(vi,vi)
edge_data={'value':float(w)}
extra_attr=zip(splitline[3::2],splitline[4::2])
edge_data.update(extra_attr)
if G.has_edge(u,v):
if G[u][v]['value'] > float(w):
G.add_edge(u,v,**edge_data)
else:
G.add_edge(u,v,**edge_data)
if not G.name:
raise Exception("No graph definition found")
if len(G.nodes()) == 0:
raise Exception("No graph definition found")
return G
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
arrowstyle='-|>',
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
connectionstyle : str, optional (default=None)
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See :py:class: `matplotlib.patches.ConnectionStyle` and
:py:class: `matplotlib.patches.FancyArrowPatch` for more info.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size` as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif np.alltrue([not is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must contain color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
msg = 'edge_color must be a color or list of one color per edge'
raise ValueError(msg)
if (not G.is_directed() or not arrows):
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values
# for each line in a LineCollection. It was fixed in matplotlib by
# r7184 and r7189 (June 6 2009). We should then not set the alpha
# value globally, since the user can instead provide per-edge alphas
# now. Only set it globally if provided as a scalar.
if isinstance(alpha, Number):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
arrow_colors = edge_colors
if arrow_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
else:
edge_cmap = plt.get_cmap() # default matplotlib colormap
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
arrow_color = None
line_width = None
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if cb.iterable(node_size): # many node sizes
src_node, dst_node = edgelist[i][:2]
index_node = nodelist.index(dst_node)
marker_size = node_size[index_node]
shrink_target = to_marker_edge(marker_size, node_shape)
else:
shrink_target = to_marker_edge(node_size, node_shape)
if arrow_colors is None:
arrow_color = edge_cmap(color_normal(edge_color[i]))
elif len(arrow_colors) > 1:
arrow_color = arrow_colors[i]
else:
arrow_color = arrow_colors[0]
if len(lw) > 1:
line_width = lw[i]
else:
line_width = lw[0]
arrow = FancyArrowPatch((x1, y1), (x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
zorder=1) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
plt.tick_params(
axis='both',
which='both',
bottom=False,
left=False,
labelbottom=False,
labelleft=False)
return arrow_collection
def write_gml(G, path):
"""
Write the graph G in GML format to the file or file handle path.
Examples
---------
>>> G=nx.path_graph(4)
>>> nx.write_gml(G,"test.gml")
path can be a filehandle or a string with the name of the file.
>>> fh=open("test.gml",'w')
>>> nx.write_gml(G,fh)
Filenames ending in .gz or .bz2 will be compressed.
>>> nx.write_gml(G,"test.gml.gz")
The output file will use the default text encoding on your system.
It is possible to write files in other encodings by opening
the file with the codecs module. See doc/examples/unicode.py
for hints.
>>> import codecs
>>> fh=codecs.open("test.gml",'w',encoding='iso8859-1')# use iso8859-1
>>> nx.write_gml(G,fh)
GML specifications indicate that the file should only use
7bit ASCII text encoding.iso8859-1 (latin-1).
Only a single level of attributes for graphs, nodes, and edges,
is supported.
"""
fh = _get_fh(path, mode='w')
# comments="#"
# pargs=comments+" "+' '.join(sys.argv)
# fh.write("%s\n" % (pargs))
# fh.write(comments+" GMT %s\n" % (time.asctime(time.gmtime())))
# fh.write(comments+" %s\n" % (G.name))
# check for attributes or assign empty dict
if hasattr(G, 'graph_attr'):
graph_attr = G.graph_attr
else:
graph_attr = {}
if hasattr(G, 'node_attr'):
node_attr = G.node_attr
else:
node_attr = {}
indent = 2 * ' '
count = iter(range(G.number_of_nodes()))
node_id = {}
fh.write("graph [\n")
# write graph attributes
for k, v in graph_attr.items():
if is_string_like(v):
v = '"' + v + '"'
fh.write(indent + "%s %s\n" % (k, v))
# write nodes
for n in G:
fh.write(indent + "node [\n")
# get id or assign number
if n in node_attr:
nid = node_attr[n].get('id', count.next())
else:
nid = count.next()
node_id[n] = nid
fh.write(2 * indent + "id %s\n" % nid)
fh.write(2 * indent + "label \"%s\"\n" % n)
if n in node_attr:
for k, v in node_attr[n].items():
if is_string_like(v): v = '"' + v + '"'
if k == 'id': continue
fh.write(2 * indent + "%s %s\n" % (k, v))
fh.write(indent + "]\n")
# write edges
for e in G.edges_iter():
if len(e) == 3:
u, v, d = e
# try to guess what is on the edge and do something reasonable
edgedata = {}
if d is None: # no data
pass
elif hasattr(d, 'iteritems'): # try dict-like
edgedata = dict(d.iteritems())
elif hasattr(d, '__iter__'): # some kind of container
edgedata['data'] = d.__repr__()
else: # something else - string, number
edgedata['data'] = d
else:
u, v = e
edgedata = {}
fh.write(indent + "edge [\n")
fh.write(2 * indent + "source %s\n" % node_id[u])
fh.write(2 * indent + "target %s\n" % node_id[v])
for k, v in edgedata.items():
if k == 'source': continue
if k == 'target': continue
if is_string_like(v): v = '"' + v + '"'
fh.write(2 * indent + "%s %s\n" % (k, v))
fh.write(indent + "]\n")
fh.write("]\n")
def draw_networkx_edges(G,
pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
Notes
-----
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
Yes, it is ugly but drawing proper arrows with Matplotlib this
way is tricky.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap
from matplotlib.collections import LineCollection
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width, )
else:
lw = width
if not is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif np.alltrue([not is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
edge_collection = LineCollection(
edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
arrow_collection = None
if G.is_directed() and arrows:
# a directed graph hack
# draw thick line segments at head end of edge
# waiting for someone else to implement arrows that will work
arrow_colors = edge_colors
a_pos = []
p = 1.0 - 0.25 # make head segment 25 percent of edge length
for src, dst in edge_pos:
x1, y1 = src
x2, y2 = dst
dx = x2 - x1 # x offset
dy = y2 - y1 # y offset
d = np.sqrt(float(dx**2 + dy**2)) # length of edge
if d == 0: # source and target at same position
continue
if dx == 0: # vertical edge
xa = x2
ya = dy * p + y1
if dy == 0: # horizontal edge
ya = y2
xa = dx * p + x1
else:
theta = np.arctan2(dy, dx)
xa = p * d * np.cos(theta) + x1
ya = p * d * np.sin(theta) + y1
a_pos.append(((xa, ya), (x2, y2)))
arrow_collection = LineCollection(
a_pos,
colors=arrow_colors,
linewidths=[4 * ww for ww in lw],
antialiaseds=(1, ),
transOffset=ax.transData,
)
arrow_collection.set_zorder(1) # edges go behind nodes
arrow_collection.set_label(label)
ax.add_collection(arrow_collection)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def union(G,H,create_using=None,rename=False,name=None):
""" Return the union of graphs G and H.
Graphs G and H must be disjoint, otherwise an exception is raised.
Parameters
----------
G,H : graph
A NetworkX graph
create_using : NetworkX graph
Use specified graph for result. Otherwise a new graph is created.
rename : bool (default=False)
Node names of G and H can be changed be specifying the tuple
rename=('G-','H-') (for example).
Node u in G is then renamed "G-u" and v in H is renamed "H-v".
name : string
Specify name for union graph
Notes
-----
To force a disjoint union with node relabeling, use
disjoint_union(G,H) or convert_node_labels_to integers().
"""
if name is None:
name="union( %s, %s )"%(G.name,H.name)
if create_using is None:
R=G.__class__()
else:
R=create_using
R.clear()
R.name=name
# rename graph to obtain disjoint node labels
# note that for objects w/o succinct __name__,
# the new labels can be quite verbose
# See also disjoint_union
if rename: # create new string labels
Gname={}
Hname={}
for v in G:
if is_string_like(v):
Gname[v]=rename[0]+v
else:
Gname[v]=rename[0]+repr(v)
for v in H:
if is_string_like(v):
Hname[v]=rename[1]+v
else:
Hname[v]=rename[1]+repr(v)
else:
Gname=dict( ((v,v) for v in G) )
Hname=dict( ((v,v) for v in H) )
# node name check
Hnames=set(Hname.values())
Gnames=set(Gname.values())
if Gnames & Hnames:
raise networkx.NetworkXError,\
"node sets of G and H are not disjoint. Use appropriate rename=('Gprefix','Hprefix')"
# node names OK, now build union
R.add_nodes_from(Gnames)
R.add_edges_from( ( (Gname[u],Gname[v],x) for u,v,x in G.edges_iter(data=True) ))
R.add_nodes_from(Hnames)
R.add_edges_from( ( (Hname[u],Hname[v],x) for u,v,x in H.edges_iter(data=True) ))
return R
def draw_networkx_labels(G, pos,
labels=None,
font_size=12,
font_color='k',
font_family='sans-serif',
font_weight='normal',
alpha=1.0,
bbox=None,
ax=None,
**kwds):
"""Draw node labels on the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
labels : dictionary, optional (default=None)
Node labels in a dictionary keyed by node of text labels
font_size : int
Font size for text labels (default=12)
font_color : string
Font color string (default='k' black)
font_family : string
Font family (default='sans-serif')
font_weight : string
Font weight (default='normal')
alpha : float
The text transparency (default=1.0)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
Returns
-------
dict
`dict` of labels keyed on the nodes
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if labels is None:
labels = dict((n, n) for n in G.nodes())
# set optional alignment
horizontalalignment = kwds.get('horizontalalignment', 'center')
verticalalignment = kwds.get('verticalalignment', 'center')
text_items = {} # there is no text collection so we'll fake one
for n, label in labels.items():
(x, y) = pos[n]
if not is_string_like(label):
label = str(label) # this will cause "1" and 1 to be labeled the same
t = ax.text(x, y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
alpha=alpha,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
transform=ax.transData,
bbox=bbox,
clip_on=True,
)
text_items[n] = t
return text_items
def generate_gml(G):
"""Generate a single entry of the graph G in GML format.
Parameters
----------
G : NetworkX graph
Returns
-------
lines: string
Lines in GML format.
Notes
-----
This implementation does not support all Python data types as GML
data. Nodes, node attributes, edge attributes, and graph
attributes must be either dictionaries or single stings or
numbers. If they are not an attempt is made to represent them as
strings. For example, a list as edge data
G[1][2]['somedata']=[1,2,3], will be represented in the GML file
as::
edge [
source 1
target 2
somedata "[1, 2, 3]"
]
"""
# recursively make dicts into gml brackets
dicttype = type({})
def listify(d, indent, indentlevel):
result = "[ \n"
dicttype = type({})
for k, v in d.items():
if type(v) == dicttype:
v = listify(v, indent, indentlevel + 1)
result += indentlevel * indent + "%s %s\n" % (k, v)
return result + indentlevel * indent + "]"
# check for attributes or assign empty dict
if hasattr(G, "graph_attr"):
graph_attr = G.graph_attr
else:
graph_attr = {}
if hasattr(G, "node_attr"):
node_attr = G.node_attr
else:
node_attr = {}
indent = 2 * " "
count = iter(range(len(G)))
node_id = {}
yield "graph ["
if G.is_directed():
yield indent + "directed 1"
# write graph attributes
for k, v in G.graph.items():
if type(v) == dicttype:
v = listify(v, indent, 2)
elif is_string_like(v):
v = '"%s"' % v
yield indent + "%s %s" % (k, v)
# write nodes
for n in G:
yield indent + "node ["
# get id or assign number
nid = G.node[n].get("id", next(count))
node_id[n] = nid
yield 2 * indent + "id %s" % nid
label = G.node[n].pop("label", n)
if is_string_like(label):
label = '"%s"' % label
yield 2 * indent + "label %s" % label
if n in G:
for k, v in G.node[n].items():
if k == "id":
continue
if type(v) == dicttype:
v = listify(v, indent, 3)
elif is_string_like(v):
v = '"%s"' % v
yield 2 * indent + "%s %s" % (k, v)
yield indent + "]"
# write edges
for u, v, edgedata in G.edges_iter(data=True):
# try to guess what is on the edge and do something reasonable
yield indent + "edge ["
yield 2 * indent + "source %s" % node_id[u]
yield 2 * indent + "target %s" % node_id[v]
for k, v in edgedata.items():
if k == "source":
continue
if k == "target":
continue
if type(v) == dicttype:
v = listify(v, indent, 3)
elif is_string_like(v):
v = '"%s"' % v
yield 2 * indent + "%s %s" % (k, v)
yield indent + "]"
yield "]"
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
arrowstyle='-|>',
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size' as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif np.alltrue([not is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must contain color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
msg = 'edge_color must be a color or list of one color per edge'
raise ValueError(msg)
if (not G.is_directed() or not arrows):
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values
# for each line in a LineCollection. It was fixed in matplotlib by
# r7184 and r7189 (June 6 2009). We should then not set the alpha
# value globally, since the user can instead provide per-edge alphas
# now. Only set it globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
arrow_colors = edge_colors
if arrow_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
else:
edge_cmap = plt.get_cmap() # default matplotlib colormap
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
arrow_color = None
line_width = None
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if cb.iterable(node_size): # many node sizes
src_node, dst_node = edgelist[i]
index_node = nodelist.index(dst_node)
marker_size = node_size[index_node]
shrink_target = to_marker_edge(marker_size, node_shape)
else:
shrink_target = to_marker_edge(node_size, node_shape)
if arrow_colors is None:
arrow_color = edge_cmap(color_normal(edge_color[i]))
elif len(arrow_colors) > 1:
arrow_color = arrow_colors[i]
else:
arrow_color = arrow_colors[0]
if len(lw) > 1:
line_width = lw[i]
else:
line_width = lw[0]
arrow = FancyArrowPatch((x1, y1), (x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
zorder=1) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
return arrow_collection
def generate_pajek(G):
"""Generate lines in Pajek graph format.
Parameters
----------
G : graph
A Networkx graph
References
----------
See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
for format information.
"""
if G.name == '':
name = 'NetworkX'
else:
name = G.name
# Apparently many Pajek format readers can't process this line
# So we'll leave it out for now.
# yield '*network %s'%name
# write nodes with attributes
yield '*vertices %s' % (G.order())
nodes = list(G)
# make dictionary mapping nodes to integers
nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
for n in nodes:
# copy node attributes and pop mandatory attributes
# to avoid duplication.
na = G.nodes.get(n, {}).copy()
x = na.pop('x', 0.0)
y = na.pop('y', 0.0)
id = int(na.pop('id', nodenumber[n]))
nodenumber[n] = id
shape = na.pop('shape', 'ellipse')
s = ' '.join(map(make_qstr, (id, n, x, y, shape)))
# only optional attributes are left in na.
for k, v in na.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
else:
warnings.warn('Node attribute %s is not processed. %s.' %
(k, 'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
yield s
# write edges with attributes
if G.is_directed():
yield '*arcs'
else:
yield '*edges'
for u, v, edgedata in G.edges(data=True):
d = edgedata.copy()
value = d.pop('weight', 1.0) # use 1 as default edge value
s = ' '.join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
for k, v in d.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
else:
warnings.warn('Edge attribute %s is not processed. %s.' %
(k, 'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
yield s
def generate_gml(G):
"""Generate a single entry of the nxgraph G in GML format.
Parameters
----------
G : NetworkX nxgraph
Returns
-------
lines: string
Lines in GML format.
Notes
-----
This implementation does not support all Python data types as GML
data. Nodes, node attributes, edge attributes, and nxgraph
attributes must be either dictionaries or single stings or
numbers. If they are not an attempt is made to represent them as
strings. For example, a list as edge data
G[1][2]['somedata']=[1,2,3], will be represented in the GML file
as::
edge [
source 1
target 2
somedata "[1, 2, 3]"
]
"""
# recursively make dicts into gml brackets
def listify(d,indent,indentlevel):
result='[ \n'
for k,v in d.items():
if type(v)==dict:
v=listify(v,indent,indentlevel+1)
result += (indentlevel+1)*indent + \
string_item(k,v,indentlevel*indent)+'\n'
return result+indentlevel*indent+"]"
def string_item(k,v,indent):
# try to make a string of the data
if type(v)==dict:
v=listify(v,indent,2)
elif is_string_like(v):
v='"%s"'%v
elif type(v)==bool:
v=int(v)
return "%s %s"%(k,v)
# check for attributes or assign empty dict
if hasattr(G,'graph_attr'):
graph_attr=G.graph_attr
else:
graph_attr={}
if hasattr(G,'node_attr'):
node_attr=G.node_attr
else:
node_attr={}
indent=2*' '
count=iter(range(len(G)))
node_id={}
yield "nxgraph ["
if G.is_directed():
yield indent+"directed 1"
# write nxgraph attributes
for k,v in G.graph.items():
if k == 'directed':
continue
yield indent+string_item(k,v,indent)
# write nodes
for n in G:
yield indent+"node ["
# get id or assign number
nid=G.node[n].get('id',next(count))
node_id[n]=nid
yield 2*indent+"id %s"%nid
label=G.node[n].get('label',n)
if is_string_like(label):
label='"%s"'%label
yield 2*indent+'label %s'%label
if n in G:
for k,v in G.node[n].items():
if k=='id' or k == 'label': continue
yield 2*indent+string_item(k,v,indent)
yield indent+"]"
# write edges
for u,v,edgedata in G.edges_iter(data=True):
yield indent+"edge ["
yield 2*indent+"source %s"%node_id[u]
yield 2*indent+"target %s"%node_id[v]
for k,v in edgedata.items():
if k=='source': continue
if k=='target': continue
yield 2*indent+string_item(k,v,indent)
yield indent+"]"
yield "]"
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
Notes
-----
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
Yes, it is ugly but drawing proper arrows with Matplotlib this
way is tricky.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap
from matplotlib.collections import LineCollection
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif np.alltrue([not is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must consist of either color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges')
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
arrow_collection = None
if G.is_directed() and arrows:
# a directed graph hack
# draw thick line segments at head end of edge
# waiting for someone else to implement arrows that will work
arrow_colors = edge_colors
a_pos = []
p = 1.0 - 0.25 # make head segment 25 percent of edge length
for src, dst in edge_pos:
x1, y1 = src
x2, y2 = dst
dx = x2 - x1 # x offset
dy = y2 - y1 # y offset
d = np.sqrt(float(dx**2 + dy**2)) # length of edge
if d == 0: # source and target at same position
continue
if dx == 0: # vertical edge
xa = x2
ya = dy * p + y1
if dy == 0: # horizontal edge
ya = y2
xa = dx * p + x1
else:
theta = np.arctan2(dy, dx)
xa = p * d * np.cos(theta) + x1
ya = p * d * np.sin(theta) + y1
a_pos.append(((xa, ya), (x2, y2)))
arrow_collection = LineCollection(a_pos,
colors=arrow_colors,
linewidths=[4 * ww for ww in lw],
antialiaseds=(1,),
transOffset=ax.transData,
)
arrow_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(arrow_collection)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def parse_pajek(lines):
"""Parse Pajek format graph from string or iterable.
Parameters
----------
lines : string or iterable
Data in Pajek format.
Returns
-------
G : NetworkX graph
See Also
--------
read_pajek()
"""
import shlex
# multigraph=False
if is_string_like(lines):
lines = iter(lines.split("\n"))
lines = iter([line.rstrip("\n") for line in lines])
G = nx.MultiDiGraph() # are multiedges allowed in Pajek? assume yes
labels = [] # in the order of the file, needed for matrix
while lines:
try:
l = next(lines)
except: # EOF
break
if l.lower().startswith("*network"):
try:
label, name = l.split(None, 1)
except ValueError:
# Line was not of the form: *network NAME
pass
else:
G.graph["name"] = name
elif l.lower().startswith("*vertices"):
nodelabels = {}
l, nnodes = l.split()
for i in range(int(nnodes)):
l = next(lines)
try:
splitline = [x.decode("utf-8") for x in shlex.split(make_str(l).encode("utf-8"))]
except AttributeError:
splitline = shlex.split(str(l))
id, label = splitline[0:2]
labels.append(label)
G.add_node(label)
nodelabels[id] = label
G.node[label] = {"id": id}
try:
x, y, shape = splitline[2:5]
G.node[label].update({"x": float(x), "y": float(y), "shape": shape})
except:
pass
extra_attr = zip(splitline[5::2], splitline[6::2])
G.node[label].update(extra_attr)
elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
if l.lower().startswith("*edge"):
# switch from multidigraph to multigraph
G = nx.MultiGraph(G)
if l.lower().startswith("*arcs"):
# switch to directed with multiple arcs for each existing edge
G = G.to_directed()
for l in lines:
try:
splitline = [x.decode("utf-8") for x in shlex.split(make_str(l).encode("utf-8"))]
except AttributeError:
splitline = shlex.split(str(l))
if len(splitline) < 2:
continue
ui, vi = splitline[0:2]
u = nodelabels.get(ui, ui)
v = nodelabels.get(vi, vi)
# parse the data attached to this edge and put in a dictionary
edge_data = {}
try:
# there should always be a single value on the edge?
w = splitline[2:3]
edge_data.update({"weight": float(w[0])})
except:
pass
# if there isn't, just assign a 1
# edge_data.update({'value':1})
extra_attr = zip(splitline[3::2], splitline[4::2])
edge_data.update(extra_attr)
# if G.has_edge(u,v):
# multigraph=True
G.add_edge(u, v, **edge_data)
elif l.lower().startswith("*matrix"):
G = nx.DiGraph(G)
adj_list = (
(labels[row], labels[col], {"weight": int(data)})
for (row, line) in enumerate(lines)
for (col, data) in enumerate(line.split())
if int(data) != 0
)
G.add_edges_from(adj_list)
return G
def draw_networkx_edge_labels(G, pos,
edge_labels=None,
label_pos=0.5,
font_size=10,
font_color='k',
font_family='sans-serif',
font_weight='normal',
alpha=1.0,
bbox=None,
ax=None,
rotate=True,
**kwds):
"""Draw edge labels.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
alpha : float
The text transparency (default=1.0)
edge_labels : dictionary
Edge labels in a dictionary keyed by edge two-tuple of text
labels (default=None). Only labels for the keys in the dictionary
are drawn.
label_pos : float
Position of edge label along edge (0=head, 0.5=center, 1=tail)
font_size : int
Font size for text labels (default=12)
font_color : string
Font color string (default='k' black)
font_weight : string
Font weight (default='normal')
font_family : string
Font family (default='sans-serif')
bbox : Matplotlib bbox
Specify text box shape and colors.
clip_on : bool
Turn on clipping at axis boundaries (default=True)
Returns
-------
dict
`dict` of labels keyed on the edges
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_labels()
"""
try:
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edge_labels is None:
labels = {(u, v): d for u, v, d in G.edges(data=True)}
else:
labels = edge_labels
text_items = {}
for (n1, n2), label in labels.items():
(x1, y1) = pos[n1]
(x2, y2) = pos[n2]
(x, y) = (x1 * label_pos + x2 * (1.0 - label_pos),
y1 * label_pos + y2 * (1.0 - label_pos))
if rotate:
angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360 # degrees
# make label orientation "right-side-up"
if angle > 90:
angle -= 180
if angle < - 90:
angle += 180
# transform data coordinate angle to screen coordinate angle
xy = np.array((x, y))
trans_angle = ax.transData.transform_angles(np.array((angle,)),
xy.reshape((1, 2)))[0]
else:
trans_angle = 0.0
# use default box of white with white border
if bbox is None:
bbox = dict(boxstyle='round',
ec=(1.0, 1.0, 1.0),
fc=(1.0, 1.0, 1.0),
)
if not is_string_like(label):
label = str(label) # this will cause "1" and 1 to be labeled the same
# set optional alignment
horizontalalignment = kwds.get('horizontalalignment', 'center')
verticalalignment = kwds.get('verticalalignment', 'center')
t = ax.text(x, y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
alpha=alpha,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
rotation=trans_angle,
transform=ax.transData,
bbox=bbox,
zorder=1,
clip_on=True,
)
text_items[(n1, n2)] = t
return text_items
def label(x):
if is_string_like(x):
name = prefix + x
else:
name = prefix + repr(x)
return name
def make_str(t):
if is_string_like(t): return t
return str(t)
def parse_pajek(lines):
"""Parse Pajek format graph from string or iterable.
Parameters
----------
lines : string or iterable
Data in Pajek format.
Returns
-------
G : NetworkX graph
See Also
--------
read_pajek()
"""
import shlex
# multigraph=False
if is_string_like(lines): lines=iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G=nx.MultiDiGraph() # are multiedges allowed in Pajek? assume yes
while lines:
try:
l=next(lines)
except: #EOF
break
if l.lower().startswith("*network"):
try:
label, name = l.split()
except ValueError:
# Line was not of the form: *network NAME
pass
else:
G.graph['name'] = name
elif l.lower().startswith("*vertices"):
nodelabels={}
l,nnodes=l.split()
for i in range(int(nnodes)):
splitline=shlex.split(str(next(lines)))
id,label=splitline[0:2]
G.add_node(label)
nodelabels[id]=label
G.node[label]={'id':id}
try:
x,y,shape=splitline[2:5]
G.node[label].update({'x':float(x),
'y':float(y),
'shape':shape})
except:
pass
extra_attr=zip(splitline[5::2],splitline[6::2])
G.node[label].update(extra_attr)
elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
if l.lower().startswith("*edge"):
# switch from multidigraph to multigraph
G=nx.MultiGraph(G)
if l.lower().startswith("*arcs"):
# switch to directed with multiple arcs for each existing edge
G=G.to_directed()
for l in lines:
splitline=shlex.split(str(l))
if len(splitline)<2:
continue
ui,vi=splitline[0:2]
u=nodelabels.get(ui,ui)
v=nodelabels.get(vi,vi)
# parse the data attached to this edge and put in a dictionary
edge_data={}
try:
# there should always be a single value on the edge?
w=splitline[2:3]
edge_data.update({'weight':float(w[0])})
except:
pass
# if there isn't, just assign a 1
# edge_data.update({'value':1})
extra_attr=zip(splitline[3::2],splitline[4::2])
edge_data.update(extra_attr)
# if G.has_edge(u,v):
# multigraph=True
G.add_edge(u,v,**edge_data)
return G
def generate_pajek(G):
"""Generate lines in Pajek graph format.
Parameters
----------
G : graph
A Networkx graph
References
----------
See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
for format information.
"""
if G.name == '':
name = 'NetworkX'
else:
name = G.name
# Apparently many Pajek format readers can't process this line
# So we'll leave it out for now.
# yield '*network %s'%name
# write nodes with attributes
yield '*vertices %s' % (G.order())
nodes = list(G)
# make dictionary mapping nodes to integers
nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
for n in nodes:
# copy node attributes and pop mandatory attributes
# to avoid duplication.
na = G.nodes.get(n, {}).copy()
x = na.pop('x', 0.0)
y = na.pop('y', 0.0)
id = int(na.pop('id', nodenumber[n]))
nodenumber[n] = id
shape = na.pop('shape', 'ellipse')
s = ' '.join(map(make_qstr, (id, n, x, y, shape)))
# only optional attributes are left in na.
for k, v in na.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
else:
warnings.warn('Node attribute %s is not processed. %s.' %
(k,
'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
yield s
# write edges with attributes
if G.is_directed():
yield '*arcs'
else:
yield '*edges'
for u, v, edgedata in G.edges(data=True):
d = edgedata.copy()
value = d.pop('weight', 1.0) # use 1 as default edge value
s = ' '.join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
for k, v in d.items():
if is_string_like(v) and v.strip() != '':
s += ' %s %s' % (make_qstr(k), make_qstr(v))
else:
warnings.warn('Edge attribute %s is not processed. %s.' %
(k,
'Empty attribute' if is_string_like(v) else
'Non-string attribute'))
yield s
else:
node_attr={}
indent=2*' '
count=iter(range(len(G)))
node_id={}
yield "graph ["
if G.is_directed():
yield indent+"directed 1"
# write graph attributes
for k,v in G.graph.items():
<<<<<<< local
if type(v)==dicttype:
v=listify(v,indent,2)
elif is_string_like(v):
v='"%s"'%v
elif type(v) == bool:
v = int(v)
yield indent+"%s %s"%(k,v)
=======
yield indent+string_item(k,v,indent)
>>>>>>> other
# write nodes
for n in G:
yield indent+"node ["
# get id or assign number
nid=G.node[n].get('id',next(count))
node_id[n]=nid
yield 2*indent+"id %s"%nid
label=G.node[n].pop('label',n)
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 parse_pajek(lines):
"""Parse Pajek format graph from string or iterable.
Parameters
----------
lines : string or iterable
Data in Pajek format.
Returns
-------
G : NetworkX graph
See Also
--------
read_pajek()
"""
import shlex
# multigraph=False
if is_string_like(lines): lines = iter(lines.split('\n'))
lines = iter([line.rstrip('\n') for line in lines])
G = nx.MultiDiGraph() # are multiedges allowed in Pajek? assume yes
while lines:
try:
l = next(lines)
except: #EOF
break
if l.lower().startswith("*network"):
label, name = l.split()
G.name = name
if l.lower().startswith("*vertices"):
nodelabels = {}
l, nnodes = l.split()
for i in range(int(nnodes)):
splitline = shlex.split(str(next(lines)))
id, label = splitline[0:2]
G.add_node(label)
nodelabels[id] = label
G.node[label] = {'id': id}
try:
x, y, shape = splitline[2:5]
G.node[label].update({
'x': float(x),
'y': float(y),
'shape': shape
})
except:
pass
extra_attr = zip(splitline[5::2], splitline[6::2])
G.node[label].update(extra_attr)
if l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
if l.lower().startswith("*edge"):
# switch from multi digraph to multi graph
G = nx.MultiGraph(G)
for l in lines:
splitline = shlex.split(str(l))
ui, vi = splitline[0:2]
u = nodelabels.get(ui, ui)
v = nodelabels.get(vi, vi)
# parse the data attached to this edge and put in a dictionary
edge_data = {}
try:
# there should always be a single value on the edge?
w = splitline[2:3]
edge_data.update({'weight': float(w[0])})
except:
pass
# if there isn't, just assign a 1
# edge_data.update({'value':1})
extra_attr = zip(splitline[3::2], splitline[4::2])
edge_data.update(extra_attr)
# if G.has_edge(u,v):
# multigraph=True
G.add_edge(u, v, **edge_data)
# if not multigraph: # use Graph/DiGraph if no parallel edges
# if G.is_directed():
# G=nx.DiGraph(G)
# else:
# G=nx.Graph(G)
return G
def make_str(t):
if is_string_like(t): return t
return str(t)
def write_gml(G, path):
"""
Write the graph G in GML format to the file or file handle path.
Parameters
----------
path : filename or filehandle
The filename or filehandle to write. Filenames ending in
.gz or .gz2 will be compressed.
See Also
--------
read_gml, parse_gml
Notes
-----
The output file will use the default text encoding on your system.
It is possible to write files in other encodings by opening
the file with the codecs module. See doc/examples/unicode.py
for hints.
>>> G=nx.path_graph(4)
>>> import codecs
>>> fh=codecs.open('test.gml','w',encoding='iso8859-1')# use iso8859-1
>>> nx.write_gml(G,fh)
GML specifications indicate that the file should only use
7bit ASCII text encoding.iso8859-1 (latin-1).
For nested attributes for graphs, nodes, and edges you should
use dicts for the value of the attribute.
Examples
---------
>>> G=nx.path_graph(4)
>>> nx.write_gml(G,"test.gml")
path can be a filehandle or a string with the name of the file.
>>> fh=open("test.gml",'w')
>>> nx.write_gml(G,fh)
Filenames ending in .gz or .bz2 will be compressed.
>>> nx.write_gml(G,"test.gml.gz")
"""
fh = _get_fh(path, mode="w")
# check for attributes or assign empty dict
if hasattr(G, "graph_attr"):
graph_attr = G.graph_attr
else:
graph_attr = {}
if hasattr(G, "node_attr"):
node_attr = G.node_attr
else:
node_attr = {}
indent = 2 * " "
count = iter(range(G.number_of_nodes()))
node_id = {}
dicttype = type({})
# recursively make dicts into gml brackets
def listify(d, indent, indentlevel):
result = "[ \n"
dicttype = type({})
for k, v in d.iteritems():
if type(v) == dicttype:
v = listify(v, indent, indentlevel + 1)
result += indentlevel * indent + "%s %s\n" % (k, v)
return result + indentlevel * indent + "]"
fh.write("graph [\n")
if G.is_directed():
fh.write(indent + "directed 1\n")
# write graph attributes
for k, v in G.graph.items():
if is_string_like(v):
v = '"' + v + '"'
elif type(v) == dicttype:
v = listify(v, indent, 1)
fh.write(indent + "%s %s\n" % (k, v))
# write nodes
for n in G:
fh.write(indent + "node [\n")
# get id or assign number
nid = G.node[n].get("id", count.next())
node_id[n] = nid
fh.write(2 * indent + "id %s\n" % nid)
fh.write(2 * indent + 'label "%s"\n' % n)
if n in G:
for k, v in G.node[n].items():
if is_string_like(v):
v = '"' + v + '"'
if type(v) == dicttype:
v = listify(v, indent, 2)
if k == "id":
continue
fh.write(2 * indent + "%s %s\n" % (k, v))
fh.write(indent + "]\n")
# write edges
for u, v, edgedata in G.edges_iter(data=True):
# try to guess what is on the edge and do something reasonable
fh.write(indent + "edge [\n")
fh.write(2 * indent + "source %s\n" % node_id[u])
fh.write(2 * indent + "target %s\n" % node_id[v])
for k, v in edgedata.items():
if k == "source":
continue
if k == "target":
continue
if is_string_like(v):
v = '"' + v + '"'
if type(v) == dicttype:
v = listify(v, indent, 2)
fh.write(2 * indent + "%s %s\n" % (k, v))
fh.write(indent + "]\n")
fh.write("]")
def draw_networkx_labels(G, pos,
labels=None,
font_size=12,
font_color='k',
font_family='sans-serif',
font_weight='normal',
alpha=1.0,
bbox=None,
ax=None,
**kwds):
"""Draw node labels on the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
labels : dictionary, optional (default=None)
Node labels in a dictionary keyed by node of text labels
Node-keys in labels should appear as keys in `pos`.
If needed use: `{n:lab for n,lab in labels.items() if n in pos}`
font_size : int
Font size for text labels (default=12)
font_color : string
Font color string (default='k' black)
font_family : string
Font family (default='sans-serif')
font_weight : string
Font weight (default='normal')
alpha : float
The text transparency (default=1.0)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
Returns
-------
dict
`dict` of labels keyed on the nodes
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if labels is None:
labels = dict((n, n) for n in G.nodes())
# set optional alignment
horizontalalignment = kwds.get('horizontalalignment', 'center')
verticalalignment = kwds.get('verticalalignment', 'center')
text_items = {} # there is no text collection so we'll fake one
for n, label in labels.items():
(x, y) = pos[n]
if not is_string_like(label):
label = str(label) # this makes "1" and 1 labeled the same
t = ax.text(x, y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
alpha=alpha,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
transform=ax.transData,
bbox=bbox,
clip_on=True,
)
text_items[n] = t
plt.tick_params(
axis='both',
which='both',
bottom=False,
left=False,
labelbottom=False,
labelleft=False)
return text_items
def draw_edges(G,
pos,
ax,
edgelist=None,
width=1.0,
width_adjuster=50,
edge_color='k',
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
traversal_weight=1.0,
edge_delengthify=0.15,
arrows=True,
label=None,
zorder=1,
**kwds):
"""
Code cleaned-up version of networkx.draw_networkx_edges
New args:
width_adjuster - the line width is generated from the weight if present, use this adjuster to thicken the lines (multiply)
"""
if edgelist is None:
edgelist = G.edges()
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = [(pos[e[0]], pos[e[1]]) for e in edgelist]
new_ep = []
for e in edge_pos:
x, y = e[0]
dx, dy = e[1]
# Get edge length
elx = (dx - x) * edge_delengthify
ely = (dy - y) * edge_delengthify
x += elx
y += ely
dx -= elx
dy -= ely
new_ep.append(((x, y), (dx, dy)))
edge_pos = numpy.asarray(new_ep)
if not cb.iterable(width):
#print [G.get_edge_data(n[0], n[1])['weight'] for n in edgelist]
# see if I can find an edge attribute:
if 'weight' in G.get_edge_data(edgelist[0][0],
edgelist[0][1]): # Test an edge
lw = [
0.5 +
((G.get_edge_data(n[0], n[1])['weight'] - traversal_weight) *
width_adjuster) for n in edgelist
]
else:
lw = (width, )
else:
lw = width
if not is_string_like(edge_color) and cb.iterable(edge_color) and len(
edge_color) == len(edge_pos):
if numpy.alltrue([cb.is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
zorder=zorder)
edge_collection.set_label(label)
ax.add_collection(edge_collection)
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
# update view
'''
minx = numpy.amin(numpy.ravel(edge_pos[:,:,0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:,:,0]))
miny = numpy.amin(numpy.ravel(edge_pos[:,:,1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:,:,1]))
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim(corners)
ax.autoscale_view()
'''
return (edge_collection)
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 write_gml(G, path):
"""
Write the graph G in GML format to the file or file handle path.
Parameters
----------
path : filename or filehandle
The filename or filehandle to write. Filenames ending in
.gz or .gz2 will be compressed.
See Also
--------
read_gml, parse_gml
Notes
-----
The output file will use the default text encoding on your system.
It is possible to write files in other encodings by opening
the file with the codecs module. See doc/examples/unicode.py
for hints.
>>> G=nx.path_graph(4)
>>> import codecs
>>> fh=codecs.open('test.gml','w',encoding='iso8859-1')# use iso8859-1
>>> nx.write_gml(G,fh)
GML specifications indicate that the file should only use
7bit ASCII text encoding.iso8859-1 (latin-1).
For nested attributes for graphs, nodes, and edges you should
use dicts for the value of the attribute.
Examples
---------
>>> G=nx.path_graph(4)
>>> nx.write_gml(G,"test.gml")
path can be a filehandle or a string with the name of the file.
>>> fh=open("test.gml",'w')
>>> nx.write_gml(G,fh)
Filenames ending in .gz or .bz2 will be compressed.
>>> nx.write_gml(G,"test.gml.gz")
"""
fh = _get_fh(path, mode='w')
# check for attributes or assign empty dict
if hasattr(G, 'graph_attr'):
graph_attr = G.graph_attr
else:
graph_attr = {}
if hasattr(G, 'node_attr'):
node_attr = G.node_attr
else:
node_attr = {}
indent = 2 * ' '
count = iter(range(G.number_of_nodes()))
node_id = {}
dicttype = type({})
# recursively make dicts into gml brackets
def listify(d, indent, indentlevel):
result = '[ \n'
dicttype = type({})
for k, v in d.iteritems():
if type(v) == dicttype:
v = listify(v, indent, indentlevel + 1)
result += indentlevel * indent + "%s %s\n" % (k, v)
return result + indentlevel * indent + "]"
fh.write("graph [\n")
if G.is_directed():
fh.write(indent + "directed 1\n")
# write graph attributes
for k, v in G.graph.items():
if is_string_like(v):
v = '"' + v + '"'
elif type(v) == dicttype:
v = listify(v, indent, 1)
fh.write(indent + "%s %s\n" % (k, v))
# write nodes
for n in G:
fh.write(indent + "node [\n")
# get id or assign number
nid = G.node[n].get('id', count.next())
node_id[n] = nid
fh.write(2 * indent + "id %s\n" % nid)
fh.write(2 * indent + "label \"%s\"\n" % n)
if n in G:
for k, v in G.node[n].items():
if is_string_like(v): v = '"' + v + '"'
if type(v) == dicttype: v = listify(v, indent, 2)
if k == 'id': continue
fh.write(2 * indent + "%s %s\n" % (k, v))
fh.write(indent + "]\n")
# write edges
for u, v, edgedata in G.edges_iter(data=True):
# try to guess what is on the edge and do something reasonable
fh.write(indent + "edge [\n")
fh.write(2 * indent + "source %s\n" % node_id[u])
fh.write(2 * indent + "target %s\n" % node_id[v])
for k, v in edgedata.items():
if k == 'source': continue
if k == 'target': continue
if is_string_like(v): v = '"' + v + '"'
if type(v) == dicttype: v = listify(v, indent, 2)
fh.write(2 * indent + "%s %s\n" % (k, v))
fh.write(indent + "]\n")
fh.write("]")