def test_simplify_explicit_with_shorten(g):
"""
Simplify using explicit mapping
Simplify include:
1. generate simplify mapping
2. ensure the mapping output shorten name
3. map to new node
"""
output_png = os.path.join(ARTIFACTS_DIR, 'simplify_tree.png')
mapping = g.get_simplify_mapping(shorten=True)
assert 'start' in mapping.keys()
# mapping = g.get_simplify_mapping(shorten=False)
# assert '[1] start' in mapping.keys()
df = pd.read_csv(FIXTURE)
g_simplify = Graphitty(df,
id_col='ip',
beahivour_col='url',
ts_col='date',
node_mapping=mapping)
assert 'start' in g_simplify.G.nodes()
nx_tree = g_simplify.render_graph(filter_subgraph=True)
draw(nx_tree, output_png, show=False)
def test_name_collapse(g, g2):
combine_g = GraphCombiner(g, g2, split_weight=False)
name_mapping = combine_g.get_simplify_mapping()
g1_simplify = combine_g.remap_graph(g, name_mapping)
g2_simplify = combine_g.remap_graph(g2, name_mapping)
nx_graph1 = g1_simplify.render_graph()
output_png = os.path.join(
ARTIFACTS_DIR,
'name_shortened_1.png'
)
draw(nx_graph1, output_png, show=False)
assert 5 <= len(nx_graph1.nodes()) <= 25
nx_graph2 = g2_simplify.render_graph()
output_png2 = os.path.join(
ARTIFACTS_DIR,
'name_shortened_2.png'
)
draw(nx_graph2, output_png2, show=False)
assert 5 <= len(nx_graph2.nodes()) <= 25
# let's check that the name converge (nothing exotically different)
def get_node_names(G):
return set(nx.nodes(G))
same = get_node_names(nx_graph1) & get_node_names(nx_graph2)
different = get_node_names(nx_graph1) ^ get_node_names(nx_graph2)
# print "same={}".format(same)
# print "different={}".format(different)
assert len(same) >= 3
assert 1 <= len(different) <= 20
def draw_graph(G, layout='circo'):
G.graph['rankdir'] = 'LR'
G.graph['dpi'] = 120
G.graph['layout'] = layout
if layout not in {'circo', 'dot'}:
G.graph['ranksep'] = 3
G.graph['nodesep'] = 2.5
draw(G)
def graph(name, nodes, edges):
G = nx.DiGraph()
G.graph['dpi'] = 200
G.graph['label'] = "SBH Graph: {}".format(name)
G.add_nodes_from(nodes)
G.add_edges_from(edges)
draw(G)
def test_simplification(g):
output_png = os.path.join(ARTIFACTS_DIR, 'tree.png')
if os.path.isfile(output_png):
os.remove(output_png)
g_simplify = g.simplify()
nx_tree = g_simplify.render_graph(filter_subgraph=True)
draw(nx_tree, output_png, show=False)
def test_simplify_comparison(g, g2):
g = GraphCombiner(g, g2)
simplified_g = g.get_simplifed_combine_graph()
nx_combined = simplified_g.render_graph()
output_png = os.path.join(
ARTIFACTS_DIR,
'simplify_combined.png'
)
draw(nx_combined, output_png, show=False)
assert len(nx_combined.nodes()) < 25
def test_read_generate_graph(g):
if os.path.isfile(TEST_GRAPH_OUTPUT):
os.remove(TEST_GRAPH_OUTPUT)
nx_graph = g.render_graph(filter_subgraph=True)
draw(nx_graph, TEST_GRAPH_OUTPUT, show=False)
# some data is drawn
assert os.path.isfile(TEST_GRAPH_OUTPUT)
filesize = os.stat(TEST_GRAPH_OUTPUT).st_size
assert filesize > 1000
assert len(nx_graph.nodes()) > 5
assert nx.number_connected_components(nx_graph.to_undirected()) == 1
def render(self, path=None, save=True):
"""Draw the graph with nxpd."""
from nxpd import draw
try:
get_ipython
from nxpd import nxpdParams
nxpdParams['show'] = 'ipynb'
except NameError:
pass
nx_graph = self._convert_to_nx()
nx_graph.graph['dpi'] = 80
if save:
filename = os.path.abspath(os.path.join(path, "graph.pdf"))
draw(nx_graph, filename, format='pdf', show=False)
else:
return draw(nx_graph)
def draw_multiple_graphs(graphs):
"""
draws multiple networkx graphs with graphviz and put the generated
images in the same IPython notebook output cell.
"""
for graph in graphs:
display(Image(filename=draw(graph, show=False)))
def draw_graph(self, show=False, **attributes):
for node in self.graph:
for key, value in node._default_graph_attributes().items():
if not key in node._graph_attributes:
self.graph.nodes[node][key] = value
for key, value in attributes.items():
self.graph.graph[key] = value
return draw(self.graph, show=show)
def plot(self):
states = self._to_nx_state()
edges = self._to_nx_edge()
g = nx.DiGraph()
g.add_nodes_from(states)
g.add_edges_from(edges)
return draw(g, format = 'svg')
def drawIndividualGraphs(participants,connections):
index=1
for p in participants:
con = [c for c in connections if c[0] == p]
links = [c[1] for c in con]
links.append(p)
H = nx.DiGraph()
H.add_nodes_from(links, style='filled', fillcolor='black',
fontcolor='white', fontname='sans-serif', fontsize=24)
for c in con:
H.add_edge(c[0], c[1], label=c[2]['label'], style='dotted', fontcolor='red', fontsize=12,
fontname='sans-serif')
draw(H, filename='Resources/'+str(index)+'.svg')
index+=1
return index
def dag_drawer(dag, scale=0.7, filename=None, style='color'):
"""Plot the directed acyclic graph (dag) to represent operation dependencies
in a quantum circuit.
Args:
dag (DAGCircuit): The dag to draw.
scale (float): scaling factor
filename (str): file path to save image to (format inferred from name)
style (str): 'plain': B&W graph
'color' (default): color input/output/op nodes
Returns:
Ipython.display.Image: if in Jupyter notebook and not saving to file,
otherwise None.
Raises:
VisualizationError: when style is not recognized.
"""
G = copy.deepcopy(
dag.multi_graph) # don't modify the original graph attributes
G.graph['dpi'] = 100 * scale
if style == 'plain':
pass
elif style == 'color':
for node in G.nodes:
n = G.nodes[node]
if n['type'] == 'op':
n['label'] = str(n['name'])
n['color'] = 'blue'
n['style'] = 'filled'
n['fillcolor'] = 'lightblue'
if n['type'] == 'in':
n['label'] = n['name'][0] + '[' + str(n['name'][1]) + ']'
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'green'
if n['type'] == 'out':
n['label'] = n['name'][0] + '[' + str(n['name'][1]) + ']'
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'red'
for e in G.edges(data=True):
e[2]['label'] = e[2]['name'][0] + "[" + str(e[2]['name'][1]) + "]"
else:
raise VisualizationError("Unrecognized style for the dag_drawer.")
show = nxpd.nxpdParams['show']
if filename:
show = False
elif ('ipykernel' in sys.modules) and ('spyder' not in sys.modules):
show = 'ipynb'
else:
show = True
return nxpd.draw(G, filename=filename, show=show)
def draw_sentence(s, **kwds):
import nxpd
for node in s.nodes():
attr = s.node[node]
if 'word' not in attr:
attr['label'] = str(node)
elif attr['word'].startswith('_'):
attr['label'] = attr['pos'] + '_%s' % node
else:
attr['label'] = attr['word'] + '_%s' % node
attr['label'] = attr['label'].replace(":", "/")
for e1, e2 in s.edges():
attr = s.edge[e1][e2]
if 'deptype' in attr:
attr['label'] = attr['deptype']
else:
attr['label'] = 'NONE'
attr['label'] = attr['label'].replace(":", "/")
nxpd.draw(s, **kwds)
def draw_sentence(s, **kwds):
import nxpd
for node in s.nodes():
attr = s.node[node]
if 'word' not in attr:
attr['label'] = str(node)
elif attr['word'].startswith('_'):
attr['label'] = attr['pos'] + '_%s' % node
else:
attr['label'] = attr['word'] + '_%s' % node
attr['label'] = attr['label'].replace(":", "/")
for e1, e2 in s.edges():
attr = s.edge[e1][e2]
if 'deptype' in attr:
attr['label'] = attr['deptype']
else:
attr['label'] = 'NONE'
attr['label'] = attr['label'].replace(":", "/")
nxpd.draw(s, **kwds)
def save_visualizations(graphs: dict,
folder_path: str,
formats: list = None):
"""
:param graphs: Dictionary of pw_id (or 'input') --> networkx graph (preferably generated by visualize_output above)
:param folder_path:
:param formats: list of formats to output the viz in. Options: ['pdf','gv','png','svg']. Any subset of these
can be provided. If not provided, all formats are generated.
:return:
"""
if not formats:
formats = ['pdf', 'gv', 'png', 'svg']
folder_name = os.path.join(folder_path, 'euler_visualization')
mkdir_p(os.path.abspath(folder_name))
for pw_id, g in graphs.items():
for f in formats:
draw(g,
format=f,
filename='{}/pw-{}.{}'.format(folder_name, pw_id, f),
show='none')
def draw(self):
"""Draw the graph with nxpd."""
from nxpd import draw
try:
get_ipython
from nxpd import nxpdParams
nxpdParams['show'] = 'ipynb'
except NameError:
pass
G = self._convert_to_nx()
G.graph['dpi'] = 80
return draw(G)
def plot(self):
def sub(c, dic):
if dic.get(c) == None:
dic[c] = 1
return str(c)
else:
dic[c] = dic[c] + 1
return str(c) + '_' + str(dic[c])
print('function invoked')
graph = nx.Graph()
nodes = [self.root]
for node in nodes:
graph.add_node(node.data)
if node.parent != None:
graph.add_edge(node.parent.data, node.data)
if node.left != None:
nodes.append(node.left)
if node.right != None:
nodes.append(node.right)
draw(graph, 'ada.png')
def draw_graph(matrix, labels=ascii_lowercase, round_by=2):
G = nx.DiGraph()
G.graph['rankdir'] = 'LR'
for i in range(matrix.cols):
G.add_node(i, label=labels[i])
for i in range(matrix.cols):
for j in range(matrix.rows):
p = round(matrix[j, i], round_by)
if p > 0:
if p < 1:
label = str(p)
G.add_edge(i, j, label=label, color='grey55')
else:
G.add_edge(i, j)
return draw(G)
def plotConstraintGraph(self):
if self._probType != 2:
print("Constraint graph available only for binary constraints")
return nx.Graph()
else:
G = nx.Graph()
G.add_node(0)
draw(G, show = 'ipynb')
for v in range (0, self._genLength):
G.add_node(v)
for cons in self._constraints:
if cons.type() == "21":
elem = cons.getElem()
weight = cons.getWeights()
G.add_edge(elem, elem, label = str(weight), color = 'green')
if cons.type() == "221":
elems = cons.getElems()
weights = cons.getWeights()
G.add_edge(elems[0], elems[1], label = str(weights), color = 'blue')
if cons.type() == "222":
elems = cons.getElems()
weights = cons.getWeights()
G.add_edge(elems[0], elems[1], label = str(weights), color = 'red')
return G
def draw_net(g, dpi=300, **kwargs):
"""
Draw the network digram
:param g:
:param dpi:
:return:
"""
kwargs.setdefault('show', 'ipynb')
kwargs.setdefault('layout', 'neato')
# Set dpi
g.graph['dpi'] = dpi
# todo: if show=True this will return a string to the tmp location of the image
gviz = draw(g, **kwargs)
img = mpimg.imread(BytesIO(gviz.data))
return img
def view(self):
G = nx.DiGraph()
G.graph['rankdir'] = 'LR'
G.graph['dpi'] = 70
for i in range(len(self.T)):
if self.T[i].startswith('_'):
G.add_node(self.T[i],
label=' ',
shape='square',
style='filled',
fillcolor='black')
else:
G.add_node(self.T[i],
shape='square',
style='filled',
fillcolor='grey',
label=self.T[i].split('_')[0])
for i in range(len(self.P)):
label = self.m[i] if self.m[i] else ' '
if i == 0:
G.add_node(self.P[i],
label=label,
style='filled',
fillcolor='green')
elif i == len(self.P) - 1:
G.add_node(self.P[i],
label=label,
style='filled',
fillcolor='red')
else:
G.add_node(self.P[i], label=label)
for i in range(len(self.T)):
for j in range(len(self.P)):
#print(D_plus[i,j])
if self.I[i, j] == 1:
G.add_edge(self.P[j], self.T[i])
#print(D_plus[i,j])
if self.O[i, j] == 1:
G.add_edge(self.T[i], self.P[j])
return draw(G)
def render(self, show=None):
G = nx.DiGraph()
G.graph['dpi'] = 100
G.graph['rankdir'] = 'RL'
G.add_nodes_from(self.final_states, style='filled', shape='doublecircle')
G.add_nodes_from(self.states.difference(self.final_states), shape='circle')
G.add_nodes_from(["start"], shape='point', color='gray')
G.add_edges_from([("start", self.initial_state, {'color': 'gray'})])
edges = []
loops = defaultdict(set)
for o, trans in self.transitions.items():
for symbol, d in trans.items():
if o == d:
loops[o].add(symbol)
else:
edges.append((o, d, {'label': symbol}))
for o, symbols in loops.items():
edges.append((o, o, {'label': '\, '.join(sorted(symbols))}))
G.add_edges_from(edges)
return draw(G, show=show)
def render(self, show=None):
G = nx.DiGraph()
G.graph['dpi'] = 100
G.graph['rankdir'] = 'RL'
G.add_nodes_from(self.final_states,
style='filled',
shape='doublecircle')
G.add_nodes_from(self.states.difference(self.final_states),
shape='circle')
G.add_nodes_from(["start"], shape='point', color='gray')
G.add_edges_from([("start", self.initial_state, {'color': 'gray'})])
edges = []
loops = defaultdict(set)
for o, trans in self.transitions.items():
for symbol, d in trans.items():
if o == d:
loops[o].add(symbol)
else:
edges.append((o, d, {'label': symbol}))
for o, symbols in loops.items():
edges.append((o, o, {'label': '\, '.join(sorted(symbols))}))
G.add_edges_from(edges)
return draw(G, show=show)
def dag_drawer(dag, scale=0.7, filename=None, style='color'):
"""Plot the directed acyclic graph (dag) to represent operation dependencies
in a quantum circuit.
Note this function leverages
`pydot <https://github.com/erocarrera/pydot>`_ (via
`nxpd <https://github.com/chebee7i/nxpd`_) to generate the graph, which
means that having `Graphviz <https://www.graphviz.org/>`_ installed on your
system is required for this to work.
Args:
dag (DAGCircuit): The dag to draw.
scale (float): scaling factor
filename (str): file path to save image to (format inferred from name)
style (str): 'plain': B&W graph
'color' (default): color input/output/op nodes
Returns:
Ipython.display.Image: if in Jupyter notebook and not saving to file,
otherwise None.
Raises:
VisualizationError: when style is not recognized.
"""
G = copy.deepcopy(
dag.multi_graph) # don't modify the original graph attributes
G.graph['dpi'] = 100 * scale
if style == 'plain':
pass
elif style == 'color':
for node in G.nodes:
n = G.nodes[node]
if n['type'] == 'op':
n['label'] = n['name']
n['color'] = 'blue'
n['style'] = 'filled'
n['fillcolor'] = 'lightblue'
if n['type'] == 'in':
n['label'] = n['name']
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'green'
if n['type'] == 'out':
n['label'] = n['name']
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'red'
for e in G.edges(data=True):
e[2]['label'] = e[2]['name']
else:
raise VisualizationError("Unrecognized style for the dag_drawer.")
show = nxpd.nxpdParams['show']
if filename:
show = False
elif ('ipykernel' in sys.modules) and ('spyder' not in sys.modules):
show = 'ipynb'
else:
show = True
return nxpd.draw(G, filename=filename, show=show)
# Write index file
env = Environment(loader=FileSystemLoader('docs/templates'))
with open('docs/index.rst', 'w') as index:
resources = {}
for registry in REGISTRIES:
resources.update(registry)
try:
graph_filename = 'images/index.png'
graph, graph_registry = make_graph(
registry=resources,
ext_base=GRAPH_BASE,
)
nxpd.draw(
graph,
filename='docs/{}'.format(graph_filename),
show=False,
)
except:
print('Graphs will not be rendered in docs')
graph_filename = None
graph_registry = None
template = env.get_template('index.tpl')
index.write(template.render(
modules=modules,
graph=graph_filename,
))
# Write module files
for module in modules:
resources = modules[module]
def draw(self):
return draw(a.graph)
def dag_drawer(dag, scale=0.7, filename=None, style='color'):
"""Plot the directed acyclic graph (dag) to represent operation dependencies
in a quantum circuit.
Note this function leverages
`pydot <https://github.com/erocarrera/pydot>`_ (via
`nxpd <https://github.com/chebee7i/nxpd`_) to generate the graph, which
means that having `Graphviz <https://www.graphviz.org/>`_ installed on your
system is required for this to work.
Args:
dag (DAGCircuit): The dag to draw.
scale (float): scaling factor
filename (str): file path to save image to (format inferred from name)
style (str): 'plain': B&W graph
'color' (default): color input/output/op nodes
Returns:
Ipython.display.Image: if in Jupyter notebook and not saving to file,
otherwise None.
Raises:
VisualizationError: when style is not recognized.
ImportError: when nxpd or pydot not installed.
"""
try:
import nxpd
import pydot # pylint: disable=unused-import
except ImportError:
raise ImportError("dag_drawer requires nxpd, pydot, and Graphviz. "
"Run 'pip install nxpd pydot', and install graphviz")
G = dag.to_networkx()
G.graph['dpi'] = 100 * scale
if style == 'plain':
pass
elif style == 'color':
for node in G.nodes:
n = G.nodes[node]
n['label'] = node.name
if node.type == 'op':
n['color'] = 'blue'
n['style'] = 'filled'
n['fillcolor'] = 'lightblue'
if node.type == 'in':
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'green'
if node.type == 'out':
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'red'
for e in G.edges(data=True):
e[2]['label'] = e[2]['name']
else:
raise VisualizationError("Unrecognized style for the dag_drawer.")
if filename:
show = False
elif ('ipykernel' in sys.modules) and ('spyder' not in sys.modules):
show = 'ipynb'
else:
show = True
return nxpd.draw(G, filename=filename, show=show)
no edges between nodes in the same set and every node in one set is connected with one or more node
from the other set
"""
import networkx as nx
from networkx.algorithms import bipartite
import pygraphviz as pgv
from nxpd import draw, nxpdParams
nxpdParams['show'] = 'ipynb'
B = nx.Graph()
#B.add_nodes_from([1, 2, 3, 4, 5, 6], bipartite=0)
#B.add_nodes_from(['a', 'b', 'c', 'd', 'e', 'f'], bipartite=1)
B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'a'), (2, 'b'), (2, 'd'), (3, 'b'),
(3, 'd'), (4, 'c'), (4, 'd'), (4, 'e'), (4, 'f'), (5, 'c'),
(5, 'e'), (5, 'f'), (6, 'd')])
draw(B, layout='circo')
# color Left set with Red and Right set with blue
if bipartite.is_bipartite(B):
R_set, L_set = bipartite.sets(B)
for v in R_set:
B.node[v]['color'] = 'blue'
for v in L_set:
B.node[v]['color'] = 'red'
else:
print("This is not a bipartite graph")
# maximum weight matching between the left set and the right set
M = nx.max_weight_matching(B)
print(M)
for v1, v2 in M.items():
def draw_with_output(g, f):
print "Drawing: {}".format(f)
draw(g, f, show=False)
# -*- coding: utf-8 -*-
import os
import networkx as nx
from nxpd import draw
from socialgraph.socialgraph import subgraph_by_topic, elect_committee, paint_graph
MAX_EDGES = 300
TOPIC = 'Groceries'
wiki_file = os.path.join('..', 'datasets', 'wiki.graphml')
G = nx.read_graphml(wiki_file)
print("Full graph: {0} nodes {1} edges".format(len(G.nodes()), len(G.edges())))
# Generate smaller graph and override G
subnodes = G.nodes()[:MAX_EDGES]
G = G.subgraph(subnodes)
print("Subgraph: {0} nodes {1} edges".format(len(G.nodes()), len(G.edges())))
# Filter graph by subtopic
G = subgraph_by_topic(G, TOPIC)
draw(G)
committee = elect_committee(G, 20)
print("Committee:")
print({k: len(v) for k, v in committee.items()})
draw(paint_graph(G, committee))
def showg(self):
draw(self.graph, filename='testmarkovnx.png', show='ipynb', format='png')
file = open("testmarkovnx.png", "rb")
image = file.read()
self.graphw.value=image
def dag_drawer(dag, scale=0.7, filename=None, style='color'):
"""Plot the directed acyclic graph (dag) to represent operation dependencies
in a quantum circuit.
Note this function leverages
`pydot <https://github.com/erocarrera/pydot>`_ (via
`nxpd <https://github.com/chebee7i/nxpd`_) to generate the graph, which
means that having `Graphviz <https://www.graphviz.org/>`_ installed on your
system is required for this to work.
The current release of Graphviz can be downloaded here: <https://graphviz.gitlab.io/download/>.
Download the version of the sotware that matches your environment and follow the instructions to
install Graph Visualization Software (Graphviz) on your operating system.
Args:
dag (DAGCircuit): The dag to draw.
scale (float): scaling factor
filename (str): file path to save image to (format inferred from name)
style (str): 'plain': B&W graph
'color' (default): color input/output/op nodes
Returns:
Ipython.display.Image: if in Jupyter notebook and not saving to file,
otherwise None.
Raises:
VisualizationError: when style is not recognized.
ImportError: when nxpd or pydot not installed.
Example:
.. jupyter-execute::
%matplotlib inline
from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
from qiskit.dagcircuit import DAGCircuit
from qiskit.converters import circuit_to_dag
from qiskit.visualization import dag_drawer
q = QuantumRegister(3, 'q')
c = ClassicalRegister(3, 'c')
circ = QuantumCircuit(q, c)
circ.h(q[0])
circ.cx(q[0], q[1])
circ.measure(q[0], c[0])
circ.rz(0.5, q[1]).c_if(c, 2)
dag = circuit_to_dag(circ)
dag_drawer(dag)
"""
try:
import nxpd
import pydot # pylint: disable=unused-import
except ImportError:
raise ImportError("dag_drawer requires nxpd and pydot. "
"Run 'pip install nxpd pydot'.")
G = dag.to_networkx()
G.graph['dpi'] = 100 * scale
if style == 'plain':
pass
elif style == 'color':
for node in G.nodes:
n = G.nodes[node]
n['label'] = node.name
if node.type == 'op':
n['color'] = 'blue'
n['style'] = 'filled'
n['fillcolor'] = 'lightblue'
if node.type == 'in':
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'green'
if node.type == 'out':
n['color'] = 'black'
n['style'] = 'filled'
n['fillcolor'] = 'red'
for e in G.edges(data=True):
e[2]['label'] = e[2]['name']
else:
raise VisualizationError("Unrecognized style for the dag_drawer.")
if filename:
show = False
elif ('ipykernel' in sys.modules) and ('spyder' not in sys.modules):
show = 'ipynb'
else:
show = True
try:
return nxpd.draw(G, filename=filename, show=show)
except nxpd.pydot.InvocationException:
raise VisualizationError(
"dag_drawer requires GraphViz installed in the system. "
"Check https://www.graphviz.org/download/ for details on "
"how to install GraphViz in your system.")
import networkx as nx
import pygraphviz as pgv
from nxpd import draw, nxpdParams
nxpdParams['show'] = 'ipynb'
G = nx.DiGraph()
G.add_edge("a", "b")
G.add_edge("b", "c")
G.add_edge("c", "d")
G.add_edge("d", "e")
G.add_edge("e", "c")
G.add_edge("a", "d")
G.add_edge("b", "e")
draw(G, layout='circo')
draw(G, layout='dot')
draw(G, layout='neato')
G.add_edges_from([('a', 'b', {
'weight': 2,
'label': 2
}), ('a', 'c', {
'weight': 3,
'label': 3
}), ('a', 'd', {
'weight': 1,
'label': 1
}), ('a', 'e', {
'weight': 3,
'label': 3
}), ('b', 'c', {
'weight': 4,
'label': 4
}), ('c', 'd', {
'weight': 5,
'label': 5
}), ('d', 'e', {
'weight': 4,
'label': 4
}), ('e', 'a', {
'weight': 1,
'label': 1
})])
draw(G, layout='circo')
T = nx.minimum_spanning_tree(G)
for e in T.edges():
G[e[0]][e[1]]['color'] = 'blue'
draw(G, layout='circo')
from nxpd import nxpdParams import networkx as nx from nxpd import draw G = nx.DiGraph() G.graph['rankdir'] = 'LR' G.graph['dpi'] = 220 G.add_cycle(range(4)) G.add_node(0, color='red', style='filled', fillcolor='pink') G.add_node(1, shape='square') G.add_node(3, style='filled', fillcolor='#00ffff') G.add_edge(0, 1, color='red', style='dashed') G.add_edge(3, 3, label='a') draw(G)
