def draw_components(g,clist,clabel="Componente",vlabel='',v_attrs={},elabel='',e_attrs={},vertexid_aslabel=False,
layout="circular",width=8,height=5,vsize=450,vshape='o',vfcolor='black'):
if len(clist) > 10:
return None
positions = draw_matplotlib.layout(g, seed=10, name=layout)
color_names = ["red", "cyan", "green", "lightgreen", "grey", "lightpink", "orange", "yellow", "violet", "lightblue"]
i = 1
for c in clist:
label = clabel + str(i)
draw_matplotlib.draw_jgrapht_vertices(
g,
positions=positions,
vertex_list=c,
vertex_color=color_names[i-1],
vertex_title=label,
vertex_size=vsize,
vertex_shape=vshape
)
i = i+1
draw_matplotlib.draw_jgrapht_edges(
g,
positions=positions,
edge_list=g.edges,
edge_color="orange"
)
vertex_labels = {}
if(vlabel!='' and v_attrs!={}):
for v in g.vertices:
if vlabel in v_attrs[v].keys():
vertex_labels[v] = v_attrs[v][vlabel]
elif vertexid_aslabel:
for v in g.vertices:
vertex_labels[v] = str(v)
edge_labels = {}
if(elabel!='' and e_attrs!={}):
for e in g.edges:
if elabel in e_attrs[e].keys():
edge_labels[e] = e_attrs[e][elabel]
draw_matplotlib.draw_jgrapht_vertex_labels(
g,
positions=positions,
labels=vertex_labels,
vertex_font_color=vfcolor
)
if not (e_attrs=={}):
draw_matplotlib.draw_jgrapht_edge_labels(
g,
positions=positions,
labels=edge_labels
)
plt.rcParams['figure.figsize'] = [width,height]
plt.show()
def draw_bipartite(g,p1,p2,vlabel='',v_attrs={},elabel='',e_attrs={},vertexid_aslabel=False):
positions = draw_matplotlib.layout(g, seed=10, name="circular")
draw_matplotlib.draw_jgrapht_vertices(
g,
positions=positions,
vertex_list=p1,
vertex_color="red",
vertex_title="Partição 1"
)
draw_matplotlib.draw_jgrapht_vertices(
g,
positions=positions,
vertex_list=p2,
vertex_color="blue",
vertex_title="Partição 2"
)
draw_matplotlib.draw_jgrapht_edges(
g,
positions=positions,
edge_list=g.edges,
edge_color="orange"
)
vertex_labels = {}
if(vlabel!='' and v_attrs!={}):
for v in g.vertices:
if vlabel in v_attrs[v].keys():
vertex_labels[v] = v_attrs[v][vlabel]
elif vertexid_aslabel:
for v in g.vertices:
vertex_labels[v] = str(v)
edge_labels = {}
if(elabel!='' and e_attrs!={}):
for e in g.edges:
if elabel in e_attrs[e].keys():
edge_labels[e] = e_attrs[e][elabel]
draw_matplotlib.draw_jgrapht_vertex_labels(
g,
positions=positions,
labels=vertex_labels,
vertex_font_color="white"
)
if not (e_attrs=={}):
draw_matplotlib.draw_jgrapht_edge_labels(
g,
positions=positions,
labels=edge_labels
)
plt.show()
def draw_graph(g,
layout="circular",
vertexid_aslabel=False,
edgeweight_aslabel=False,
vlabel='',
v_attrs={},
elabel='',
e_attrs={},
vertex_color='cyan',
vmap=None,
edge_color='orange',
emap=None,
vertex_font_color='black',
edge_font_color='gray',
axis=False,
width=8,
height=5,
vsize=450,
vshape='o',
vset=[],
vsetcolor=[],
vsetlabel=[],
eset=[],
esetcolor=[],
esetlabel=[]):
flatten = lambda t: [item for sublist in t for item in sublist]
positions = draw_matplotlib.layout(g, seed=10, name=layout)
if vset == []:
draw_matplotlib.draw_jgrapht_vertices(g,
positions=positions,
vertex_list=g.vertices,
vertex_color=vertex_color,
vertex_size=vsize,
vertex_cmap=vmap,
vertex_shape=vshape)
else:
notinvset = [v for v in g.vertices if v not in flatten(vset)]
for i in range(len(vset)):
draw_matplotlib.draw_jgrapht_vertices(g,
positions=positions,
vertex_list=vset[i],
vertex_color=vsetcolor[i],
vertex_title=vsetlabel[i],
vertex_size=vsize,
vertex_shape=vshape)
if notinvset != []:
draw_matplotlib.draw_jgrapht_vertices(g,
positions=positions,
vertex_list=notinvset,
vertex_color=vertex_color,
vertex_size=vsize,
vertex_cmap=vmap,
vertex_shape=vshape)
if eset == []:
draw_matplotlib.draw_jgrapht_edges(g,
positions=positions,
edge_list=g.edges,
edge_color=edge_color,
arrow_color=edge_color)
else:
notineset = [e for e in g.edges if e not in flatten(eset)]
for i in range(len(eset)):
draw_matplotlib.draw_jgrapht_edges(g,
positions=positions,
edge_list=eset[i],
edge_color=esetcolor[i],
arrow_color=esetcolor[i],
edge_title=esetlabel[i])
if notineset != []:
draw_matplotlib.draw_jgrapht_edges(g,
positions=positions,
edge_list=notineset,
edge_color=edge_color,
arrow_color=edge_color)
vertex_labels = {}
if (vlabel != '' and v_attrs != {}):
for v in g.vertices:
if vlabel in v_attrs[v].keys():
vertex_labels[v] = v_attrs[v][vlabel]
elif vertexid_aslabel:
for v in g.vertices:
vertex_labels[v] = str(v)
edge_labels = {}
if (elabel != '' and e_attrs != {}):
for e in g.edges:
if elabel in e_attrs[e].keys():
edge_labels[e] = e_attrs[e][elabel]
elif edgeweight_aslabel:
for e in g.edges:
edge_labels[e] = str(g.get_edge_weight(e))
draw_matplotlib.draw_jgrapht_vertex_labels(
g,
positions=positions,
labels=vertex_labels,
vertex_font_color=vertex_font_color)
if (not e_attrs == {}) or (edgeweight_aslabel):
draw_matplotlib.draw_jgrapht_edge_labels(
g,
positions=positions,
labels=edge_labels,
edge_font_color=edge_font_color)
pyplot.rcParams['figure.figsize'] = [width, height]
pyplot.show()
# %%
# Then, we execute the greedy coloring algorithm.
num_colors, color_map = greedy_dsatur(g)
print(num_colors)
print(color_map)
# %%
# We next plot the graph with the colors.
int_to_actual_color = {0: 'orangered', 1: 'lightsteelblue', 2: 'lightgreen'}
vertex_color = [int_to_actual_color[color_map[v]] for v in g.vertices]
vertex_labels = {v: str(v) for v in g.vertices}
import jgrapht.drawing.draw_matplotlib as drawing
import matplotlib.pyplot as plt
positions = drawing.layout(g, name="fruchterman_reingold", seed=17)
drawing.draw_jgrapht_vertices(g,
positions=positions,
vertex_color=vertex_color)
drawing.draw_jgrapht_vertex_labels(g,
positions=positions,
labels=vertex_labels)
drawing.draw_jgrapht_edges(g, positions=positions)
plt.show()
e4 = g.add_edge(0, 5)
e5 = g.add_edge(0, 6)
e6 = g.add_edge(0, 7)
e7 = g.add_edge(0, 8)
e8 = g.add_edge(0, 9)
# %%
# Compute the position of the vertices
positions = draw_matplotlib.layout(g, seed=10, name="fruchterman_reingold")
# %%
# Draw the graph
# Draw vertices with title
draw_matplotlib.draw_jgrapht_vertices(
g, positions=positions, node_list=(0, 1, 2, 3, 4), node_title="green nodes"
)
draw_matplotlib.draw_jgrapht_vertices(
g, positions=positions, node_list=(5, 6, 7, 8, 9), node_color="red", node_title="red nodes"
)
# Draw the edges with edge list, edge color and edge title
draw_matplotlib.draw_jgrapht_edges(
g,
positions=positions,
edge_list=(e0, e1, e2, e3, e4),
edge_color="orange",
edge_title="orange edges",
)
draw_matplotlib.draw_jgrapht_edges(
g,
# %%
# The result is an instance of :py:class:`.Clustering`.
# We convert the actual clusters to lists as they are returned as
# iterators.
#
clusters = []
for i in range(clustering.number_of_clusters()):
clusters.append(list(clustering.ith_cluster(i)))
print('# of clusters: {}'.format(len(clusters)))
print(clusters)
# %%
# Ploting the graph with a separate color per community.
import jgrapht.drawing.draw_matplotlib as drawing
import matplotlib.pyplot as plt
positions = drawing.layout(g, name="fruchterman_reingold", seed=17)
vertex_labels = {v:str(v) for v in g.vertices}
colors = ['red', 'yellow', 'green']
for cluster, color in zip(clusters, colors):
drawing.draw_jgrapht_vertices(g, positions=positions, vertex_list=cluster, vertex_color=color)
drawing.draw_jgrapht_vertex_labels(g, positions=positions, labels=vertex_labels)
drawing.draw_jgrapht_edges(g, positions=positions)
plt.show()
g.add_edge(i, 10)
print(g)
# %%
# Then, we execute the 2-approximation algorithm of R. Bar-Yehuda and S. Even.
weight, vertex_cover = vc.baryehuda_even(g)
# %%
# The result is a tuple which contains the weight and the vertex cover.
# Although it failed to find the optimum which is 1.0, the returned solution is
# at most twice the optimum.
#
print('Vertex cover weight: {}'.format(weight))
print('Vertex cover: {}'.format(vertex_cover))
# %%
# Ploting the graph using separate color for the vertices in the vertex cover.
positions = drawing.layout(g, name="fruchterman_reingold", seed=17)
drawing.draw_jgrapht_vertices(g, positions=positions, vertex_list=vertex_cover, vertex_color='red')
non_vertex_cover = g.vertices - vertex_cover
drawing.draw_jgrapht_vertices(g, positions=positions, vertex_list=non_vertex_cover, vertex_color='gray')
drawing.draw_jgrapht_edges(g, positions=positions)
plt.show()
e1 = g.add_edge(0, 1)
e2 = g.add_edge(0, 2)
e3 = g.add_edge(1, 3)
e4 = g.add_edge(2, 3)
e5 = g.add_edge(2, 4)
e4 = g.add_edge(3, 4)
# %%
# Compute the position of the vertices
positions = [(0, 0), (1, 0), (0, 1), (1, 1), (0.5, 2.0)]
# %%
# Draw the graph
# Draw the vertexs using the vertex list and vertex color
drawing.draw_jgrapht_vertices(g,
positions=positions,
vertex_list=(0, 1, 2, 3),
vertex_color="green")
# Draw the vertexs using the vertex list, vertex color and vertex shape
drawing.draw_jgrapht_vertices(g,
positions=positions,
vertex_list=[4],
vertex_color="red",
vertex_shape="^")
# Draw the edges using edge color
drawing.draw_jgrapht_edges(g, positions=positions, edge_color="orange")
plt.tight_layout()
plt.show()
# Then, we execute Prim's algorithm.
mst_weight, mst_edges = spanning.prim(g)
# %%
# The result is a tuple which contains the weight and the minimum spanning tree.
#
print('mst weight: {}'.format(mst_weight))
print('mst tree: {}'.format(mst_edges))
# %%
# Ploting the graph with highlighted the MST edges
#
positions = drawing.layout(g, name="fruchterman_reingold", seed=17)
drawing.draw_jgrapht_vertices(g, positions=positions)
non_mst_edges = g.edges - mst_edges
drawing.draw_jgrapht_edges(g,
positions=positions,
edge_list=mst_edges,
edge_color='blue',
edge_linewidth=3)
drawing.draw_jgrapht_edges(g,
positions=positions,
edge_list=non_mst_edges,
edge_linewidth=1)
plt.show()
e8 = g.add_edge(0, 8, weight=0.2)
e9 = g.add_edge(0, 9, weight=0.7)
# %%
# create list of edges depending on their weights
weight_large = [e for e in g.edges if g.get_edge_weight(e) > 0.5]
weight_small = [e for e in g.edges if g.get_edge_weight(e) <= 0.5]
# %%
# Compute the position of the vertices
positions = draw_matplotlib.layout(g, seed=10, name="fruchterman_reingold")
# %%
# Draw the graph
# Draw nodes
draw_matplotlib.draw_jgrapht_vertices(g, positions=positions)
# Draw the edges using edge list,edge color,edge title and line style
draw_matplotlib.draw_jgrapht_edges(
g,
positions=positions,
edge_list=weight_large,
edge_color="red",
edge_title="weight>0.5",
line_style="dashed",
)
draw_matplotlib.draw_jgrapht_edges(
g,
positions=positions,
edge_list=weight_small,
def draw_cut(g,cut=[],cutlabel='',vlabel='',vset=[],vsetlabel='',
v_attrs={},elabel='',e_attrs={},vertexid_aslabel=False,
edgeweight_aslabel=False,layout="circular",width=8,height=5,vsize=450,
cmap=None,vsetcolor="red"):
if cutlabel == '':
cutlabel = 'Edge cut'
positions = draw_matplotlib.layout(g, seed=10, name=layout)
notcut = [e for e in g.edges if e not in cut]
notvset = [v for v in g.vertices if v not in vset]
if not (notvset == []):
draw_matplotlib.draw_jgrapht_vertices(
g,
positions=positions,
vertex_list=notvset,
vertex_color="cyan",
vertex_size=vsize,
)
if vset != []:
draw_matplotlib.draw_jgrapht_vertices(
g,
positions=positions,
vertex_list=vset,
vertex_color=vsetcolor,
vertex_title=vsetlabel,
vertex_size=vsize,
vertex_cmap=cmap
)
if cut != []:
draw_matplotlib.draw_jgrapht_edges(
g,
positions=positions,
edge_list=cut,
edge_color="orange",
arrow_color="orange",
edge_title = cutlabel
)
draw_matplotlib.draw_jgrapht_edges(
g,
positions=positions,
edge_list=notcut,
edge_color="black",
arrow_color="black",
)
vertex_labels = {}
if(vlabel!='' and v_attrs!={}):
for v in g.vertices:
if vlabel in v_attrs[v].keys():
vertex_labels[v] = v_attrs[v][vlabel]
elif vertexid_aslabel:
for v in g.vertices:
vertex_labels[v] = str(v)
edge_labels = {}
if(elabel!='' and e_attrs!={}):
for e in g.edges:
if elabel in e_attrs[e].keys():
edge_labels[e] = e_attrs[e][elabel]
elif edgeweight_aslabel:
for e in g.edges:
edge_labels[e] = str(g.get_edge_weight(e))
draw_matplotlib.draw_jgrapht_vertex_labels(
g,
positions=positions,
labels=vertex_labels,
vertex_font_color="black"
)
if (not e_attrs=={}) or (edgeweight_aslabel):
draw_matplotlib.draw_jgrapht_edge_labels(
g,
positions=positions,
labels=edge_labels
)
plt.rcParams['figure.figsize'] = [width,height]
plt.show()