nx.draw_networkx_edgesdoes not draw arrows for self-loops。
所以当有向图包含自环时，传递给nx.draw_networkx_edges 的权重必须跳过自环权重。否则，权重与绘制的边缘不同步。
因此，如果你改变
weights = [G[u][v]['weight'] for u, v in G.edges()]
到
weights = [G[u][v]['weight'] for u, v in G.edges() if u != v]
然后
import networkx as nx
import matplotlib.pyplot as plt
u = ['SAC', 'SAC', 'SAC', 'SAC', 'SAC', 'SAC', 'SAC', 'REDES SOCIAIS', 'REDES SOCIAIS',
'REDES SOCIAIS', 'PROCON', 'PROCON', 'PROCON', 'BACEN', 'BACEN', 'BACEN', 'BACEN',
'OUVIDORIA', 'OUVIDORIA', 'OUVIDORIA', 'OUVIDORIA', 'OUVIDORIA', 'CHAT', 'CHAT',
'CHAT']
v = ['RECLAME AQUI', 'SAC', 'REDES SOCIAIS', 'PROCON', 'BACEN', 'OUVIDORIA', 'CHAT',
'RECLAME AQUI', 'SAC', 'REDES SOCIAIS', 'RECLAME AQUI', 'SAC', 'PROCON',
'RECLAME AQUI', 'SAC', 'BACEN', 'OUVIDORIA', 'RECLAME AQUI', 'SAC',
'REDES SOCIAIS', 'BACEN', 'OUVIDORIA', 'RECLAME AQUI', 'SAC', 'REDES SOCIAIS']
w = [437, 207, 13, 1, 7, 13, 2, 70, 10, 12, 5,
1, 2, 23, 1, 4, 2, 16, 2, 2, 2, 4, 4, 1, 1]
G = nx.DiGraph()
for ui, vi, wi in zip(u, v, w):
G.add_edges_from([(ui, vi)], weight=wi)
pos = nx.circular_layout(G)
edge_labels = dict([((u, v,), d['weight']) for u, v, d in G.edges(data=True)])
weights = [G[u][v]['weight'] for u, v in G.edges() if u != v]
weights = list(map(lambda x: (x - min(weights)) /
(max(weights) - min(weights)), weights))
weights = list(map(lambda x: (x * 4) + 1, weights))
i = 0
for u, v in G.edges():
if u != v:
print(u, v, G[u][v]['weight'], weights[i])
i += 1
fig = plt.figure(figsize=(25, 15))
plt.axis('off')
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
nx.draw_networkx_nodes(G, pos,
nodelist=G.nodes(),
node_color='r',
node_size=500)
nx.draw_networkx_edges(G, pos,
edgelist=G.edges(),
alpha=0.5, edge_color='#5cce40', width=weights)
nx.draw_networkx_labels(G, pos, font_size=16, font_color='white')
fig.set_facecolor("#262626")
plt.show()
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DiGraph 中箭头的形状和粗细目前由this code 设置。要将矩形“箭头”替换为尖箭头，需要将 nx.draw_networkx_edges 替换为自定义 draw_networkx_edges_with_arrows 函数：
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
def draw_networkx_edges_with_arrows(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,
arrow_width=1.0,
**kwds):
"""
Most of this code comes from https://github.com/networkx/networkx/blob/master/networkx/drawing/nx_pylab.py#L575, except that the arrow LineCollection
has been replaced by mpatches.Arrows below.
"""
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 matplotlib.patches as mpatches
import numpy
import itertools as IT
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 = G.edges()
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = numpy.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not cb.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 cb.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(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()
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), lwi, color in zip(edge_pos, lw, IT.cycle(arrow_colors)):
x1, y1 = src
x2, y2 = dst
dx = x2-x1 # x offset
dy = y2-y1 # y offset
d = numpy.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 = numpy.arctan2(dy, dx)
xa = p*d*numpy.cos(theta)+x1
ya = p*d*numpy.sin(theta)+y1
dx, dy = x2-xa, y2-ya
patch = mpatches.Arrow(xa, ya, dx, dy,
width=arrow_width,
color=color,
transform=ax.transData)
ax.add_patch(patch)
# 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
u = ['SAC', 'SAC', 'SAC', 'SAC', 'SAC', 'SAC', 'SAC', 'REDES SOCIAIS', 'REDES SOCIAIS',
'REDES SOCIAIS', 'PROCON', 'PROCON', 'PROCON', 'BACEN', 'BACEN', 'BACEN', 'BACEN',
'OUVIDORIA', 'OUVIDORIA', 'OUVIDORIA', 'OUVIDORIA', 'OUVIDORIA', 'CHAT', 'CHAT',
'CHAT']
v = ['RECLAME AQUI', 'SAC', 'REDES SOCIAIS', 'PROCON', 'BACEN', 'OUVIDORIA', 'CHAT',
'RECLAME AQUI', 'SAC', 'REDES SOCIAIS', 'RECLAME AQUI', 'SAC', 'PROCON',
'RECLAME AQUI', 'SAC', 'BACEN', 'OUVIDORIA', 'RECLAME AQUI', 'SAC',
'REDES SOCIAIS', 'BACEN', 'OUVIDORIA', 'RECLAME AQUI', 'SAC', 'REDES SOCIAIS']
w = [437, 207, 13, 1, 7, 13, 2, 70, 10, 12, 5,
1, 2, 23, 1, 4, 2, 16, 2, 2, 2, 4, 4, 1, 1]
G = nx.DiGraph()
for ui, vi, wi in zip(u, v, w):
G.add_edges_from([(ui, vi)], weight=wi)
pos = nx.circular_layout(G)
edge_labels = dict([((u, v,), d['weight']) for u, v, d in G.edges(data=True)])
weights = [G[u][v]['weight'] for u, v in G.edges()]
weights = np.log(weights)
weights = list(map(lambda x: (x - min(weights)) /
(max(weights) - min(weights)), weights))
weights = list(map(lambda x: (x * 10) + 1, weights))
fig = plt.figure(figsize=(25, 15))
plt.axis('off')
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
nx.draw_networkx_nodes(G, pos,
nodelist=G.nodes(),
node_color='r',
node_size=500)
draw_networkx_edges_with_arrows(G, pos,
width=weights, arrow_width=0.05,
alpha=0.5, edge_color='#5cce40')
nx.draw_networkx_labels(G, pos, font_size=16, font_color='white')
fig.set_facecolor("#262626")
plt.savefig('/tmp/out.pdf', format='pdf', facecolor=fig.get_facecolor(),
bbox_inches='tight')
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