周五的时候计算出来一条线路，但是计算出来的只是类似与

0->10->19->2->..0

这样的线路只有写代码的人才能看的懂无法直观的表达出来，让其它同事看的不清晰，所以考虑怎样直观的把线路图画出来。

&esp; 当然是考虑用matplotlib了，

导入相关的库

import matplotlib.pyplot as pltimport numpyimport matplotlib.colors as colorsimport matplotlib.cm as cmx

后面两个主要是用于处理颜色的。

准备数据

_locations = [ (4, 4), # depot (4, 4), # unload depot_prime (4, 4), # unload depot_second (4, 4), # unload depot_fourth (4, 4), # unload depot_fourth (4, 4), # unload depot_fifth (2, 0), (8, 0), # locations to visit (0, 1), (1, 1), (5, 2), (7, 2), (3, 3), (6, 3), (5, 5), (8, 5), (1, 6), (2, 6), (3, 7), (6, 7), (0, 8), (7, 8) ]

画图

plt.figure(figsize=(10, 10))p1 = [l[0] for l in _locations]p2 = [l[1] for l in _locations]plt.plot(p1[:6], p2[:6], 'g*', ms=20, label='depot')plt.plot(p1[6:], p2[6:], 'ro', ms=15, label='customer')plt.grid(True)plt.legend(loc='lower left')way = [[0, 12, 18, 17, 16, 4, 14, 10, 11, 13, 5], [0, 6, 9, 8, 20, 3], [0, 19, 21, 15, 7, 2]] # cmap = plt.cm.jetcNorm = colors.Normalize(vmin=0, vmax=len(way))scalarMap = cmx.ScalarMappable(norm=cNorm,cmap=cmap)for k in range(0, len(way)): way0 = way[k] colorVal = scalarMap.to_rgba(k) for i in range(0, len(way0)-1): start = _locations[way0[i]] end = _locations[way0[i+1]]# plt.arrow(start[0], start[1], end[0]-start[0], end[1]-start[1], length_includes_head=True,# head_width=0.2, head_length=0.3, fc='k', ec='k', lw=2, ls=lineStyle[k], color='red') plt.arrow(start[0], start[1], end[0]-start[0], end[1]-start[1], length_includes_head=True, head_width=0.2, lw=2, color=colorVal)plt.show()cmap = plt.cm.jetcNorm = colors.Normalize(vmin=0, vmax=len(way))scalarMap = cmx.ScalarMappable(norm=cNorm,cmap=cmap)

cmap可以理解为颜色库，cNorm设置颜色的范围，有几条线路就设置几种颜色，scalarMap颜色生成完毕。最后在绘图的时候，根据索引获得相应的颜色就可以了。

结果如下：

补充知识：Python包matplotlib绘图--如何标注某点--附代码

# -*- coding: utf-8 -*-import matplotlib as mplimport matplotlib.pyplot as pltimport numpy as npplt.style.use('classic')plt.rcParams['font.sans-serif'] = ['SimHei'] #解决中文显示plt.rcParams['axes.unicode_minus'] = False #解决符号无法显示x=np.array([1,2,3,4,5,6,7,8])y1=np.array([3,5,35,300,800,600,1200,4000])y2=np.array([8,14,94,703,1300,1660,2801,12768])fig1 = plt.figure()ax = plt.axes()ax.plot(x, y2,label='时间/秒')ax.set(xlabel='目标函数个数', ylabel='程序运行时间',title='多目标收敛速度')plt.hlines(703, 0, 4, colors='r', linestyle="--")plt.text(0, 703, "703")plt.hlines(1300, 0, 5, colors='g', linestyle="--")plt.text(0, 1300, "1300")# annotate plt.annotate("703秒", (4,703), xycoords='data', xytext=(4.2, 2000), arrowprops=dict(arrowstyle='->')) plt.annotate("94秒", (3,94), xycoords='data', xytext=(3.5, 2000), arrowprops=dict(arrowstyle='->')) plt.annotate("14秒", (2,14), xycoords='data', xytext=(2.5, 2000), arrowprops=dict(arrowstyle='->')) plt.annotate("8秒", (1,8), xycoords='data', xytext=(1.5, 2000), arrowprops=dict(arrowstyle='->')) plt.legend()plt.show()fig1.savefig('my_figure1.png')import numpy as npfrom matplotlib import pyplot as pltfrom matplotlib.path import Pathfrom matplotlib.patches import PathPatch# Use seaborn to change the default graphics to something nicerimport seaborn as sns# And set a nice color palettesns.set_color_codes('deep')# Create the plot objectfig, ax = plt.subplots(figsize=(5, 4))x = np.linspace(0, 1000)# Add finishing constraint: x2 <= 100/2 - x1/2plt.plot(x, 50/4 - 3*x/4, linewidth=3, label='First constraint')plt.fill_between(x, 0, 100/2 - x/2, alpha=0.1)# Add carpentry constraint: x2 <= 80 - x1plt.plot(x, 30 - 2*x, linewidth=3, label='Second constraint')plt.fill_between(x, 0, 100 - 2*x, alpha=0.1)# Add non-negativity constraintsplt.plot(np.zeros_like(x), x, linewidth=3, label='$x$ Sign restriction')plt.plot(x, np.zeros_like(x), linewidth=3, label='$y$ Sign restriction')#====================================================# This part is different from giapetto_feasible.py# Plot the possible (x1, x2) pairspairs = [(x, y) for x in np.arange(101) for y in np.arange(101) if (300*x + 400*y) <= 5000 and (200*x + 100*y) <= 3000]# Split these into our variableschairs, tables = np.hsplit(np.array(pairs), 2)# Caculate the objective function at each pairz =8*chairs + 9*tables# Plot the resultsplt.scatter(chairs, tables, c=z, cmap='jet', edgecolor='gray', alpha=0.5, label='Profit at each point', zorder=3)# Colorbarcb = plt.colorbar()cb.set_label('Profit Colormap ($)')#====================================================# Labels and stuffplt.xlabel('Package A')plt.ylabel('Package B')plt.xlim(-0.5, 20)plt.ylim(-0.5, 20)plt.legend()fig01 = plt.figure()plt.show()

以上这篇使用Matplotlib绘制不同颜色的带箭头的线实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。