Matplotlib带箭头的线 使用Matplotlib绘制不同颜色的带箭头的线实例

import matplotlib.pyplot as plt
import numpy
import matplotlib.colors as colors
import matplotlib.cm as cmx

 _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)
  ]

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.jet
cNorm = 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.jet
cNorm = colors.Normalize(vmin=0, vmax=len(way))
scalarMap = cmx.ScalarMappable(norm=cNorm,cmap=cmap)

# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
plt.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 np
from matplotlib import pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch

# Use seaborn to change the default graphics to something nicer
import seaborn as sns
# And set a nice color palette
sns.set_color_codes('deep')

# Create the plot object
fig, ax = plt.subplots(figsize=(5, 4))
x = np.linspace(0, 1000)

# Add finishing constraint: x2 <= 100/2 - x1/2
plt.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 - x1
plt.plot(x, 30 - 2*x, linewidth=3, label='Second constraint')
plt.fill_between(x, 0, 100 - 2*x, alpha=0.1)

# Add non-negativity constraints
plt.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) pairs
pairs = [(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 variables
chairs, tables = np.hsplit(np.array(pairs), 2)

# Caculate the objective function at each pair
z =8*chairs + 9*tables

# Plot the results
plt.scatter(chairs, tables, c=z, cmap='jet', edgecolor='gray', alpha=0.5, label='Profit at each point', zorder=3)

# Colorbar
cb = plt.colorbar()
cb.set_label('Profit Colormap ($)')
#====================================================

# Labels and stuff
plt.xlabel('Package A')
plt.ylabel('Package B')
plt.xlim(-0.5, 20)
plt.ylim(-0.5, 20)
plt.legend()
fig01 = plt.figure()
plt.show()