在作图过程中，需要绘制多个变量，但是每个变量的数量级不同，在一个坐标轴下作图导致曲线变化很难观察，这时就用到多个坐标轴。本文除了涉及多个坐标轴还包括Axisartist相关作图指令、做图中label为公式的表达方式、matplotlib中常用指令。
一、放一个官方例子先
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes import matplotlib.pyplot as plt import numpy as np fig = plt.figure(1) #定义figure，（1）中的1是什么 ax_cof = HostAxes(fig, [0, 0, 0.9, 0.9]) #用[left, bottom, weight, height]的方式定义axes，0 <= l,b,w,h <= 1 #parasite addtional axes, share x ax_temp = ParasiteAxes(ax_cof, sharex=ax_cof) ax_load = ParasiteAxes(ax_cof, sharex=ax_cof) ax_cp = ParasiteAxes(ax_cof, sharex=ax_cof) ax_wear = ParasiteAxes(ax_cof, sharex=ax_cof) #append axes ax_cof.parasites.append(ax_temp) ax_cof.parasites.append(ax_load) ax_cof.parasites.append(ax_cp) ax_cof.parasites.append(ax_wear) #invisible right axis of ax_cof ax_cof.axis[\'right\'].set_visible(False) ax_cof.axis[\'top\'].set_visible(False) ax_temp.axis[\'right\'].set_visible(True) ax_temp.axis[\'right\'].major_ticklabels.set_visible(True) ax_temp.axis[\'right\'].label.set_visible(True) #set label for axis ax_cof.set_ylabel(\'cof\') ax_cof.set_xlabel(\'Distance (m)\') ax_temp.set_ylabel(\'Temperature\') ax_load.set_ylabel(\'load\') ax_cp.set_ylabel(\'CP\') ax_wear.set_ylabel(\'Wear\') load_axisline = ax_load.get_grid_helper().new_fixed_axis cp_axisline = ax_cp.get_grid_helper().new_fixed_axis wear_axisline = ax_wear.get_grid_helper().new_fixed_axis ax_load.axis[\'right2\'] = load_axisline(loc=\'right\', axes=ax_load, offset=(40,0)) ax_cp.axis[\'right3\'] = cp_axisline(loc=\'right\', axes=ax_cp, offset=(80,0)) ax_wear.axis[\'right4\'] = wear_axisline(loc=\'right\', axes=ax_wear, offset=(120,0)) fig.add_axes(ax_cof) \'\'\' #set limit of x, y ax_cof.set_xlim(0,2) ax_cof.set_ylim(0,3) \'\'\' curve_cof, = ax_cof.plot([0, 1, 2], [0, 1, 2], label=\"CoF\", color=\'black\') curve_temp, = ax_temp.plot([0, 1, 2], [0, 3, 2], label=\"Temp\", color=\'red\') curve_load, = ax_load.plot([0, 1, 2], [1, 2, 3], label=\"Load\", color=\'green\') curve_cp, = ax_cp.plot([0, 1, 2], [0, 40, 25], label=\"CP\", color=\'pink\') curve_wear, = ax_wear.plot([0, 1, 2], [25, 18, 9], label=\"Wear\", color=\'blue\') ax_temp.set_ylim(0,4) ax_load.set_ylim(0,4) ax_cp.set_ylim(0,50) ax_wear.set_ylim(0,30) ax_cof.legend() #轴名称，刻度值的颜色 #ax_cof.axis[\'left\'].label.set_color(ax_cof.get_color()) ax_temp.axis[\'right\'].label.set_color(\'red\') ax_load.axis[\'right2\'].label.set_color(\'green\') ax_cp.axis[\'right3\'].label.set_color(\'pink\') ax_wear.axis[\'right4\'].label.set_color(\'blue\') ax_temp.axis[\'right\'].major_ticks.set_color(\'red\') ax_load.axis[\'right2\'].major_ticks.set_color(\'green\') ax_cp.axis[\'right3\'].major_ticks.set_color(\'pink\') ax_wear.axis[\'right4\'].major_ticks.set_color(\'blue\') ax_temp.axis[\'right\'].major_ticklabels.set_color(\'red\') ax_load.axis[\'right2\'].major_ticklabels.set_color(\'green\') ax_cp.axis[\'right3\'].major_ticklabels.set_color(\'pink\') ax_wear.axis[\'right4\'].major_ticklabels.set_color(\'blue\') ax_temp.axis[\'right\'].line.set_color(\'red\') ax_load.axis[\'right2\'].line.set_color(\'green\') ax_cp.axis[\'right3\'].line.set_color(\'pink\') ax_wear.axis[\'right4\'].line.set_color(\'blue\') plt.show()
该例子的作图结果为：
二、实际绘制
在实际使用中希望绘制的多变量数值如下表所示：
为了实现这个作图，经过反复修改美化，代码如下：
1.导入包
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes import matplotlib.pyplot as plt
2.导入数据
x = [\'ATL\',\'LAX\',\'CLT\',\'LAS\',\'MSP\',\'DTW\',\'PHX\',\'DCA\',\'SLC\',\'ORD\',\'DFW\',\'PHL\',\'PDX\',\'DEN\',\'IAH\',\'BOS\',\'SAN\',\'BWI\',\'MDW\',\'IND\'] k_in = [49.160,47.367,26.858,30.315,16.552,28.590,23.905,18.818,28.735,6.721,10.315,26.398,38.575,7.646,11.227,8.864,15.327,19.120,11.521,19.618] k_out = [38.024,19.974,25.011,22.050,30.108,18.327,20.811,28.464,23.72,8.470,4.119,10.000,25.158,7.851,10.450,11.130,15.441,7.519,20.819,32.825] p = [0.0537,0.0301,0.0306,0.0217,0.0229,0.0223,0.0218,0.0179,0.0155,0.0465,0.0419,0.0165,0.0091,0.0357,0.0232,0.0200,0.0129,0.0143,0.0113,0.0064] K = [4.6844,2.0296,1.5858,1.1347,1.0706,1.0442,0.9764,0.8447,0.8141,0.7066,0.6041,0.5990,0.5808,0.5534,0.5023,0.3992,0.3964,0.3799,0.3639,0.3331]
3.作图并保存，相关指令后有备注，可以帮助理解
fig = plt.figure(1) #定义figure
ax_k = HostAxes(fig, [0, 0, 0.9, 0.9]) #用[left, bottom, weight, height]的方式定义axes，0 <= l,b,w,h <= 1
#parasite addtional axes, share x
ax_p = ParasiteAxes(ax_k, sharex=ax_k)
ax_K = ParasiteAxes(ax_k, sharex=ax_k)
#append axes
ax_k.parasites.append(ax_p)
ax_k.parasites.append(ax_K)
ax_k.set_ylabel(\'$K_i^{in}\\;/\\;K_i^{out}$\')
ax_k.axis[\'bottom\'].major_ticklabels.set_rotation(45)
ax_k.set_xlabel(\'Airport\')
ax_k.axis[\'bottom\',\'left\'].label.set_fontsize(12) # 设置轴label的大小
ax_k.axis[\'bottom\'].major_ticklabels.set_pad(8) #设置x轴坐标刻度与x轴的距离，坐标轴刻度旋转会使label和坐标轴重合
ax_k.axis[\'bottom\'].label.set_pad(12) #设置x轴坐标刻度与x轴label的距离，label会和坐标轴刻度重合
ax_k.axis[:].major_ticks.set_tick_out(True) #设置坐标轴上刻度突起的短线向外还是向内
#invisible right axis of ax_k
ax_k.axis[\'right\'].set_visible(False)
ax_k.axis[\'top\'].set_visible(True)
ax_p.axis[\'right\'].set_visible(True)
ax_p.axis[\'right\'].major_ticklabels.set_visible(True)
ax_p.axis[\'right\'].label.set_visible(True)
ax_p.axis[\'right\'].major_ticks.set_tick_out(True)
ax_p.set_ylabel(\'${p_i}$\')
ax_p.axis[\'right\'].label.set_fontsize(13)
ax_K.set_ylabel(\'${K_i}$\')
K_axisline = ax_K.get_grid_helper().new_fixed_axis
ax_K.axis[\'right2\'] = K_axisline(loc=\'right\', axes=ax_K, offset=(60,0))
ax_K.axis[\'right2\'].major_ticks.set_tick_out(True)
ax_K.axis[\'right2\'].label.set_fontsize(13)
fig.add_axes(ax_k)
curve_k1, = ax_k.plot(list(range(20)), k_in, marker =\'v\',markersize=8,label=\"$K_i^{in}$\",alpha = 0.7)
curve_k2, = ax_k.plot(list(range(20)), k_out, marker =\'^\',markersize=8, label=\"$K_i^{out}$\",alpha = 0.7)
curve_p, = ax_p.plot(list(range(20)), p, marker =\'P\',markersize=8,label=\"${p_i}$\",alpha = 0.7)
curve_K, = ax_K.plot(list(range(20)), K, marker =\'o\',markersize=8, label=\"${K_i}$\",alpha = 0.7,linewidth=3)
plt.xticks(list(range(20)), x)
# ax_k.set_xticks(list(range(20)))
# ax_k.set_xticklabels(x)
ax_k.axis[\'bottom\'].major_ticklabels.set_rotation(45)
# ax_k.set_rotation(90)
# plt.xticks(list(range(20)), x, rotation = \'vertical\')
ax_p.set_ylim(0,0.06)
ax_K.set_ylim(0,5)
ax_k.legend(labelspacing = 0.4, fontsize = 10)
#轴名称，刻度值的颜色
ax_p.axis[\'right\'].label.set_color(curve_p.get_color()) # 坐标轴label的颜色
ax_K.axis[\'right2\'].label.set_color(curve_K.get_color())
ax_p.axis[\'right\'].major_ticks.set_color(curve_p.get_color()) # 坐标轴刻度小突起的颜色
ax_K.axis[\'right2\'].major_ticks.set_color(curve_K.get_color())
ax_p.axis[\'right\'].major_ticklabels.set_color(curve_p.get_color()) # 坐标轴刻度值的颜色
ax_K.axis[\'right2\'].major_ticklabels.set_color(curve_K.get_color())
ax_p.axis[\'right\'].line.set_color(curve_p.get_color()) # 坐标轴线的颜色
ax_K.axis[\'right2\'].line.set_color(curve_K.get_color())
plt.savefig(\'10.key metrics mapping.pdf\', bbox_inches=\'tight\', dpi=800)
plt.show()
4.绘制结果
PS
该作图是在Axisartist的基础上完成的，一些平时常用的绘制指令在此处是无用的。经过查找相关资料，https://www.osgeo.cn/matplotlib/tutorials/toolkits/axisartist.html 该网站可以提供一些用法的帮助。
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