当前位置： 云海天教程网 > 技术新闻 > 编程最新 > 正文

python 用Matplotlib作图中有多个Y轴

  • 作者：佚名    来源： 网络转载
  • 时间：2020-11-28

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。

以上就是python 用Matplotlib作图中有多个Y轴的详细内容，更多关于python Matplotlib作图的资料请关注云海天教程其它相关文章！

原文链接：https://www.cnblogs.com/Big-Big-Watermelon/p/14051994.html