python画出2行4列个子图-编程知识

python画出2行4列个子图

代码

import matplotlib.pyplot as plt# 数据
areas = [50, 80, 120]
shenzhen_prices = [1500, 2000, 2500]
guangzhou_prices = [1200, 1800, 2300]
zhengzhou_prices = [800, 1200, 1500]# 创建一个包含2行4列共8个子图的图表
fig, axs = plt.subplots(2, 4, figsize=(14, 8))def draw_three_linegraph(ax,x,S,H,U,x_begin,x_end,y_begin,y_end,x_label):# 创建折线图ax.plot(x, S, label='S', color='green', marker='^',linewidth=2.0)ax.plot(x, H, label='H', color='orange', marker='s',linewidth=2.0)ax.plot(x, U, label='U', color='blue', marker='o',linewidth=2.0)# 设置图表标题和轴标签ax.set_xlabel(x_label)ax.set_ylabel('Accuracy(%)')# 设置图例ax.legend()# 设置坐标轴范围ax.set_xlim(x_begin, x_end)ax.set_ylim(y_begin, y_end)# 显示网格线ax.grid(True)# 保存图表为图像文件（例如PNG）# plt.savefig('house_prices_plot.png')# 显示图表# plt.show()'''CUB第一个图'''
result_S2,result_H2,result_U2 = [69.72, 68.93, 70.3, 70.28, 69.72],[67.69, 67.72, 68.4, 67.94, 67.86],[65.78, 66.56, 66.6, 65.76, 66.09]
x_CUB = [0.3,0.4,0.5,0.6,0.7]
# x_CUB_begin, x_CUB_end = 0.2,0.9
x_CUB_begin, x_CUB_end = 0.3,0.7
# y_CUB_begin, y_CUB_end = 65, 72
y_CUB_begin, y_CUB_end = 64, 72
x_label = 'visual-semantic contrastive generation loss weight λ₁'
draw_three_linegraph(axs[0,0],x_CUB,result_S2,result_H2,result_U2,x_CUB_begin,x_CUB_end,y_CUB_begin,y_CUB_end,x_label)'''CUB第二个图'''
result_S2,result_H2,result_U2 = [68.39, 70.3, 69.6, 69.87, 69.0],[67.13, 68.4, 67.53, 67.64, 67.67],[65.92, 66.6, 65.58, 65.54, 66.39]
x_CUB = [0.5,1,1.5,2,2.5]
# 设置x和y的坐标轴范围
x_CUB_begin, x_CUB_end = 0.5,2.5
# x_CUB_begin, x_CUB_end = 0.5,2.5
y_CUB_begin, y_CUB_end = 64, 72
x_label = 'regressor loss weight λ₂'
draw_three_linegraph(axs[0,1],x_CUB,result_S2,result_H2,result_U2,x_CUB_begin,x_CUB_end,y_CUB_begin,y_CUB_end,x_label)'''CUB第三个图'''
result_S2,result_H2,result_U2 = [69.12, 68.66, 68.9, 70.3, 69.76],[67.24, 67.06, 67.14, 68.4, 67.13],[65.46, 65.54, 65.47, 66.6, 64.69]
x_CUB = [0.3,0.4,0.5,0.6,0.7]
# 设置x和y的坐标轴范围
x_CUB_begin, x_CUB_end = 0.3,0.7
y_CUB_begin, y_CUB_end = 64, 72
x_label = 'semantic fusion loss weight λ₃'
draw_three_linegraph(axs[0,2],x_CUB,result_S2,result_H2,result_U2,x_CUB_begin,x_CUB_end,y_CUB_begin,y_CUB_end,x_label)'''CUB第四个图'''
result_S2,result_H2,result_U2 = [68.41, 69.77, 70.3, 69.19, 70.0],[67.19, 67.9, 68.4, 67.17, 67.89],[66.02, 66.13, 66.6, 65.27, 65.91]
x_CUB = [0.1,0.2,0.3,0.4,0.5]
# 设置x和y的坐标轴范围
x_CUB_begin, x_CUB_end = 0.1,0.5
y_CUB_begin, y_CUB_end = 64, 72
x_label = 'discriminator loss weight λ₄'
draw_three_linegraph(axs[0,3],x_CUB,result_S2,result_H2,result_U2,x_CUB_begin,x_CUB_end,y_CUB_begin,y_CUB_end,x_label)'''FLO第一个图'''
resultS2,resultH2,resultU2 = [87.97, 86.9, 87.0, 88.4, 86.78, 86.4],[70.28, 70.08, 69.99, 71.27, 69.4, 68.98],[58.51, 58.72, 58.54, 59.7, 57.82, 57.4]
x_FLO = [0.3,0.4,0.5,0.6,0.7,0.8]
# 设置x和y的坐标轴范围
x_FLO_begin, x_FLO_end = 0.3,0.8
# y_FLO_begin, y_FLO_end = 40, 110
y_FLO_begin, y_FLO_end = 50, 100
x_label = 'visual-semantic contrastive generation loss weight λ₁'
draw_three_linegraph(axs[1,0],x_FLO,resultS2,resultH2,resultU2,x_FLO_begin,x_FLO_end,y_FLO_begin,y_FLO_end,x_label)'''FLO第二个图'''
resultS2,resultH2,resultU2 = [84.3, 86.46, 86.47, 86.27, 88.4, 87.19],[68.56, 70.23, 68.66, 68.68, 71.27, 69.84],[57.77, 59.13, 56.93, 57.05, 59.7, 58.25]
x_FLO = [14,15,16,17,18,19]
# 设置x和y的坐标轴范围
x_FLO_begin, x_FLO_end = 14,19
# y_FLO_begin, y_FLO_end = 40, 110
y_FLO_begin, y_FLO_end = 50, 100
x_label = 'regressor loss weight λ₂'
draw_three_linegraph(axs[1,1],x_FLO,resultS2,resultH2,resultU2,x_FLO_begin,x_FLO_end,y_FLO_begin,y_FLO_end,x_label)'''FLO第三个图'''
resultS2,resultH2,resultU2 = [87.36, 85.69, 88.4, 86.44, 86.36, 84.86],[70.36, 70.14, 71.27, 68.52, 68.93, 69.7],[58.9, 59.37, 59.7, 56.76, 57.36, 59.14]
x_FLO = [0.3,0.4,0.5,0.6,0.7,0.8]
# 设置x和y的坐标轴范围
x_FLO_begin, x_FLO_end = 0.3,0.8
# y_FLO_begin, y_FLO_end = 40, 110
y_FLO_begin, y_FLO_end = 50, 100
x_label = 'semantic fusion loss weight λ₃'
draw_three_linegraph(axs[1,2],x_FLO,resultS2,resultH2,resultU2,x_FLO_begin,x_FLO_end,y_FLO_begin,y_FLO_end,x_label)'''FLO第四个图'''
resultS2,resultH2,resultU2 = [87.38, 87.59, 88.4, 85.77, 86.74, 85.64],[70.72, 70.44, 71.27, 69.06, 70.3, 69.49],[59.39, 58.9, 59.7, 57.8, 59.1, 58.47]
x_FLO = [1,2,3,4,5,6]
# 设置x和y的坐标轴范围
x_FLO_begin, x_FLO_end = 1,6
# y_FLO_begin, y_FLO_end = 40, 110
y_FLO_begin, y_FLO_end = 50, 100
x_label = 'discriminator loss weight λ₄'
draw_three_linegraph(axs[1,3],x_FLO,resultS2,resultH2,resultU2,x_FLO_begin,x_FLO_end,y_FLO_begin,y_FLO_end,x_label)# 调整子图布局
plt.tight_layout()# 保存图表为图像文件（例如PNG）
plt.savefig('2_4_figure_2.0.png')
# 显示图表
plt.show()