ax1 = plt.axes(projection='3d') ax1.set_xlim(0, 5) # X轴,横向向右方向 ax1.set_ylim(5, 0) # Y轴,左向与X,Z轴互为垂直 ax1.set_zlim(0, 5) # 竖向为Z轴 color1 = ['r', 'g', 'b', 'k', 'm'] marker1 = ['o', 'v', '1', 's', 'H'] i = 0 for x in dot...
fig, (ax1, ax2) = plt.subplots(1, 2, sharey='all') fig.subplots_adjust(wspace=0.05) # 将用相同的绘图数据,在两个轴上绘制折线图 ax1.plot(pts,np.arange(0,30)) ax2.plot(pts,np.arange(0,30)) # 调整两个x轴的显示范围 ax2.set_xlim(0.78, 1.0) # outliers only ax1.set_xlim(0...
(15,7)) ax1 = plt.subplot(221) ax1.plot(t, signal1) ax1.set_ylim(-3, 3) ax1.set_xlim(0, view) plt.title('Signal, {0:.0f}, {1:.0f} Hz plus {2:.0f} Hz'.format(support*1000, f, f1)) ax2 = plt.subplot(222) ax2.set_xlim(0, 2*f2) ax2.plot(freq, SIGNAL1...
ax1 = fig.add_axes(rect1, aspect='auto') ax1.plot(x, y, lw=4, c='darkcyan') ax1.set_ylim(-1.2, 3.2) ax1.axvspan(x[20], x[35], ymin=0.05, ymax=0.55, lw=4, edgecolor='tomato', facecolor='white') ### ax2 ### x2 = x[20:35] y2 = y[20:35] ax2 = fig.ad...
ax1.set(xlim=(0, 10), ylim=(-2, 2), ###轴区间范围 xlabel='x', ylabel='sin(x)', ##标题 title='AX1'); ax2.set(xlim=(0, 10), ylim=(-2, 2), xlabel='x', ylabel='cos(x)', title='AX2'); 5.3 高级定制 显示标签 text ...
ax1.set_title('Linear VS Non-linear') ax1.set_ylim(0.0, 30.3) ax1.set_xlim(0.0, 2.7) ax1.grid(True) 动画模拟杆件在非线性分析工况中随着时序加载过程的大位移变化过程。 # Animation of second subplot. ax2 = pl.subplot(1, 3, 2) ...
ax1=fig.add_subplot(221),221里面前两个代表的是画布划分的行数和列数,公共分为4个子图,最后一个1是代表,现在选中第一个子图。 import matplotlib.gridspec as gridspec#调用网格 fig=plt.figure(num=1,figsize=(4,6))#创建画布 gs=gridspec.GridSpec(3,3)#设定网格 ...
ax_d.set_xlim(-1,1) plt.show() AI代码助手复制代码 刻度及标记 importmpl_toolkits.axisartist.axislinesasaxislines fig = plt.figure(1, figsize=(10,6)) fig.subplots_adjust(bottom=0.2)# 子图1ax1 = axislines.Subplot(fig,131) fig.add_subplot(ax1)# for axis in ax.axis.values():# axis...
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8], title='An Example Axes', ylabel='Y-Axis', xlabel='X-Axis') plt.show() 在处理复杂的绘图时,需要借助Axes,简单的可以使用下面的方法 绘制简单图形 importmatplotlib.pyplot as plt#图形输入值input_values = [1,2,3,4,5]#图形输出值squares = [1,4...
fig, (ax1) = plt.subplots(1,1, figsize=(12,5)) fig.suptitle('Piecewise Constant', fontsize=14) #画出样条回归的散点图 ax1.scatter(train_x, train_y, facecolor='None', edgecolor='k', alpha=0.3) ax1.plot(xp, pred2, c='b...