importmatplotlib.pyplotaspltimportnumpyasnp# 创建数据x=np.linspace(0,10,100)y=np.sin(x)# 创建图表plt.figure(figsize=(10,6))plt.plot(x,y,label='sin(x)')# 添加注释plt.annotate('Maximum',xy=(np.pi/2,1),xytext=(np.pi/2,1.2),arrowprops=dict(facecolor='black',shrink=0.05),horizonta...
fig=plt.figure()ax=fig.add_subplot(111,projection='3d')x,y,z=np.random.rand(3,10)ax.scatter(x,y,z)ax.annotate('3D Bold Annotation - How2matplotlib.com',xy=(x[0],y[0]),xytext=(x[0]+0.1,y[0]+0.1),fontweight='bold',arrowprops=dict(arrowstyle='->',color='r'))plt.show(...
This is owed in part to the complicated and sometimes non-intuitive syntax requirements of SAS/GRAPH procedures such as GPLOT and GCHART, and in part to the fact that many figure specifications can only be satisfied through a hybrid of procedure syntax and Annotate functions. This paper explores...
plt.figure(figsize=(5,4),dpi=120) plt.plot([1,2,5],[7,8,9]) plt.annotate('basic unility of annotate', xy=(2, 8),#箭头末端位置 xytext=(1.0, 8.75),#文本起始位置 #箭头属性设置 arrowprops=dict(facecolor='#74C476', shrink=1,#箭头的收缩比 alpha=0.6, width=7,#箭身宽 headwidt...
fig=plt.figure() ax= fig.add_subplot(111) t= np.arange(0.0, 5.0, 0.01) s= np.cos(2*np.pi*t) line,= ax.plot(t, s, lw=2) ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05), ...
xycoords="axes fraction", textcoords="figure fraction", bbox=dict(box, fc="0.8"), arrowprops=dict(arrow)) plt.show() AI代码助手复制代码 结果如下: 可视化annotate()函数解析 函数功能:添加图形内容细节的指向型注释文本。 调用签名: plt.annotate(string, xy=(np.pi/2,1.0), xytext=((np.pi/...
figure; x=0:0.1:6; y=sin(x); h=plot(x,y); obj1 = Annotate(gca, 'ellipse', [1.5,2.5], [-0.8,0.4]); obj2 = Annotate(gca, 'arrow', [x(1),x(10)]+0.2, [y(1),y(10)]); obj3 = Annotate(gca, 'doublearrow', [4,3], [-0.6,0.4], 'linestyle', '--'); obj4 =...
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fig = plt.figure() ax = fig.add_subplot(111, polar=True) r = np.arange(0,1,0.001) theta = 2 * 2*np.pi * r line, = ax.plot(theta, r, color='#ee8d18', lw=3) # 对索引为800处画一个圆点,并做注释 ind = 800 thisr, thistheta = r[ind], theta[ind] ...
fig = plt.figure() ax = fig.add_subplot(111) x = np.arange(0.0, 5.0, 0.01) y = np.cos(2*np.pi*x) ax.plot(x, y, lw = 2) ''' xy=(横坐标,纵坐标) 箭头尖端 xytext=(横坐标,纵坐标) 文字的坐标,指的是最左边的坐标