phi = np.radians(angle) xx = [x + .5, x, x + .5 * np.cos(phi)] yy = [y, y, y + .5 * np.sin(phi)] ax.plot(xx, yy, lw=8, color='blue', solid_joinstyle=style) ax.plot(xx[1:], yy[1:], lw=1, color='black') ax.plot(xx[1::-1], yy[1::-1], lw=1,...
y1 = np.sin(x1) function1 = ax1.plot(x1, y1, 'b', label='Sine') # 指定x坐标和y坐标的数据,设定图线颜色为蓝色blue,图线标签为“Sine” x2 = np.linspace(0, 4*np.pi, 100) y2 = np.cos(x2) function2 = ax2.plot(x2, y2, 'r', label='Cosine') functions = function1 + ...
sns.图名(x=np.array, y=np.array[, ...])hue的意思是variable in data to map plot aspects to different colors。 举个例子,建立如下数据集 X = np.linspace(0, 20, 10) df = pd.DataFrame({"Input": X, "Linear": X, "Sin": np.sin(X)}) 1. 2. Input Linear Sin 0 0.000000 0.00000...
np.pi,200)x2=2*np.linspace(-np.pi,np.pi,300)y1=x1# linear liney2=x1**2# Quadratic liney3=x1**3# Cubic liney4=5*np.cos(x2)# cosin functiony5=10*np.sin(2*x2)# sin function#Fig.1fig,ax=plt.subplots(figsize=(12,8),dpi=100)#And dpi=100 increased the number of dots p...
(t,g) = sine_wave(f,overSampRate,phase,nCyl) """ fs = overSampRate*f # sampling frequency t = np.arange(0,nCyl*1/f-1/fs,1/fs) # time base g = np.sin(2*np.pi*f*t+phase) # replace with cos if a cosine wave is desired return (t,g) # return time base and sig...
Python绘制参数方程图 x = np.cos(3*t) y = np.sin(2*t) # 参数式 plt.plot(x, y, color='blue', linewidth...接下来绘制圆的参数方程 #半径 r = 2.0 # 圆心 a, b = (0., 0.)...另外一种绘制圆形的方法~ import numpy as np from matplotlib import pyplot as plt r=2.0 a,b=0.0,...
plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle="--") scatter([t,],[np.sin(t),], 50, color ='red') annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$', xy=(t, np.cos(t)), xycoords='data', ...
matplot()函数可以统一设置各点的格式,并自动选择合适的坐标轴范围,使图形中包含全部数据,例如: > y1=sin(x) > y2=cos(x) > y=cbind(y1,y2) > matplot...符号、线与坐标轴风格的选项 可以使用一些选项来制定R绘制的图形。在函数plot()和matplot()中,选项通常作为附加参数被调用。可选参数能以任意顺...
In the end, display the plots and exit. Open Compiler import matplotlib.pyplot as plt import numpy as np # generating some random data for plotting val = np.linspace(0, 10, 100) pl1 = np.sin(val) pl2 = np.cos(val) # Creating subplots fig, (axs1, axs2) = plt.subplots(2, 1,...
plt.plot(x,y,label= '$sin(x) + 1',color = 'red' , linewidth = 2) #用xy作图,设置x轴的标签,设置线条的颜色和粗细plt.plot(x, z, 'b--',label= '$\cos x^2+1') #用xz作图,设置线条的类型plt.xlabel('Time(s) ') # x标签plt ...