如何在python中绘制碎石图？[关闭]

我在矩阵上使用奇异矢量分解，并获得了U，S和Vt矩阵。在这一点上，我试图为保留的维数选择一个阈值。有人建议我看一下碎石图，但想知道如何用numpy绘制它。目前，我正在使用python中的numpy和scipy库执行以下操作：

U, S, Vt = svd(A)

有什么建议么？

这是一个示例，可以粘贴到IPython提示符并生成如下图像（它使用随机数据）：

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

#Make a random array and then make it positive-definite
num_vars = 6
num_obs = 9
A = np.random.randn(num_obs, num_vars)
A = np.asmatrix(A.T) * np.asmatrix(A)
U, S, V = np.linalg.svd(A) 
eigvals = S**2 / np.sum(S**2)  # NOTE (@amoeba): These are not PCA eigenvalues. 
                               # This question is about SVD.

fig = plt.figure(figsize=(8,5))
sing_vals = np.arange(num_vars) + 1
plt.plot(sing_vals, eigvals, 'ro-', linewidth=2)
plt.title('Scree Plot')
plt.xlabel('Principal Component')
plt.ylabel('Eigenvalue')
#I don't like the default legend so I typically make mine like below, e.g.
#with smaller fonts and a bit transparent so I do not cover up data, and make
#it moveable by the viewer in case upper-right is a bad place for it 
leg = plt.legend(['Eigenvalues from SVD'], loc='best', borderpad=0.3, 
                 shadow=False, prop=matplotlib.font_manager.FontProperties(size='small'),
                 markerscale=0.4)
leg.get_frame().set_alpha(0.4)
leg.draggable(state=True)
plt.show()