如何在Python的SciPy中更改稀疏矩阵中的元素？

我构建了一个小代码，我想用它来解决涉及大型稀疏矩阵的特征值问题。它工作正常，我现在要做的就是将稀疏矩阵中的一些元素设置为零，即最顶行中的元素（对应于实现边界条件）。我可以调整下面的列向量（C0、C1 和 C2）来实现这一点。不过我想知道是否有更直接的方法。显然，NumPy 索引不适用于 SciPy 的稀疏包。

import scipy.sparse as sp
import scipy.sparse.linalg  as la
import numpy as np
import matplotlib.pyplot as plt

#discretize x-axis
N = 11
x = np.linspace(-5,5,N)
print(x)
V = x * x / 2
h = len(x)/(N)
hi2 = 1./(h**2)
#discretize Schroedinger Equation, i.e. build 
#banded matrix from difference equation
C0 = np.ones(N)*30. + V
C1 = np.ones(N) * -16.
C2 = np.ones(N) * 1.
diagonals = np.array([-2,-1,0,1,2])
H = sp.spdiags([C2, C1, C0,C1,C2],[-2,-1,0,1,2], N, N)
H *= hi2 * (- 1./12.) * (- 1. / 2.)
#solve for eigenvalues
EV = la.eigsh(H,return_eigenvectors = False)

#check structure of H
plt.figure()
plt.spy(H)
plt.show()

This is a visualisation of the matrix that is build by the code above. I want so set the elements in the first row zero.

根据评论中的建议，我将发布我为自己的问题找到的答案。 SciPy 的稀疏包中有几个矩阵类，列出了它们here http://docs.scipy.org/doc/scipy/reference/sparse.html。人们可以将稀疏矩阵从一类转换为另一类。因此，对于我需要做的事情，我选择将稀疏矩阵转换为 csr_matrix 类，只需通过

H = sp.csr_matrix(H)

然后我可以使用常规 NumPy 表示法将第一行中的元素设置为 0：

H[0,0] = 0
H[0,1] = 0
H[0,2] = 0

为了完整起见，我在下面发布了完整修改的代码片段。

#SciPy Sparse linear algebra takes care of sparse matrix computations
#http://docs.scipy.org/doc/scipy/reference/sparse.linalg.html
import scipy.sparse as sp
import scipy.sparse.linalg  as la

import numpy as np
import matplotlib.pyplot as plt

#discretize x-axis
N = 1100
x = np.linspace(-100,100,N)
V = x * x / 2.
h = len(x)/(N)
hi2 = 1./(h**2)

#discretize Schroedinger Equation, i.e. build 
#banded matrix from difference equation
C0 = np.ones(N)*30. + V
C1 = np.ones(N) * -16.
C2 = np.ones(N) * 1.

H = sp.spdiags([C2, C1, C0, C1, C2],[-2,-1,0,1,2], N, N)
H *= hi2 * (- 1./12.) * (- 1. / 2.)
H = sp.csr_matrix(H)
H[0,0] = 0
H[0,1] = 0
H[0,2] = 0

#check structure of H
plt.figure()
plt.spy(H)
plt.show()

EV = la.eigsh(H,return_eigenvectors = False)