Find k eigenvalues and eigenvectors of the square matrix A.
Solves A * x[i] = w[i] * x[i], the standard eigenvalue problem for w[i] eigenvalues with corresponding eigenvectors x[i].
If M is specified, solves A * x[i] = w[i] * M * x[i], the generalized eigenvalue problem for w[i] eigenvalues with corresponding eigenvectors x[i]
Parameters :  A : An N x N matrix, array, sparse matrix, or LinearOperator representing
k : integer


Returns :  w : array
v : array

Other Parameters:  
M : An N x N matrix, array, sparse matrix, or LinearOperator representing
sigma : real or complex
v0 : array
ncv : integer
which : string [‘LM’  ‘SM’  ‘LR’  ‘SR’  ‘LI’  ‘SI’]
maxiter : integer
tol : float
return_eigenvectors : boolean
Minv : N x N matrix, array, sparse matrix, or linear operator
OPinv : N x N matrix, array, sparse matrix, or linear operator
OPpart : ‘r’ or ‘i’.


Raises :  ArpackNoConvergence :

See also
Notes
This function is a wrapper to the ARPACK [R59] SNEUPD, DNEUPD, CNEUPD, ZNEUPD, functions which use the Implicitly Restarted Arnoldi Method to find the eigenvalues and eigenvectors [R60].
References
[R59]  (1, 2) ARPACK Software, http://www.caam.rice.edu/software/ARPACK/ 
[R60]  (1, 2) R. B. Lehoucq, D. C. Sorensen, and C. Yang, ARPACK USERS GUIDE: Solution of Large Scale Eigenvalue Problems by Implicitly Restarted Arnoldi Methods. SIAM, Philadelphia, PA, 1998. 
Examples
Find 6 eigenvectors of the identity matrix:
>>> id = np.identity(13)
>>> vals, vecs = sp.sparse.linalg.eigs(id, k=6)
>>> vals
array([ 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j])
>>> vecs.shape
(13, 6)