55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
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#!/usr/bin/env python
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import sys
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from numpy.testing import *
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set_package_path()
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from arpack.speigs import *
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restore_path()
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import numpy as N
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class TestEigs(NumpyTestCase):
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def test(self):
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maxn=15 # Dimension of square matrix to be solved
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# Use a PDP^-1 factorisation to construct matrix with known
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# eiegevalues/vectors. Used random eiegenvectors initially.
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P = N.mat(N.random.random((maxn,)*2))
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P /= map(N.linalg.norm, P.T) # Normalise the eigenvectors
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D = N.mat(N.zeros((maxn,)*2))
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D[range(maxn), range(maxn)] = (N.arange(maxn, dtype=float)+1)/N.sqrt(maxn)
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A = P*D*N.linalg.inv(P)
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vals = N.array(D.diagonal())[0]
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vecs = P
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uv_sortind = vals.argsort()
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vals = vals[uv_sortind]
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vecs = vecs[:,uv_sortind]
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from scipy.linalg.iterative import get_matvec
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matvec = get_matvec(A)
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#= lambda x: N.asarray(A*x)[0]
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nev=4
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eigvs = ARPACK_eigs(matvec, A.shape[0], nev=nev)
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calc_vals = eigvs[0]
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# Ensure the calculate eigenvectors have the same sign as the refence values
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calc_vecs = eigvs[1] / [N.sign(x[0]) for x in eigvs[1].T]
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assert_array_almost_equal(calc_vals, vals[0:nev], decimal=7)
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assert_array_almost_equal(calc_vecs, N.array(vecs)[:,0:nev], decimal=7)
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# class TestGeneigs(NumpyTestCase):
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# def test(self):
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# import pickle
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# import scipy.linsolve
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# A,B = pickle.load(file('mats.pickle'))
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# sigma = 27.
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# sigma_solve = scipy.linsolve.splu(A - sigma*B).solve
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# w = ARPACK_gen_eigs(B.matvec, sigma_solve, B.shape[0], sigma, 10)[0]
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# assert_array_almost_equal(w,
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# [27.346442255386375, 49.100299170945405, 56.508474856551544, 56.835800191692492,
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# 65.944215785041365, 66.194792400328367, 78.003788872725238, 79.550811647295944,
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# 94.646308846854879, 95.30841709116271], decimal=11)
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if __name__ == "__main__":
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NumpyTest().run()
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