| 1 | #!/usr/bin/env python |
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| 2 | |
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| 3 | import unittest |
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| 4 | |
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| 5 | from Matrix import * |
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| 6 | from Numeric import dot, allclose, array, transpose, arange, ones, Float |
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| 7 | from cg_solve import * |
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| 8 | from scipy import sparse, mat |
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| 9 | |
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| 10 | |
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| 11 | class TestCase(unittest.TestCase): |
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| 12 | |
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| 13 | ## def test_solve(self): |
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| 14 | ## """Small Matrix""" |
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| 15 | |
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| 16 | ## A = [[2.0, -1.0, 0.0, 0.0 ], |
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| 17 | ## [-1.0, 2.0, -1.0, 0.0], |
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| 18 | ## [0.0, -1.0, 2.0, -1.0], |
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| 19 | ## [0.0, 0.0, -1.0, 2.0]] |
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| 20 | |
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| 21 | ## A = mat(A) |
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| 22 | |
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| 23 | ## xe = asarray([0.0, 1.0, 2.0, 3.0]) |
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| 24 | ## b = A*xe |
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| 25 | ## x = asarray([0.0, 0.0, 0.0, 0.0]) |
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| 26 | |
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| 27 | ## print A |
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| 28 | ## print xe |
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| 29 | ## print b |
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| 30 | ## print b.shape |
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| 31 | |
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| 32 | ## x = conjugate_gradient(A,b,x,iprint=1) |
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| 33 | |
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| 34 | ## assert allclose(x,xe) |
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| 35 | |
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| 36 | def test_sparse_solve(self): |
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| 37 | """Small Sparse Matrix""" |
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| 38 | |
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| 39 | A = [[2.0, -1.0, 0.0, 0.0 ], |
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| 40 | [-1.0, 2.0, -1.0, 0.0], |
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| 41 | [0.0, -1.0, 2.0, -1.0], |
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| 42 | [0.0,0.0, -1.0, 2.0]] |
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| 43 | |
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| 44 | A = sparse.dok_matrix(A) |
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| 45 | |
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| 46 | xe = [0.0, 1.0, 2.0, 3.0] |
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| 47 | b = A*xe |
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| 48 | x = [0.0, 0.0, 0.0, 0.0] |
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| 49 | |
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| 50 | x = conjugate_gradient(A,b,x,iprint=0) |
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| 51 | |
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| 52 | assert allclose(x,xe) |
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| 53 | |
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| 54 | |
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| 55 | def test_solve_large(self): |
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| 56 | """Standard 1d laplacian """ |
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| 57 | |
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| 58 | A = sparse.dok_matrix() |
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| 59 | |
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| 60 | n = 50 |
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| 61 | for i in arange(0,n): |
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| 62 | A[i,i] = 1.0 |
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| 63 | if i > 0 : |
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| 64 | A[i,i-1] = -0.5 |
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| 65 | if i < n-1 : |
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| 66 | A[i,i+1] = -0.5 |
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| 67 | |
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| 68 | xe = ones( (n,), Float) |
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| 69 | |
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| 70 | b = A*xe |
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| 71 | x = conjugate_gradient(A,b,b,tol=1.0e-5,iprint=0) |
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| 72 | |
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| 73 | assert allclose(x,xe) |
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| 74 | |
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| 75 | def test_solve_large_2d(self): |
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| 76 | """Standard 2d laplacian""" |
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| 77 | |
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| 78 | A = sparse.dok_matrix() |
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| 79 | |
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| 80 | n = 20 |
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| 81 | m = 10 |
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| 82 | for i in arange(0,n): |
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| 83 | for j in arange(0,m): |
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| 84 | I = j+m*i |
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| 85 | A[I,I] = 4.0 |
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| 86 | if i > 0 : |
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| 87 | A[I,I-m] = -1.0 |
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| 88 | if i < n-1 : |
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| 89 | A[I,I+m] = -1.0 |
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| 90 | if j > 0 : |
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| 91 | A[I,I-1] = -1.0 |
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| 92 | if j < m-1 : |
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| 93 | A[I,I+1] = -1.0 |
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| 94 | |
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| 95 | xe = ones( (n*m,), Float) |
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| 96 | |
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| 97 | b = A*xe |
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| 98 | x = conjugate_gradient(A,b,b,iprint=0) |
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| 99 | |
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| 100 | assert allclose(x,xe) |
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| 101 | |
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| 102 | |
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| 103 | def test_vector_shape_error(self): |
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| 104 | """Raise VectorShapeError""" |
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| 105 | |
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| 106 | A = [[2.0, -1.0, 0.0, 0.0 ], |
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| 107 | [-1.0, 2.0, -1.0, 0.0], |
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| 108 | [0.0, -1.0, 2.0, -1.0], |
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| 109 | [0.0,0.0, -1.0, 2.0]] |
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| 110 | |
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| 111 | A = sparse.dok_matrix(A) |
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| 112 | |
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| 113 | xe = [[0.0,2.0], [1.0,3.0], [2.0,4.0], [3.0,2.0]] |
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| 114 | b = A*xe |
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| 115 | x = [0.0, 0.0, 0.0, 0.0] |
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| 116 | |
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| 117 | try: |
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| 118 | x = conjugate_gradient(A,b,b,iprint=0) |
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| 119 | except: |
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| 120 | pass |
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| 121 | else: |
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| 122 | msg = 'Should have raised exception' |
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| 123 | raise msg |
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| 124 | |
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| 125 | |
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| 126 | #------------------------------------------------------------- |
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| 127 | if __name__ == "__main__": |
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| 128 | suite = unittest.makeSuite(TestCase,'test') |
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| 129 | runner = unittest.TextTestRunner() #(verbosity=2) |
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| 130 | runner.run(suite) |
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| 131 | |
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| 132 | |
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| 133 | |
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| 134 | |
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| 135 | |
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| 136 | |
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