mirror of
https://github.com/wassname/simpeg.git
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rowanc1
251 lines
8.9 KiB
Python
251 lines
8.9 KiB
Python
import unittest
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from SimPEG.Utils import *
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from SimPEG import Mesh, np, sp
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from SimPEG.Tests import checkDerivative
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TOL = 1e-8
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class TestCheckDerivative(unittest.TestCase):
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def test_simplePass(self):
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def simplePass(x):
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return np.sin(x), sdiag(np.cos(x))
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passed = checkDerivative(simplePass, np.random.randn(5), plotIt=False)
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self.assertTrue(passed, True)
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def test_simpleFunction(self):
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def simpleFunction(x):
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return np.sin(x), lambda xi: sdiag(np.cos(x))*xi
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passed = checkDerivative(simpleFunction, np.random.randn(5), plotIt=False)
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self.assertTrue(passed, True)
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def test_simpleFail(self):
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def simpleFail(x):
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return np.sin(x), -sdiag(np.cos(x))
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passed = checkDerivative(simpleFail, np.random.randn(5), plotIt=False)
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self.assertTrue(not passed, True)
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class TestCounter(unittest.TestCase):
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def test_simpleFail(self):
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class MyClass(object):
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def __init__(self, url):
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self.counter = Counter()
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@count
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def MyMethod(self):
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pass
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@timeIt
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def MySecondMethod(self):
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pass
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c = MyClass('blah')
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for i in range(100): c.MyMethod()
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for i in range(300): c.MySecondMethod()
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c.counter.summary()
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self.assertTrue(True)
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class TestSequenceFunctions(unittest.TestCase):
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def setUp(self):
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self.a = np.array([1, 2, 3])
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self.b = np.array([1, 2])
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self.c = np.array([1, 2, 3, 4])
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def test_mkvc1(self):
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x = mkvc(self.a)
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self.assertTrue(x.shape, (3,))
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def test_mkvc2(self):
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x = mkvc(self.a, 2)
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self.assertTrue(x.shape, (3, 1))
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def test_mkvc3(self):
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x = mkvc(self.a, 3)
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self.assertTrue(x.shape, (3, 1, 1))
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def test_ndgrid_2D(self):
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XY = ndgrid([self.a, self.b])
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X1_test = np.array([1, 2, 3, 1, 2, 3])
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X2_test = np.array([1, 1, 1, 2, 2, 2])
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self.assertTrue(np.all(XY[:, 0] == X1_test))
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self.assertTrue(np.all(XY[:, 1] == X2_test))
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def test_ndgrid_3D(self):
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XYZ = ndgrid([self.a, self.b, self.c])
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X1_test = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3])
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X2_test = np.array([1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2])
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X3_test = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4])
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self.assertTrue(np.all(XYZ[:, 0] == X1_test))
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self.assertTrue(np.all(XYZ[:, 1] == X2_test))
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self.assertTrue(np.all(XYZ[:, 2] == X3_test))
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def test_sub2ind(self):
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x = np.ones((5,2))
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self.assertTrue(np.all(sub2ind(x.shape, [0,0]) == [0]))
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self.assertTrue(np.all(sub2ind(x.shape, [4,0]) == [4]))
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self.assertTrue(np.all(sub2ind(x.shape, [0,1]) == [5]))
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self.assertTrue(np.all(sub2ind(x.shape, [4,1]) == [9]))
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self.assertTrue(np.all(sub2ind(x.shape, [[4,1]]) == [9]))
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self.assertTrue(np.all(sub2ind(x.shape, [[0,0],[4,0],[0,1],[4,1]]) == [0,4,5,9]))
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def test_ind2sub(self):
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x = np.ones((5,2))
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self.assertTrue(np.all(ind2sub(x.shape, [0,4,5,9])[0] == [0,4,0,4]))
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self.assertTrue(np.all(ind2sub(x.shape, [0,4,5,9])[1] == [0,0,1,1]))
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def test_indexCube_2D(self):
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nN = np.array([3, 3])
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self.assertTrue(np.all(indexCube('A', nN) == np.array([0, 1, 3, 4])))
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self.assertTrue(np.all(indexCube('B', nN) == np.array([3, 4, 6, 7])))
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self.assertTrue(np.all(indexCube('C', nN) == np.array([4, 5, 7, 8])))
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self.assertTrue(np.all(indexCube('D', nN) == np.array([1, 2, 4, 5])))
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def test_indexCube_3D(self):
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nN = np.array([3, 3, 3])
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self.assertTrue(np.all(indexCube('A', nN) == np.array([0, 1, 3, 4, 9, 10, 12, 13])))
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self.assertTrue(np.all(indexCube('B', nN) == np.array([3, 4, 6, 7, 12, 13, 15, 16])))
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self.assertTrue(np.all(indexCube('C', nN) == np.array([4, 5, 7, 8, 13, 14, 16, 17])))
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self.assertTrue(np.all(indexCube('D', nN) == np.array([1, 2, 4, 5, 10, 11, 13, 14])))
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self.assertTrue(np.all(indexCube('E', nN) == np.array([9, 10, 12, 13, 18, 19, 21, 22])))
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self.assertTrue(np.all(indexCube('F', nN) == np.array([12, 13, 15, 16, 21, 22, 24, 25])))
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self.assertTrue(np.all(indexCube('G', nN) == np.array([13, 14, 16, 17, 22, 23, 25, 26])))
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self.assertTrue(np.all(indexCube('H', nN) == np.array([10, 11, 13, 14, 19, 20, 22, 23])))
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def test_invXXXBlockDiagonal(self):
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a = [np.random.rand(5, 1) for i in range(4)]
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B = inv2X2BlockDiagonal(*a)
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A = sp.vstack((sp.hstack((sdiag(a[0]), sdiag(a[1]))),
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sp.hstack((sdiag(a[2]), sdiag(a[3])))))
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Z2 = B*A - sp.identity(10)
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self.assertTrue(np.linalg.norm(Z2.todense().ravel(), 2) < TOL)
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a = [np.random.rand(5, 1) for i in range(9)]
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B = inv3X3BlockDiagonal(*a)
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A = sp.vstack((sp.hstack((sdiag(a[0]), sdiag(a[1]), sdiag(a[2]))),
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sp.hstack((sdiag(a[3]), sdiag(a[4]), sdiag(a[5]))),
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sp.hstack((sdiag(a[6]), sdiag(a[7]), sdiag(a[8])))))
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Z3 = B*A - sp.identity(15)
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self.assertTrue(np.linalg.norm(Z3.todense().ravel(), 2) < TOL)
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def test_invPropertyTensor2D(self):
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M = Mesh.TensorMesh([6, 6])
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a1 = np.random.rand(M.nC)
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a2 = np.random.rand(M.nC)
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a3 = np.random.rand(M.nC)
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prop1 = a1
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prop2 = np.c_[a1, a2]
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prop3 = np.c_[a1, a2, a3]
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for prop in [4, prop1, prop2, prop3]:
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b = invPropertyTensor(M, prop)
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A = makePropertyTensor(M, prop)
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B1 = makePropertyTensor(M, b)
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B2 = invPropertyTensor(M, prop, returnMatrix=True)
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Z = B1*A - sp.identity(M.nC*2)
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self.assertTrue(np.linalg.norm(Z.todense().ravel(), 2) < TOL)
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Z = B2*A - sp.identity(M.nC*2)
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self.assertTrue(np.linalg.norm(Z.todense().ravel(), 2) < TOL)
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def test_TensorType2D(self):
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M = Mesh.TensorMesh([6, 6])
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a1 = np.random.rand(M.nC)
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a2 = np.random.rand(M.nC)
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a3 = np.random.rand(M.nC)
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prop1 = a1
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prop2 = np.c_[a1, a2]
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prop3 = np.c_[a1, a2, a3]
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for ii, prop in enumerate([4, prop1, prop2, prop3]):
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self.assertTrue(TensorType(M, prop) == ii)
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self.assertRaises(Exception, TensorType, M, np.c_[a1, a2, a3, a3])
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self.assertTrue(TensorType(M, None) == -1)
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def test_TensorType3D(self):
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M = Mesh.TensorMesh([6, 6, 7])
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a1 = np.random.rand(M.nC)
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a2 = np.random.rand(M.nC)
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a3 = np.random.rand(M.nC)
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a4 = np.random.rand(M.nC)
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a5 = np.random.rand(M.nC)
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a6 = np.random.rand(M.nC)
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prop1 = a1
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prop2 = np.c_[a1, a2, a3]
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prop3 = np.c_[a1, a2, a3, a4, a5, a6]
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for ii, prop in enumerate([4, prop1, prop2, prop3]):
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self.assertTrue(TensorType(M, prop) == ii)
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self.assertRaises(Exception, TensorType, M, np.c_[a1, a2, a3, a3])
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self.assertTrue(TensorType(M, None) == -1)
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def test_invPropertyTensor3D(self):
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M = Mesh.TensorMesh([6, 6, 6])
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a1 = np.random.rand(M.nC)
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a2 = np.random.rand(M.nC)
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a3 = np.random.rand(M.nC)
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a4 = np.random.rand(M.nC)
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a5 = np.random.rand(M.nC)
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a6 = np.random.rand(M.nC)
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prop1 = a1
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prop2 = np.c_[a1, a2, a3]
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prop3 = np.c_[a1, a2, a3, a4, a5, a6]
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for prop in [4, prop1, prop2, prop3]:
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b = invPropertyTensor(M, prop)
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A = makePropertyTensor(M, prop)
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B1 = makePropertyTensor(M, b)
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B2 = invPropertyTensor(M, prop, returnMatrix=True)
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Z = B1*A - sp.identity(M.nC*3)
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self.assertTrue(np.linalg.norm(Z.todense().ravel(), 2) < TOL)
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Z = B2*A - sp.identity(M.nC*3)
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self.assertTrue(np.linalg.norm(Z.todense().ravel(), 2) < TOL)
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def test_isScalar(self):
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self.assertTrue(isScalar(1.))
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self.assertTrue(isScalar(1))
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self.assertTrue(isScalar(long(1)))
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self.assertTrue(isScalar(np.r_[1.]))
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self.assertTrue(isScalar(np.r_[1]))
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def test_asArray_N_x_Dim(self):
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true = np.array([[1,2,3]])
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listArray = asArray_N_x_Dim([1,2,3],3)
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self.assertTrue(np.all(true == listArray))
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self.assertTrue(true.shape == listArray.shape)
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listArray = asArray_N_x_Dim(np.r_[1,2,3],3)
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self.assertTrue(np.all(true == listArray))
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self.assertTrue(true.shape == listArray.shape)
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listArray = asArray_N_x_Dim(np.array([[1,2,3.]]),3)
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self.assertTrue(np.all(true == listArray))
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self.assertTrue(true.shape == listArray.shape)
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true = np.array([[1,2],[4,5]])
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listArray = asArray_N_x_Dim([[1,2],[4,5]],2)
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self.assertTrue(np.all(true == listArray))
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self.assertTrue(true.shape == listArray.shape)
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if __name__ == '__main__':
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unittest.main()
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