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| from sympy.core.random import randint | |
| from sympy.core.numbers import Integer | |
| from sympy.matrices.dense import (Matrix, ones, zeros) | |
| from sympy.physics.quantum.matrixutils import ( | |
| to_sympy, to_numpy, to_scipy_sparse, matrix_tensor_product, | |
| matrix_to_zero, matrix_zeros, numpy_ndarray, scipy_sparse_matrix | |
| ) | |
| from sympy.external import import_module | |
| from sympy.testing.pytest import skip | |
| m = Matrix([[1, 2], [3, 4]]) | |
| def test_sympy_to_sympy(): | |
| assert to_sympy(m) == m | |
| def test_matrix_to_zero(): | |
| assert matrix_to_zero(m) == m | |
| assert matrix_to_zero(Matrix([[0, 0], [0, 0]])) == Integer(0) | |
| np = import_module('numpy') | |
| def test_to_numpy(): | |
| if not np: | |
| skip("numpy not installed.") | |
| result = np.array([[1, 2], [3, 4]], dtype='complex') | |
| assert (to_numpy(m) == result).all() | |
| def test_matrix_tensor_product(): | |
| if not np: | |
| skip("numpy not installed.") | |
| l1 = zeros(4) | |
| for i in range(16): | |
| l1[i] = 2**i | |
| l2 = zeros(4) | |
| for i in range(16): | |
| l2[i] = i | |
| l3 = zeros(2) | |
| for i in range(4): | |
| l3[i] = i | |
| vec = Matrix([1, 2, 3]) | |
| #test for Matrix known 4x4 matricies | |
| numpyl1 = np.array(l1.tolist()) | |
| numpyl2 = np.array(l2.tolist()) | |
| numpy_product = np.kron(numpyl1, numpyl2) | |
| args = [l1, l2] | |
| sympy_product = matrix_tensor_product(*args) | |
| assert numpy_product.tolist() == sympy_product.tolist() | |
| numpy_product = np.kron(numpyl2, numpyl1) | |
| args = [l2, l1] | |
| sympy_product = matrix_tensor_product(*args) | |
| assert numpy_product.tolist() == sympy_product.tolist() | |
| #test for other known matrix of different dimensions | |
| numpyl2 = np.array(l3.tolist()) | |
| numpy_product = np.kron(numpyl1, numpyl2) | |
| args = [l1, l3] | |
| sympy_product = matrix_tensor_product(*args) | |
| assert numpy_product.tolist() == sympy_product.tolist() | |
| numpy_product = np.kron(numpyl2, numpyl1) | |
| args = [l3, l1] | |
| sympy_product = matrix_tensor_product(*args) | |
| assert numpy_product.tolist() == sympy_product.tolist() | |
| #test for non square matrix | |
| numpyl2 = np.array(vec.tolist()) | |
| numpy_product = np.kron(numpyl1, numpyl2) | |
| args = [l1, vec] | |
| sympy_product = matrix_tensor_product(*args) | |
| assert numpy_product.tolist() == sympy_product.tolist() | |
| numpy_product = np.kron(numpyl2, numpyl1) | |
| args = [vec, l1] | |
| sympy_product = matrix_tensor_product(*args) | |
| assert numpy_product.tolist() == sympy_product.tolist() | |
| #test for random matrix with random values that are floats | |
| random_matrix1 = np.random.rand(randint(1, 5), randint(1, 5)) | |
| random_matrix2 = np.random.rand(randint(1, 5), randint(1, 5)) | |
| numpy_product = np.kron(random_matrix1, random_matrix2) | |
| args = [Matrix(random_matrix1.tolist()), Matrix(random_matrix2.tolist())] | |
| sympy_product = matrix_tensor_product(*args) | |
| assert not (sympy_product - Matrix(numpy_product.tolist())).tolist() > \ | |
| (ones(sympy_product.rows, sympy_product.cols)*epsilon).tolist() | |
| #test for three matrix kronecker | |
| sympy_product = matrix_tensor_product(l1, vec, l2) | |
| numpy_product = np.kron(l1, np.kron(vec, l2)) | |
| assert numpy_product.tolist() == sympy_product.tolist() | |
| scipy = import_module('scipy', import_kwargs={'fromlist': ['sparse']}) | |
| def test_to_scipy_sparse(): | |
| if not np: | |
| skip("numpy not installed.") | |
| if not scipy: | |
| skip("scipy not installed.") | |
| else: | |
| sparse = scipy.sparse | |
| result = sparse.csr_matrix([[1, 2], [3, 4]], dtype='complex') | |
| assert np.linalg.norm((to_scipy_sparse(m) - result).todense()) == 0.0 | |
| epsilon = .000001 | |
| def test_matrix_zeros_sympy(): | |
| sym = matrix_zeros(4, 4, format='sympy') | |
| assert isinstance(sym, Matrix) | |
| def test_matrix_zeros_numpy(): | |
| if not np: | |
| skip("numpy not installed.") | |
| num = matrix_zeros(4, 4, format='numpy') | |
| assert isinstance(num, numpy_ndarray) | |
| def test_matrix_zeros_scipy(): | |
| if not np: | |
| skip("numpy not installed.") | |
| if not scipy: | |
| skip("scipy not installed.") | |
| sci = matrix_zeros(4, 4, format='scipy.sparse') | |
| assert isinstance(sci, scipy_sparse_matrix) | |