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