def test_frac(): from sympy.functions.elementary.integers import frac expr = frac(x) prntr = NumPyPrinter() assert prntr.doprint(expr) == 'numpy.mod(x, 1)' prntr = SciPyPrinter() assert prntr.doprint(expr) == 'numpy.mod(x, 1)' prntr = PythonCodePrinter() assert prntr.doprint(expr) == 'x % 1' prntr = MpmathPrinter() assert prntr.doprint(expr) == 'mpmath.frac(x)' prntr = SymPyPrinter() assert prntr.doprint(expr) == 'sympy.functions.elementary.integers.frac(x)'
def test_numpy_piecewise_regression(): """ NumPyPrinter needs to print Piecewise()'s choicelist as a list to avoid breaking compatibility with numpy 1.8. This is not necessary in numpy 1.9+. See gh-9747 and gh-9749 for details. """ printer = NumPyPrinter() p = Piecewise((1, x < 0), (0, True)) assert printer.doprint(p) == \ 'numpy.select([numpy.less(x, 0),True], [1,0], default=numpy.nan)' assert printer.module_imports == {'numpy': {'select', 'less', 'nan'}}
def test_16857(): if not np: skip("NumPy not installed") a_1 = MatrixSymbol('a_1', 10, 3) a_2 = MatrixSymbol('a_2', 10, 3) a_3 = MatrixSymbol('a_3', 10, 3) a_4 = MatrixSymbol('a_4', 10, 3) A = BlockMatrix([[a_1, a_2], [a_3, a_4]]) assert A.shape == (20, 6) printer = NumPyPrinter() assert printer.doprint(A) == 'numpy.block([[a_1, a_2], [a_3, a_4]])'
def test_NumPyPrinter(): from sympy import (Lambda, ZeroMatrix, OneMatrix, FunctionMatrix, HadamardProduct, KroneckerProduct, Adjoint, DiagonalOf, DiagMatrix, DiagonalMatrix) from sympy.abc import a, b p = NumPyPrinter() assert p.doprint(sign(x)) == 'numpy.sign(x)' A = MatrixSymbol("A", 2, 2) B = MatrixSymbol("B", 2, 2) C = MatrixSymbol("C", 1, 5) D = MatrixSymbol("D", 3, 4) assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)" assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)" assert p.doprint(Identity(3)) == "numpy.eye(3)" u = MatrixSymbol('x', 2, 1) v = MatrixSymbol('y', 2, 1) assert p.doprint(MatrixSolve(A, u)) == 'numpy.linalg.solve(A, x)' assert p.doprint(MatrixSolve(A, u) + v) == 'numpy.linalg.solve(A, x) + y' assert p.doprint(ZeroMatrix(2, 3)) == "numpy.zeros((2, 3))" assert p.doprint(OneMatrix(2, 3)) == "numpy.ones((2, 3))" assert p.doprint(FunctionMatrix(4, 5, Lambda((a, b), a + b))) == \ "numpy.fromfunction(lambda a, b: a + b, (4, 5))" assert p.doprint(HadamardProduct(A, B)) == "numpy.multiply(A, B)" assert p.doprint(KroneckerProduct(A, B)) == "numpy.kron(A, B)" assert p.doprint(Adjoint(A)) == "numpy.conjugate(numpy.transpose(A))" assert p.doprint(DiagonalOf(A)) == "numpy.reshape(numpy.diag(A), (-1, 1))" assert p.doprint(DiagMatrix(C)) == "numpy.diagflat(C)" assert p.doprint(DiagonalMatrix(D)) == "numpy.multiply(D, numpy.eye(3, 4))" # Workaround for numpy negative integer power errors assert p.doprint(x**-1) == 'x**(-1.0)' assert p.doprint(x**-2) == 'x**(-2.0)' expr = Pow(2, -1, evaluate=False) assert p.doprint(expr) == "2**(-1.0)" assert p.doprint(S.Exp1) == 'numpy.e' assert p.doprint(S.Pi) == 'numpy.pi' assert p.doprint(S.EulerGamma) == 'numpy.euler_gamma' assert p.doprint(S.NaN) == 'numpy.nan' assert p.doprint(S.Infinity) == 'numpy.PINF' assert p.doprint(S.NegativeInfinity) == 'numpy.NINF'
def test_array_printer(): A = ArraySymbol('A', (4, 4, 6, 6, 6)) I = IndexedBase('I') i, j, k = Idx('i', (0, 1)), Idx('j', (2, 3)), Idx('k', (4, 5)) prntr = NumPyPrinter() assert prntr.doprint(ZeroArray(5)) == 'numpy.zeros((5,))' assert prntr.doprint(OneArray(5)) == 'numpy.ones((5,))' assert prntr.doprint(ArrayContraction( A, [2, 3])) == 'numpy.einsum("abccd->abd", A)' assert prntr.doprint(I) == 'I' assert prntr.doprint(ArrayDiagonal( A, [2, 3, 4])) == 'numpy.einsum("abccc->abc", A)' assert prntr.doprint(ArrayDiagonal( A, [0, 1], [2, 3])) == 'numpy.einsum("aabbc->cab", A)' assert prntr.doprint(ArrayContraction( A, [2], [3])) == 'numpy.einsum("abcde->abe", A)' assert prntr.doprint(Assignment(I[i, j, k], I[i, j, k])) == 'I = I' prntr = TensorflowPrinter() assert prntr.doprint(ZeroArray(5)) == 'tensorflow.zeros((5,))' assert prntr.doprint(OneArray(5)) == 'tensorflow.ones((5,))' assert prntr.doprint(ArrayContraction( A, [2, 3])) == 'tensorflow.linalg.einsum("abccd->abd", A)' assert prntr.doprint(I) == 'I' assert prntr.doprint(ArrayDiagonal( A, [2, 3, 4])) == 'tensorflow.linalg.einsum("abccc->abc", A)' assert prntr.doprint(ArrayDiagonal( A, [0, 1], [2, 3])) == 'tensorflow.linalg.einsum("aabbc->cab", A)' assert prntr.doprint(ArrayContraction( A, [2], [3])) == 'tensorflow.linalg.einsum("abcde->abe", A)' assert prntr.doprint(Assignment(I[i, j, k], I[i, j, k])) == 'I = I'
def _generate_numpy_code(expr: sp.Expr) -> str: printer = NumPyPrinter() return printer.doprint(expr)