def test_frac(): from sympy 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_NumPyPrinter(): p = NumPyPrinter() assert p.doprint(sign(x)) == 'numpy.sign(x)' A = MatrixSymbol("A", 2, 2) 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' # Workaround for numpy negative integer power errors assert p.doprint(x**-1) == 'x**(-1.0)' assert p.doprint(x**-2) == 'x**(-2.0)'
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)' 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_NumPyPrinter(): p = NumPyPrinter() assert p.doprint(sign(x)) == 'numpy.sign(x)' A = MatrixSymbol("A", 2, 2) 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' # Workaround for numpy negative integer power errors assert p.doprint(x**-1) == 'x**(-1.0)' assert p.doprint(x**-2) == 'x**(-2.0)' 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_NumPyPrinter(): p = NumPyPrinter() assert p.doprint(sign(x)) == 'numpy.sign(x)'
def test_NumPyPrinter(): p = NumPyPrinter() assert p.doprint(sign(x)) == 'numpy.sign(x)'
def test_NumPyPrinter(): p = NumPyPrinter() assert p.doprint(sign(x)) == 'numpy.sign(x)' A = MatrixSymbol("A", 2, 2) assert p.doprint(A**(-1)) == "numpy.linalg.inv(A)" assert p.doprint(A**5) == "numpy.linalg.matrix_power(A, 5)"