numpoly.divide

numpoly.divide(x1: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], numpoly.baseclass.ndpoly], x2: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], numpoly.baseclass.ndpoly], out: Optional[numpoly.baseclass.ndpoly] = None, where: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]] = True, **kwargs: Any)numpoly.baseclass.ndpoly

Return true division of the inputs, element-wise.

Instead of the Python traditional ‘floor division’, this returns a true division. True division adjusts the output type to present the best answer, regardless of input types.

Args:
x1:

Dividend array.

x2:

Divisor array. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

out:

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

where:

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

kwargs:

Keyword args passed to numpy.ufunc.

Return:

This is a scalar if both x1 and x2 are scalars.

Raise:
numpoly.baseclass.FeatureNotSupported:

If x2 contains indeterminants, numerical division is no longer possible and an error is raised instead. For polynomial division see numpoly.poly_divide.

Example:
>>> q0q1q2 = numpoly.variable(3)
>>> numpoly.true_divide(q0q1q2, 4)
polynomial([0.25*q0, 0.25*q1, 0.25*q2])
>>> numpoly.true_divide(q0q1q2, [1, 2, 4])
polynomial([q0, 0.5*q1, 0.25*q2])