flopscope.

flopscope.numpy.fmax

fnp.fmax(*args, **kwargs)[flopscope source][numpy source]

Element-wise maximum of array elements.

Adapted from NumPy docs np.fmax

Areacore
Typecounted
NumPy Refnp.fmax
Cost
numel(output)\text{numel}(\text{output})
Flopscope Context

Element-wise maximum ignoring NaN.

Compare two arrays and return a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible.

Parameters

x1, x2:array_like

The arrays holding the elements to be compared. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

out:ndarray, None, or tuple of ndarray and None, optional

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:array_like, optional

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

For other keyword-only arguments, see the ufunc docs.

Returns

y:ndarray or scalar

The maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.

See also

Notes

The fmax is equivalent to flops.where(x1 >= x2, x1, x2) when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting.

Examples

>>> import flopscope.numpy as fnp
>>> flops.fmax([2, 3, 4], [1, 5, 2])
array([ 2,  5,  4])
>>> flops.fmax(flops.eye(2), [0.5, 2])
array([[ 1. ,  2. ],
       [ 0.5,  2. ]])
>>> flops.fmax([flops.nan, 0, flops.nan],[0, flops.nan, flops.nan])
array([ 0.,  0., nan])