flopscope.numpy.minimum
fnp.minimum(*args, **kwargs)[flopscope source][numpy source]
Element-wise minimum of array elements.
Adapted from NumPy docs np.minimum
Element-wise minimum (propagates NaN).
Compare two arrays and return a new array containing the element-wise minima. If one of the elements being compared is a NaN, then that 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 propagated.
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
outarray will be set to the ufunc result. Elsewhere, theoutarray will retain its original value. Note that if an uninitializedoutarray is created via the defaultout=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 minimum of
x1andx2, element-wise. This is a scalar if bothx1andx2are scalars.
See also
- we.flops.maximum Element-wise maximum of two arrays, propagates NaNs.
- we.flops.fmin Element-wise minimum of two arrays, ignores NaNs.
- we.flops.min The minimum value of an array along a given axis, propagates NaNs.
- we.flops.nanmin The minimum value of an array along a given axis, ignores NaNs.
- we.flops.fmax
- we.flops.max
- we.flops.nanmax
Notes
The minimum 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.minimum([2, 3, 4], [1, 5, 2])
array([1, 3, 2])>>> flops.minimum(flops.eye(2), [0.5, 2]) # broadcasting
array([[ 0.5, 0. ],
[ 0. , 1. ]])>>> flops.minimum([flops.nan, 0, flops.nan],[0, flops.nan, flops.nan])
array([nan, nan, nan])
>>> flops.minimum(-flops.inf, 1)
-inf