nums.numpy.minimum

nums.numpy.minimum(x1, x2, out=None, where=True, **kwargs)[source]

Element-wise minimum of array elements.

This docstring was copied from numpy.minimum.

Some inconsistencies with the NumS version may exist.

Compare two arrays and returns 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 (BlockArray) – 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).

  • x2 (BlockArray) – 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 (BlockArray, None, or 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 (BlockArray, 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 – The minimum of x1 and x2, element-wise.

Return type

BlockArray

See also

maximum

Element-wise maximum of two arrays, propagates NaNs.

fmin

Element-wise minimum of two arrays, ignores NaNs.

amin

The minimum value of an array along a given axis, propagates NaNs.

nanmin

The minimum value of an array along a given axis, ignores NaNs.

fmax, amax, nanmax

Notes

The minimum is equivalent to nps.where(x1 <= x2, x1, x2).get() when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.minimum(nps.array([2, 3, 4]), nps.array([1, 5, 2])).get()  
array([1, 3, 2])

>>> nps.minimum(nps.eye(2), nps.array([0.5, 2])).get() # broadcasting  
array([[ 0.5,  0. ],
       [ 0. ,  1. ]])

>>> nps.minimum(nps.array([nps.nan, 0, nps.nan]),
...     nps.array([0, nps.nan, nps.nan])).get()  
array([nan, nan, nan])
>>> nps.minimum(nps.array(-nps.Inf), nps.array(1)).get()  
array(-inf)