FWIW, numpy calls it "clip": numpy.clip(a, a_min, a_max, out=None, **kwargs) Clip (limit) the values in an array.
Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1. Equivalent to but faster than np.minimum(a_max, np.maximum(a, a_min)). No check is performed to ensure a_min < a_max.-CHB On Fri, Jul 3, 2020 at 5:37 PM Christopher Barker <python...@gmail.com> wrote: > On Fri, Jul 3, 2020 at 5:25 PM <tcphon...@gmail.com> wrote: > >> > I'd go for val[min:max] tbh. >> > > another reason this is Not Good: in slicing syntax, a:b means >=a and < b > -- this asymmetry is not what we would want here. > > -CHB > > > -- > Christopher Barker, PhD > > Python Language Consulting > - Teaching > - Scientific Software Development > - Desktop GUI and Web Development > - wxPython, numpy, scipy, Cython > -- Christopher Barker, PhD Python Language Consulting - Teaching - Scientific Software Development - Desktop GUI and Web Development - wxPython, numpy, scipy, Cython
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