On Thu, Mar 23, 2023 at 5:21 AM Sebastian Berg <sebast...@sipsolutions.net> wrote:
> On Wed, 2023-03-22 at 12:00 -0400, Robert Kern wrote: > > On Wed, Mar 22, 2023 at 9:34 AM Neal Becker <ndbeck...@gmail.com> > > wrote: > > > > > I have a function F > > > def F(a, b): > > > c = a * b > > > > > > Initially, a is a scalar, b[240,3000]. No problem. > > > Later I want to use F, where a[240] is a vector. I want to allow > > > both the > > > scalar and vector cases. So I write: > > > > > > def F(a,b): > > > a = np.atleast_1d(a) > > > c = a[:,None] * b > > > > > > This now works for scalar a or vector a. But this solutions seems > > > inelegant, and somewhat fragile. Suppose later we want to allow > > > a[240,3000], a 2d array matching b. > > > > > > Certainly don't want to write code like: > > > if a.ndim == 0:... > > > > > > Is there a more elegant/robust approach? > > > > > > > I would leave it as `c = a * b` and simply record in the docstring > > that `a` > > and `b` should be broadcastable. Yes, that means that the user will > > have to > > write `F(a[:, np.newaxis], b)` for that one case, and that looks a > > little > > ugly, but overall it's less cognitive load on the user to just reuse > > the > > common convention of broadcasting than to record the special case. > > > I will note that it is not hard to insert the new axes. > `np.expand_dims` may be convenient. many functions (ufuncs) also have > the `outer` version which does this: `np.add.outer()`, etc. > > However, I agree. Unless the use-case exceedingly clear about > requiring "outer" behavior. "outer" behavior is uncommon for functions > in the NumPy world and broadcasting is what users will generally expect > (and that includes future self). > > - Sebastian > > Thanks for the advice! On reflection, I agree.
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