When I write a function that accepts ndarray or scalar inputs def foo(a): # Does something to `a` # # a: `x` dimensional array or scalar # . . . cast(a, x) # Deal with `a` as if it is an `x`-d array after this Is there an efficient way to write that cast function? Basically, I'd want a function that would cast: a, a scalar to ndarray with shape ((1,)*x) b, an ndarray with y<x dims explicitly to shape ((1,) * (y-x) + b.shape) (same as broadcasting) c, an ndarray with x dims is unaffected d, an ndarray with y>x dims throws an error do it all in-place (at least when starting with an array), to prevent double memory It seems like this functionality is repeated so often in built-in functions that there should be some shortcut for it, but I'm not finding it. I can do a_ = np.array(a, ndmin = x, copy = False) and then assert len(a_.shape) == x) , but that still makes a copy of arrays. (I.e., a_.base is a is False). Is there a way around this? Continue reading...
