[Numpy-discussion] Why does np.repeat build a full array?
Sebastian Berg
sebastian at sipsolutions.net
Tue Dec 15 04:29:07 EST 2015
On Di, 2015-12-15 at 08:56 +0100, Sebastian Berg wrote:
> On Di, 2015-12-15 at 17:49 +1100, Juan Nunez-Iglesias wrote:
> > Hi,
> >
> >
> > I've recently been using the following pattern to create arrays of a
> > specific repeating value:
> >
> >
> > from numpy.lib.stride_tricks import as_strided
> >
> > value = np.ones((1,), dtype=float)
> > arr = as_strided(value, shape=input_array.shape, strides=(0,))
> >
> >
> > I can then use arr e.g. to count certain pairs of elements using
> > sparse.coo_matrix. It occurred to me that numpy might have a similar
> > function, and found np.repeat. But it seems that repeat actually
> > creates the full, replicated array, rather than using stride tricks to
> > keep it small. Is there any reason for this?
> >
>
> Two reasons:
> 1. For most arrays, arrays even the simple repeats cannot be done with
> stride tricks. (yours has a dimension size of 1)
> 2. Stride tricks can be nice, but they can also be
> unexpected/inconsistent when you start writing to the result array, so
> you should not do it (and the array should preferably be read-only IMO,
> as_strided itself does not do that).
>
> But yes, there might be room for a function or so to make some stride
> tricks more convenient.
>
Actually, your particular use-case is covered by the new `broadcast_to`
function.
> - Sebastian
>
> >
> > Thanks!
> >
> >
> > Juan.
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>
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