[Numpy-discussion] Add a function to broadcast arrays to a given shape to numpy's stride_tricks?

[Sebastian Berg]

On Fr, 2014-12-12 at 06:25 -0800, Jaime Fernández del Río wrote:
> On Fri, Dec 12, 2014 at 5:57 AM, Sebastian Berg
> <sebastian at sipsolutions.net> wrote:
>         On Fr, 2014-12-12 at 05:48 -0800, Jaime Fernández del Río
>         wrote:
>         > On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer
>         <shoyer at gmail.com>
>         > wrote:
>         >         On Thu, Dec 11, 2014 at 8:17 AM, Sebastian Berg
>         >         <sebastian at sipsolutions.net> wrote:
>         >                 One option
>         >                 would also be to have something like:
>         >
>         >                 np.common_shape(*arrays)
>         >                 np.broadcast_to(array, shape)
>         >                 # (though I would like many arrays too)
>         >
>         >                 and then broadcast_ar rays could be
>         implemented in
>         >                 terms of these two.
>         >
>         >
>         >         It looks like np.broadcast let's us write the
>         common_shape
>         >         function very easily;
>         >
>         >
>         >         def common_shape(*args):
>         >             return np.broadcast(*args).shape
>         >
>         >
>         >         And it's also very fast:
>         >         1000000 loops, best of 3: 1.04 µs per loop
>         >
>         >         So that does seem like a feasible
>         refactor/simplification for
>         >         np.broadcast_arrays.
>         >
>         >
>         >         Sebastian -- if you're up for writing
>         np.broadcast_to in C,
>         >         that's great! If you're not sure if you'll be able
>         to get
>         >         around to that in the near future, I'll submit my PR
>         with a
>         >         Python implementation (which will have tests that
>         will be
>         >         useful in any case).
>         >
>         >
>         > np.broadcast is the Python object of the old iterator. It
>         may be a
>         > better idea to write all of these functions using the new
>         one,
>         > np.nditer:
>         >
>         >
>         > def common_shape(*args):
>         >     return np.nditer(args).shape[::-1]  # Yes, you do need
>         to reverse
>         > it!
>         >
>         >
>         > And in writing 'broadcast_to', rather than rewriting the
>         broadcasting
>         > logic, you could check the compatibility of the shape with
>         something
>         > like:
>         >
>         >
>         > np.nditer((arr,), itershape=shape)  # will raise ValueError
>         if shapes
>         > incompatible
>         >
>         >
>         >
>         > After that, all that would be left is some prepending of
>         zero strides,
>         > and some zeroing of strides of shape 1 dimensions before
>         calling
>         > as_strided
>         >
>         
>         
>         Hahaha, right there is the 32 limitation, but you can also
>         (ab)use it:
>         
>         np.nditer(np.arange(10), itershape=(5, 10)).itviews[0]
> 
> 
> That's neat! But itviews is not even listed in the attributes of
> nditer in the docs, we should fix that.
> 
> 
> Is the 32 argument limitation really a concern? Because that aside, it
> seems that all the functionality that has been discussed are
> one-liners using nditer: do we need new functions, or better
> documentation?
> 

Maybe we could say it isn't a large concern, more something you can fix
later on if we find it is, but you would have to check the types, I
think that subclasses are probably lost here.

> 
> Jaime
> 
> 
> -- 
> (\__/)
> ( O.o)
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