[Numpy-discussion] numpy.fix and subclasses
Darren Dale
dsdale24@gmail....
Sun Mar 8 18:12:08 CDT 2009
On Sun, Feb 22, 2009 at 11:49 PM, Darren Dale <dsdale24@gmail.com> wrote:
> On Sun, Feb 22, 2009 at 10:35 PM, Darren Dale <dsdale24@gmail.com> wrote:
>
>> I've been finding some numpy functions that could maybe be improved to
>> work better with ndarray subclasses. For example:
>>
>> def fix(x, y=None):
>> x = nx.asanyarray(x)
>> if y is None:
>> y = nx.zeros_like(x)
>> y1 = nx.floor(x)
>> y2 = nx.ceil(x)
>> y[...] = nx.where(x >= 0, y1, y2)
>> return y
>>
>> This implementation is a problematic for subclasses, since it does not
>> allow metadata to propagate using the usual ufunc machinery of
>> __array_wrap__, like ceil and floor do. nx.zeros_like does yield another
>> instance of type(x), but y does not get x's metadata (such as units or a
>> mask). Would it be possible to do something like:
>>
>> if y is None:
>> y = x*0
>>
>> "where" is another function that could maybe be improved to work with the
>> rules established by array_priority, but I'm a lousy C programmer and I
>> haven't actually looked into how this would work. If "where" respected
>> array_priority, fix could be implemented as:
>>
>> def fix(x, y=None):
>> x = nx.asanyarray(x)
>> y1 = nx.floor(x)
>> y2 = nx.ceil(x)
>> if y is None:
>> return nx.where(x >= 0, y1, y2)
>> y[...] = nx.where(x >= 0, y1, y2)
>> return y
>
>
> Actually, I just remembered that quantities tries to prevent things like
> ([1,2,3,4]*m)[:2] = [0,1], since the units dont match, so setting y=x*0 and
> then setting data to a slice of y would be problematic. It would be most
> desirable for "where" to respect __array_priority__, if possible. Any
> comments?
>
I was wondering if we could consider applying a decorator to functions like
fix that do not tie into the ufunc machinery that determines an appropriate
__array_wrap__ to call. It would be simple enough to write a decorator that
does the same thing as _find_array_wrap in umath_ufunc_object.inc, and if an
array_wrap method is identified, apply it to the output of the existing
function. This way numpy's functions would be more cooperative with ndarray
subclasses.
I don't mind writing the decorator and some unit tests, but I don't have a
lot of free time so I would like to discuss it first. Does it sound
reasonable?
Thanks,
Darren
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