David Cournapeau wrote: > Charles R Harris wrote: >> >> On 1/15/07, *A. M. Archibald* <[EMAIL PROTECTED] >> <mailto:[EMAIL PROTECTED]>> wrote: >> >> On 15/01/07, David Cournapeau <[EMAIL PROTECTED] >> <mailto:[EMAIL PROTECTED]>> wrote: >> > Hi, >> > >> > I am trying to add support for out argument to one C >> function using >> > numpy API (still the clip function). I was wondering about the >> expected >> > behaviour when out does not have the "expected" type. >> > For example, using again the clip function (but the question >> is not >> > specific to this function) >> > >> > In [1]: import numpy >> > >> > In [2]: a = numpy.linspace(0, 10, 101) >> > >> > In [3]: b = numpy.zeros(a.shape, dtype = numpy.int32) >> > >> > In [4]: print a.dtype >> > float64 >> > >> > In [5]: a.clip(0.1, 0.5, b) >> > >> > Should this be equivalent to b = a.clip(0.1, 0.5); b = >> > b.astype(numpy.int32) (ie, the casting is done at the end, >> similar to an >> > ufunc) ? >> >> Since the point of output arguments is to avoid allocating new >> storage, >> >> >> >> If we take that seriously, then an error should be raised on a shape, >> or type mismatch. > For the shape, there is no problem, I guess. A different shape is an > error (except if I want to support broadcasting...). The problem is > really for out having same shape than in, but having different type than > input. > > At first, I wanted to throw an error (for example, clipping an array > which gives a float, and out is integer), but that would be incompatible > with current clip behaviour. > > Concerning the point of avoiding allocating new storage, I am a bit > suspicious: if the types do not match, and the casting is done at the > end, then it means all internal computation will be done is whatever > type is chosen by the function (I am using PyArray_CommonType for that), > and the cast done at the end, meaning new storage. > > Actually, I find more logical to throw an error of the point is to avoid > new storage, as giving a mismatched type out buffer would make the > function create an internal buffer. Sorry, this last sentence is incomplete and does not really make sense: I meant
Actually, I find more logical to throw an error of the point is to avoid new storage, as giving a mismatched type out buffer would make the function create an new array when using the numpy C Api function PyArray_ConvertToCommonType. David _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
