On Tue, Sep 18, 2012 at 2:33 PM, Travis Oliphant <tra...@continuum.io>wrote:
> > On Sep 18, 2012, at 2:44 PM, Charles R Harris wrote: > > > > On Tue, Sep 18, 2012 at 1:35 PM, Benjamin Root <ben.r...@ou.edu> wrote: > >> >> >> On Tue, Sep 18, 2012 at 3:25 PM, Charles R Harris < >> charlesr.har...@gmail.com> wrote: >> >>> >>> >>> On Tue, Sep 18, 2012 at 1:13 PM, Benjamin Root <ben.r...@ou.edu> wrote: >>> >>>> >>>> >>>> On Tue, Sep 18, 2012 at 2:47 PM, Charles R Harris < >>>> charlesr.har...@gmail.com> wrote: >>>> >>>>> >>>>> >>>>> On Tue, Sep 18, 2012 at 11:39 AM, Benjamin Root <ben.r...@ou.edu>wrote: >>>>> >>>>>> >>>>>> >>>>>> On Mon, Sep 17, 2012 at 9:33 PM, Charles R Harris < >>>>>> charlesr.har...@gmail.com> wrote: >>>>>> >>>>>>> >>>>>>> >>>>>>> On Mon, Sep 17, 2012 at 3:40 PM, Travis Oliphant < >>>>>>> tra...@continuum.io> wrote: >>>>>>> >>>>>>>> >>>>>>>> On Sep 17, 2012, at 8:42 AM, Benjamin Root wrote: >>>>>>>> >>>>>>>> > Consider the following code: >>>>>>>> > >>>>>>>> > import numpy as np >>>>>>>> > a = np.array([1, 2, 3, 4, 5], dtype=np.int16) >>>>>>>> > a *= float(255) / 15 >>>>>>>> > >>>>>>>> > In v1.6.x, this yields: >>>>>>>> > array([17, 34, 51, 68, 85], dtype=int16) >>>>>>>> > >>>>>>>> > But in master, this throws an exception about failing to cast via >>>>>>>> same_kind. >>>>>>>> > >>>>>>>> > Note that numpy was smart about this operation before, consider: >>>>>>>> > a = np.array([1, 2, 3, 4, 5], dtype=np.int16) >>>>>>>> > a *= float(128) / 256 >>>>>>>> >>>>>>>> > yields: >>>>>>>> > array([0, 1, 1, 2, 2], dtype=int16) >>>>>>>> > >>>>>>>> > Of course, this is different than if one does it in a >>>>>>>> non-in-place manner: >>>>>>>> > np.array([1, 2, 3, 4, 5], dtype=np.int16) * 0.5 >>>>>>>> > >>>>>>>> > which yields an array with floating point dtype in both versions. >>>>>>>> I can appreciate the arguments for preventing this kind of implicit >>>>>>>> casting between non-same_kind dtypes, but I argue that because the >>>>>>>> operation is in-place, then I (as the programmer) am explicitly stating >>>>>>>> that I desire to utilize the current array to store the results of the >>>>>>>> operation, dtype and all. Obviously, we can't completely turn off this >>>>>>>> rule (for example, an in-place addition between integer array and a >>>>>>>> datetime64 makes no sense), but surely there is some sort of happy >>>>>>>> medium >>>>>>>> that would allow these sort of operations to take place? >>>>>>>> > >>>>>>>> > Lastly, if it is determined that it is desirable to allow >>>>>>>> in-place operations to continue working like they have before, I would >>>>>>>> like >>>>>>>> to see such a fix in v1.7 because if it isn't in 1.7, then other >>>>>>>> libraries >>>>>>>> (such as matplotlib, where this issue was first found) would have to >>>>>>>> change >>>>>>>> their code anyway just to be compatible with numpy. >>>>>>>> >>>>>>>> I agree that in-place operations should allow different casting >>>>>>>> rules. There are different opinions on this, of course, but generally >>>>>>>> this >>>>>>>> is how NumPy has worked in the past. >>>>>>>> >>>>>>>> We did decide to change the default casting rule to "same_kind" but >>>>>>>> making an exception for in-place seems reasonable. >>>>>>>> >>>>>>> >>>>>>> I think that in these cases same_kind will flag what are most likely >>>>>>> programming errors and sloppy code. It is easy to be explicit and doing >>>>>>> so >>>>>>> will make the code more readable because it will be immediately obvious >>>>>>> what the multiplicand is without the need to recall what the numpy >>>>>>> casting >>>>>>> rules are in this exceptional case. IISTR several mentions of this >>>>>>> before >>>>>>> (Gael?), and in some of those cases it turned out that bugs were being >>>>>>> turned up. Catching bugs with minimal effort is a good thing. >>>>>>> >>>>>>> Chuck >>>>>>> >>>>>>> >>>>>> True, it is quite likely to be a programming error, but then again, >>>>>> there are many cases where it isn't. Is the problem strictly that we are >>>>>> trying to downcast the float to an int, or is it that we are trying to >>>>>> downcast to a lower precision? Is there a way for one to explicitly >>>>>> relax >>>>>> the same_kind restriction? >>>>>> >>>>> >>>>> I think the problem is down casting across kinds, with the result that >>>>> floats are truncated and the imaginary parts of imaginaries might be >>>>> discarded. That is, the value, not just the precision, of the rhs changes. >>>>> So I'd favor an explicit cast in code like this, i.e., cast the rhs to an >>>>> integer. >>>>> >>>>> It is true that this forces downstream to code up to a higher >>>>> standard, but I don't see that as a bad thing, especially if it exposes >>>>> bugs. And it isn't difficult to fix. >>>>> >>>>> Chuck >>>>> >>>>> >>>> Mind you, in my case, casting the rhs as an integer before doing the >>>> multiplication would be a bug, since our value for the rhs is usually >>>> between zero and one. Multiplying first by the integer numerator before >>>> dividing by the integer denominator would likely cause issues with >>>> overflowing the 16 bit integer. >>>> >>>> >>> For the case in point I'd do >>> >>> In [1]: a = np.array([1, 2, 3, 4, 5], dtype=np.int16) >>> >>> In [2]: a //= 2 >>> >>> In [3]: a >>> Out[3]: array([0, 1, 1, 2, 2], dtype=int16) >>> >>> Although I expect you would want something different in practice. But >>> the current code already looks fragile to me and I think it is a good thing >>> you are taking a closer look at it. If you really intend going through a >>> float, then it should be something like >>> >>> a = (a*(float(128)/256)).astype(int16) >>> >>> Chuck >>> >>> >> And thereby losing the memory benefit of an in-place multiplication? >> > > What makes you think you are getting that? I'd have to check the numpy C > source, but I expect the multiplication is handled just as I wrote it out. > I don't recall any loops that handle mixed types likes that. I'd like to > see some, though, scaling integers is a common problem. > > > > >> That is sort of the point of all this. We are using 16 bit integers >> because we wanted to be as efficient as possible and didn't need anything >> larger. Note, that is what we changed the code to, I am just wondering if >> we are being too cautious. The casting kwarg looks to be what I might >> want, though it isn't as clean as just writing an "*=" statement. >> >> > I think even there you will have an intermediate float array followed by a > cast. > > > This is true, but it is done in chunks of a fixed size (controllable by a > thread-local variable or keyword argument to the ufunc). > > How difficult would it be to change in-place operations back to the > "unsafe" default? > Probably not too difficult, but I think it would be a mistake. What keyword argument are you referring to? In the current case, I think what is wanted is a scaling function that will actually do things in place. The matplotlib folks would probably be happier with the result if they simply coded up a couple of small Cython routines to do that. Chuck
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