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
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