Re: [Numpy-discussion] Integers to integer powers, let's make a decision

I do think one of the main arguments for returning float remains the analogy with division. I don't know about the rest of you, but it has been such a relief not to have to tell students any more "you should add a ".", otherwise it does integer division". For most purposes, it simply shouldn't

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On 06/10/2016 03:38 PM, Alan Isaac wrote: np.find_common_type([np.int8],[np.int32]) > dtype('int8') (np.arange(10,dtype=np.int8)+np.int32(2**10)).dtype > dtype('int16') > > And so on. If these other binary operators upcast based > on the scalar value, why wouldn't exponentiation? > I

Re: [Numpy-discussion] Datarray 0.1.0 release

On Fri, Jun 10, 2016 at 1:19 PM, Stephan Hoyer wrote: > On Fri, Jun 10, 2016 at 12:51 PM, Matthew Brett > wrote: >> >> If you like the general idea, and you don't mind the pandas >> dependency, `xray` is a much better choice for production code right >>

Re: [Numpy-discussion] Datarray 0.1.0 release

On Fri, Jun 10, 2016 at 12:51 PM, Matthew Brett wrote: > If you like the general idea, and you don't mind the pandas > dependency, `xray` is a much better choice for production code right > now, and will do the same stuff and more: > >

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On Fri, Jun 10, 2016 at 12:01 PM Nathaniel Smith wrote: > On Jun 10, 2016 10:50, "Alan Isaac" wrote: > > > > On 6/10/2016 1:34 PM, Nathaniel Smith wrote: > >> > >> You keep pounding on this example. It's a fine example, but, c'mon. **2 > is probably at

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On 06/10/2016 01:50 PM, Alan Isaac wrote: > Again, **almost all** integer combinations overflow: that's the point. Don't almost all integer combinations overflow for multiplication as well? I estimate that for unsigned 32 bit integers, only roughly 1 in 2e8 combinations don't overflow. The

[Numpy-discussion] Datarray 0.1.0 release

Hi, I just released a new version of the Datarray package: https://pypi.python.org/pypi/datarray/0.1.0 https://github.com/BIDS/datarray It's a very lightweight implementation of arrays with labeled axes and ticks, that allows you to do stuff like: >>> narr = DataArray(np.zeros((1,2,3)),

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

I guess I have one more question; sorry. Suppose we stipulate that `np.int_(9)**np.int__(10)` should just overflow, since that appears to be the clear intent of the (informed) user. When a Python 3 user writes `np.arange(10)**10`, how are we to infer the intended type of the output? (I specify

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On Fri, Jun 10, 2016 at 2:00 PM, Nathaniel Smith wrote: > On Jun 10, 2016 10:50, "Alan Isaac" wrote: > > > > On 6/10/2016 1:34 PM, Nathaniel Smith wrote: > >> > >> You keep pounding on this example. It's a fine example, but, c'mon. **2 > is probably at

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On Jun 10, 2016 10:50, "Alan Isaac" wrote: > > On 6/10/2016 1:34 PM, Nathaniel Smith wrote: >> >> You keep pounding on this example. It's a fine example, but, c'mon. **2 is probably at least 100x more common in real source code. Maybe 1000x more common. Why should we break

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On 6/10/2016 1:34 PM, Nathaniel Smith wrote: You keep pounding on this example. It's a fine example, but, c'mon. **2 is probably at least 100x more common in real source code. Maybe 1000x more common. Why should we break the common case for your edge case? It is hardly an "edge case".

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On 6/10/2016 1:20 PM, Allan Haldane wrote: numpy users have to be aware of overflow issues in lots of other (simple) cases anyway, eg plain addition and multiplication. This is not comparable because *almost all* integer combinations overflow for exponentiation. See the discussion at

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On Jun 10, 2016 05:11, "Alan Isaac" wrote: > > On 6/10/2016 2:42 AM, Nathaniel Smith wrote: >> >> I dunno, with my user hat on I'd be incredibly surprised / confused / >> annoyed if an innocent-looking expression like >> >> np.arange(10) ** 2 >> >> started returning

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On 6/10/2016 1:20 PM, Ian Henriksen wrote: forcing float output for people who actually want integers is not at all ideal Yes, there definitely should be a function supporting this. Alan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On Fri, Jun 10, 2016 at 12:42 AM Nathaniel Smith wrote: > On Mon, Jun 6, 2016 at 1:17 PM, Charles R Harris > wrote: > > > > > > > > On Mon, Jun 6, 2016 at 2:11 PM, Marten van Kerkwijk < > m.h.vankerkw...@gmail.com> wrote: > >> > >> Hi Chuck, > >> > >>

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On 06/10/2016 08:10 AM, Alan Isaac wrote: > Is np.arange(10)**10 also "innocent looking" to a Python user? This doesn't bother me much because numpy users have to be aware of overflow issues in lots of other (simple) cases anyway, eg plain addition and multiplication. I'll add my +1 for integer

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

+1 to Alan's point. Having different type behaviour depending on the values of x and y for np.arange(x) ** y would be awful, and it would also be awful to have to worry about overflow here... ... Having said that, it would be equally annoying to not have a way to define integer powers... From:

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On 6/10/2016 2:42 AM, Nathaniel Smith wrote: I dunno, with my user hat on I'd be incredibly surprised / confused / annoyed if an innocent-looking expression like np.arange(10) ** 2 started returning floats... having exact ints is a really nice feature of Python/numpy as compared to

Re: [Numpy-discussion] Indexing with floats

On 06/10/2016 01:48 PM, Robert Kern wrote: https://mail.scipy.org/pipermail/numpy-discussion/2012-December/064705.html https://github.com/numpy/numpy/issues/2810 https://github.com/numpy/numpy/pull/2891 https://github.com/numpy/numpy/pull/3243

Re: [Numpy-discussion] Indexing with floats

On Fri, Jun 10, 2016 at 12:15 PM, Fabien wrote: > > Hi, > > I really tried to do my homework before asking this here, but I just couldn't find the relevant information anywhere... > > My question is about the rationale behind forbidding indexing with floats, i.e.: > >

[Numpy-discussion] Indexing with floats

Hi, I really tried to do my homework before asking this here, but I just couldn't find the relevant information anywhere... My question is about the rationale behind forbidding indexing with floats, i.e.: >>> x[2.] __main__:1: VisibleDeprecationWarning: using a non-integer number instead

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On Fri, Jun 10, 2016 at 7:42 AM, Nathaniel Smith wrote: > On Mon, Jun 6, 2016 at 1:17 PM, Charles R Harris > wrote: >> >> ... >> >> It looks to me like users want floats, while developers want the >> easy path of raising an error. Darn those users, they

[Numpy-discussion] scipy.stats.qqplot and scipy.stats.probplot axis labeling

The scipy.stats.qqplot and scipy.stats.probplot functions plot expected values versus actual data values for visualization of fit to a distribution. First a one-D array of expected percentiles is generated for a sample of size N; then that is passed to dist.ppf, the per cent point

Re: [Numpy-discussion] Integers to integer powers, let's make a decision

On Mon, Jun 6, 2016 at 1:17 PM, Charles R Harris wrote: > > > > On Mon, Jun 6, 2016 at 2:11 PM, Marten van Kerkwijk > wrote: >> >> Hi Chuck, >> >> I consider either proposal an improvement, but among the two I favour >> returning float for