Re: [Numpy-discussion] Change in behavior of np.concatenate for upcoming release
You're welcome. I do not have many expectations; only those you can expect from an open source project. ;-) On Sat, Sep 15, 2012 at 10:33 PM, Travis Oliphant wrote: > It's very nice to get your help.I hope I haven't inappropriately set > expectations :-) > > -Travis > > On Sep 15, 2012, at 3:14 PM, Han Genuit wrote: > >> Yeah, that merge was fast. :-) >> >> Regards, >> Han ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Change in behavior of np.concatenate for upcoming release
It's very nice to get your help.I hope I haven't inappropriately set expectations :-) -Travis On Sep 15, 2012, at 3:14 PM, Han Genuit wrote: > Yeah, that merge was fast. :-) > > Regards, > Han > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Change in behavior of np.concatenate for upcoming release
Yeah, that merge was fast. :-) Regards, Han ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Change in behavior of np.concatenate for upcoming release
I was working on the same fix and so I saw your code was similar and merged it. It needs to be back-ported to 1.7.0 Thanks, -Travis On Sep 15, 2012, at 11:06 AM, Han Genuit wrote: > Okay, sent in a pull request: https://github.com/numpy/numpy/pull/443. > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Change in behavior of np.concatenate for upcoming release
Okay, sent in a pull request: https://github.com/numpy/numpy/pull/443. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] easy way to change part of only unmasked elements value?
but I think I personally prefer the reverse. I would expect when I do a[3:6]=1 the mask state would not change. then I want to change the "base", I would use a.base[3:6]=1 then the mask state would change also. By the way, I found b.data always be equal to b.base? cheers, Chao On Tue, Sep 11, 2012 at 5:24 PM, Chao YUE wrote: > Dear Richard, > > this is what I want. Thanks! > > Chao > > > On Tue, Sep 11, 2012 at 3:19 PM, Richard Hattersley > wrote: > >> Hi Chao, >> >> If you don't mind modifying masked values, then if you write to the >> underlying ndarray it won't touch the mask: >> >> >>> a = np.ma.masked_less(np.arange(10),5) >> >>> a.base[3:6] = 1 >> >>> a >> >> masked_array(data = [-- -- -- -- -- 1 6 7 8 9], >> mask = [ True True True True True False False False >> False False], >>fill_value = 99) >> >> Regards, >> Richard Hattersley >> >> >> On 10 September 2012 17:43, Chao YUE wrote: >> >>> Dear all numpy users, >>> >>> what's the easy way if I just want to change part of the unmasked array >>> elements into another new value? like an example below: >>> in my real case, I would like to change a subgrid of a masked numpy >>> array to another value, but this grid include both masked and unmasked data. >>> If I do a simple array[index1:index2, index3:index4] = another_value, >>> those data with original True mask will change into False. I am using numpy >>> 1.6.2. >>> Thanks for any ideas. >>> >>> In [91]: a = np.ma.masked_less(np.arange(10),5) >>> >>> In [92]: or_mask = a.mask.copy() >>> In [93]: a >>> Out[93]: >>> masked_array(data = [-- -- -- -- -- 5 6 7 8 9], >>> mask = [ True True True True True False False False >>> False False], >>>fill_value = 99) >>> >>> >>> In [94]: a[3:6]=1 >>> >>> In [95]: a >>> Out[95]: >>> masked_array(data = [-- -- -- 1 1 1 6 7 8 9], >>> mask = [ True True True False False False False False >>> False False], >>>fill_value = 99) >>> >>> >>> In [96]: a = np.ma.masked_array(a,mask=or_mask) >>> >>> In [97]: a >>> Out[97]: >>> masked_array(data = [-- -- -- -- -- 1 6 7 8 9], >>> mask = [ True True True True True False False False >>> False False], >>>fill_value = 99) >>> >>> Chao >>> >>> -- >>> >>> *** >>> Chao YUE >>> Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL) >>> UMR 1572 CEA-CNRS-UVSQ >>> Batiment 712 - Pe 119 >>> 91191 GIF Sur YVETTE Cedex >>> Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16 >>> >>> >>> >>> >>> ___ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >> >> ___ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > > -- > > *** > Chao YUE > Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL) > UMR 1572 CEA-CNRS-UVSQ > Batiment 712 - Pe 119 > 91191 GIF Sur YVETTE Cedex > Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16 > > > > -- *** Chao YUE Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL) UMR 1572 CEA-CNRS-UVSQ Batiment 712 - Pe 119 91191 GIF Sur YVETTE Cedex Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Change in ``round`` behavior for numpy scalars in python 3
Hi, I just noticed that Python 3 raises an error for 0 dimensional numpy arrays. Here's Python 2.6: In [14]: a = np.array(1.1) In [15]: round(a) Out[15]: 1.0 and Python 3.2: In [3]: a = np.array(1.1) In [4]: round(a) --- TypeError Traceback (most recent call last) /Users/mb312/dev_trees/ in () > 1 round(a) TypeError: type numpy.ndarray doesn't define __round__ method Should arrays implement __round__ ?Returning an error for 1D or above? Best, Matthew ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Change in behavior of np.concatenate for upcoming release
Hi, On Fri, Sep 14, 2012 at 11:25 PM, Han Genuit wrote: > I think there is something wrong with the implementation.. I would > expect each incoming array in PyArray_ConcatenateFlattenedArrays to be > flattened and the sizes of all of them added into a one-dimensional > shape. Now the shape is two-dimensional, which does not make sense to > me. Also the requirement that all sizes must be equal between the > incoming arrays only makes sense when you want to stack them into a > two-dimensional array, which makes it unnecessarily complicated. The > difficulty here is to use PyArray_CopyAsFlat without having to > transform/copy each incoming array to the priority dtype, because they > can have different item sizes between them, but other than that it > should be pretty straightforward, imo. > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion Thanks for the feedback. Feeling inadequate to a full understanding of the code there, I've entered an issue for it: https://github.com/numpy/numpy/issues/442 Ondrej - would you consider this a blocker for release? Best, Matthew ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] OpenOpt Suite release 0.42
Hi all, I'm glad to inform you about new OpenOpt Suite release 0.42 (2012-Sept-15). Main changes: * Some improvements for solver interalg, including handling of categorical variables * Some parameters for solver gsubg * Speedup objective function for de and pswarm on FuncDesigner models * New global (GLP) solver: asa (adaptive simulated annealing) * Some new classes for network problems: TSP (traveling salesman problem), STAB (maximum graph stable set)], MCP (maximum clique problem) * Improvements for FD XOR (and now it can handle many inputs) * Solver de has parameter "seed", also, now it works with PyPy * Function sign now is available in FuncDesigner * FuncDesigner interval analysis (and thus solver interalg) now can handle non-monotone splines of 1st order * FuncDesigner now can handle parameter fixedVars as Python dict * Now scipy InterpolatedUnivariateSpline is used in FuncDesigner interpolator() instead of UnivariateSpline. This creates backward incompatibility - you cannot pass smoothing parameter (s) to interpolator no longer. * SpaceFuncs: add Point weight, Disk, Ball and method contains(), bugfix for importing Sphere, some new examples * Some improvements (essential speedup, new parameter interpolate for P()) for our (currently commercial) FuncDesigner Stochastic Programming addon * Some bugfixes In our website ( http://openopt.org ) you could vote for most required OpenOpt Suite development direction(s) (poll has been renewed, previous results are here). Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
