Mailman 3 September 2012 - NumPy-Discussion

Where is the code for PyArray_UpdateFlags?
by Frédéric Bastien Sept. 19, 2012

Sept. 19, 2012

Hi, I would like to reuse the code of this function: PyArray_UpdateFlags I don't find its definition in the numpy source code. In the build directory, it get defined in the file build/src.linux-x86_64-2.7/numpy/core/include/numpy/__multiarray_api.h like this: #define PyArray_UpdateFlags \ (*(void (*)(PyArrayObject *, int)) \ PyArray_API[92]) This file is generated by this file numpy/core/code_generators/generate_numpy_api.py, that use the numpy_api_order.txt file. But I'm not able up to now to find the code itself. Do someone know where I can get it? thanks Frédéric

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numpy.ma.MaskedArray.min() makes a copy?
by Benjamin Root Sept. 18, 2012

Sept. 18, 2012

An issue just reported on the matplotlib-users list involved a user who ran out of memory while attempting to do an imshow() on a large array. While this wouldn't be totally unexpected, the user's traceback shows that they ran out of memory before any actual building of the image occurred. Memory usage sky-rocketed when imshow() attempted to determine the min and max of the image. The input data was a masked array, and it appears that the implementation of min() for masked arrays goes something like this (paraphrasing here): obj.filled(inf).min() The idea is that any masked element is set to the largest possible value for their dtype in a copied array of itself, and then a min() is performed on that copied array. I am assuming that max() does the same thing. Can this be done differently/more efficiently? If the "filled" approach has to be done, maybe it would be a good idea to make the copy in chunks instead of all at once? Ideally, it would be nice to avoid the copying altogether and utilize some of the special iterators that Mark Weibe created last year. Cheers! Ben Root

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Inconsistencies with string indices
by Ronan Lamy Sept. 17, 2012

Sept. 17, 2012

Consider the following: >>> import numpy as np >>> np.__version__ '1.6.1' >>> arr = np.asarray([[1, 2, 3]]) >>> arr["0"] Traceback (most recent call last): File "<ipython-input-5-28d5d8b86f9b>", line 1, in <module> arr["0"] ValueError: field named 0 not found. >>> arr["0",] array([1, 2, 3]) >>> arr["0", 1] 2 >>> arr[0, "1"] 2 >>> arr[1] Traceback (most recent call last): File "<ipython-input-9-62bda37481b6>", line 1, in <module> arr[1] IndexError: index out of bounds >>> arr[1, 1] Traceback (most recent call last): File "<ipython-input-13-61422633ac63>", line 1, in <module> arr[1, 1] IndexError: index (1) out of range (0<=index<1) in dimension 0 >>> arr["1", "1"] Traceback (most recent call last): File "<ipython-input-14-2e114ec345f6>", line 1, in <module> arr["1", "1"] IndexError: index (1) out of range (0<=index<0) in dimension 0 Is there some kind of logic here, or is this just accumulated cruft? IMHO, strings should simply never be coerced to int when indexing.

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Status of fixing bugs for the 1.7.0rc1 release
by Ondřej Čertík Sept. 16, 2012

Sept. 16, 2012

Hi, I've finally finished review of https://github.com/numpy/numpy/pull/439 which back-ports all the PRs from master into the release branch and pushed it in. Here is the current status of bugs for the 1.7.0 release: https://github.com/numpy/numpy/issues/396 I believe that for example a lot of the Debian based bugs were fixed by now (in the 1.7.0 branch). Can I release 1.7.0b2? So that others can try it out, while we work on the rest of the issues. I don't think it's ready for rc1 yet, but we've done a lot of work since beta1 I think. Ondrej

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[ANN] python-blosc 1.0.5 released
by Francesc Alted Sept. 16, 2012

Sept. 16, 2012

============================= Announcing python-blosc 1.0.5 ============================= What is it? =========== A Python wrapper for the Blosc compression library. Blosc (http://blosc.pytables.org) is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Blosc works well for compressing numerical arrays that contains data with relatively low entropy, like sparse data, time series, grids with regular-spaced values, etc. python-blosc is a Python package that wraps it. What is new? ============ - Upgraded to latest Blosc 1.1.4. - Better handling of condition errors, and improved memory releasing in case of errors (thanks to Valentin Haenel and Han Genuit). - Better handling of types (should compile without warning now, at least with GCC). For more info, you can see the release notes in: https://github.com/FrancescAlted/python-blosc/wiki/Release-notes More docs and examples are available in the Quick User's Guide wiki page: https://github.com/FrancescAlted/python-blosc/wiki/Quick-User's-Guide Download sources ================ Go to: http://github.com/FrancescAlted/python-blosc and download the most recent release from there. Blosc is distributed using the MIT license, see LICENSES/BLOSC.txt for details. Mailing list ============ There is an official mailing list for Blosc at: blosc(a)googlegroups.com http://groups.google.es/group/blosc -- Francesc Alted

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Change in behavior of np.concatenate for upcoming release
by Matthew Brett Sept. 15, 2012

Sept. 15, 2012

Hi, I just noticed that this works for numpy 1.6.1: In [36]: np.concatenate(([2, 3], [1]), 1) Out[36]: array([2, 3, 1]) but the beta release branch: In [3]: np.concatenate(([2, 3], [1]), 1) --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /Users/mb312/<ipython-input-3-0fa244c8aaa8> in <module>() ----> 1 np.concatenate(([2, 3], [1]), 1) IndexError: axis 1 out of bounds [0, 1) In the interests of backward compatibility maybe it would be better to raise a warning for this release, rather than an error? Best, Matthew

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easy way to change part of only unmasked elements value?
by Chao YUE Sept. 15, 2012

Sept. 15, 2012

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 = 999999) 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 = 999999) 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 = 999999) 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 ************************************************************************************

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Change in ``round`` behavior for numpy scalars in python 3
by Matthew Brett Sept. 15, 2012

Sept. 15, 2012

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/<ipython-input-4-7736a3ba67a1> in <module>() ----> 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

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[ANN] OpenOpt Suite release 0.42
by Dmitrey Sept. 15, 2012

Sept. 15, 2012

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.

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John Hunter has been awarded the first Distinguished Service Award by the PSF
by Fernando Perez Sept. 14, 2012

Sept. 14, 2012

Hi folks, you may have already seen this, but in case you haven't, I'm thrilled to share that the Python Software Foundation has just created its newest and highest distinction, the Distinguished Service Award, and has chosen John as its first recipient: http://pyfound.blogspot.com/2012/09/announcing-2012-distinctive-service.html This is a fitting tribute to his many contributions. Cheers, f

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