Mailman 3 September 2015 - NumPy-Discussion

Fwd: [Matplotlib-devel] Fwd: [NumFOCUS Projects] grant opportunity
by Nathaniel Smith Sept. 24, 2015

Sept. 24, 2015

---------- Forwarded message ---------- From: Thomas Caswell <tcaswell(a)gmail.com> Date: Wed, Sep 23, 2015 at 8:51 PM Subject: [Matplotlib-devel] Fwd: [NumFOCUS Projects] grant opportunity To: matplotlib development list <matplotlib-devel(a)python.org> If anyone has some downtime and would like to be paid to hack on open source. Submissions are due Sept 30 Tom ---------- Forwarded message --------- From: Gina Helfrich <gina(a)numfocus.org> Date: Fri, Sep 18, 2015 at 12:… [View More]

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Tentative NumPy Tutorial inaccessible
by Andriy Yurchuk Sept. 23, 2015

Sept. 23, 2015

Hi! The Tentative NumPy Tutorial is no longer accessible by the URL http://wiki.scipy.org/Tentative_NumPy_Tutorial, it returns a 403. The link to this page is still on NumPy homepage though. Has the page been moved somewhere else? --- Regards, Andriy Yurchuk

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Plan for 1.9.4 release
by Matthew Brett Sept. 23, 2015

Sept. 23, 2015

Hi, I'm afraid our very helpful testers have found a problem with f2py and Numpy 1.9.3 (and earlier): https://github.com/numpy/numpy/issues/6348 I think we need a numpy 1.9.4 soon therefore. I started a branch / PR here : https://github.com/numpy/numpy/pull/6350 Any other fixes? Carl - do you want to get your mingw-w64 PR in for this? Cheers, Matthew

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"Become an Open Source Contributor" workshop
by Jaime Fernández del Río Sept. 23, 2015

Sept. 23, 2015

Apologies for the cross-posting. The Data Science Student Society of the University of California San Diego, or DS3 @ UCSD as they like to call themselves, will be holding biweekly Python themed workshops starting this fall. On the week of October 19th, they will be having yours truly doing a "Become an Open Source Contributor" piece. It will be a shortish event, 60-90 minutes, so my idea was to cover the following: 1. (15 min) An introduction to the Python data science landscape. 2. (… [View More]

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Steering Committee Size
by Sebastian Berg Sept. 23, 2015

Sept. 23, 2015

Hi, Trying to figure out at least a bit from the discussions. While I am happy with the draft, I wonder if someone has some insights about some questions: 1. How large crowds have examples of working well with apache style voting? 2. How large do we expect numpy steering council to be (I have always thought about 10). 3. More on opinions, how large does the community feel is too large (so that we should maybe elect people). And to maybe more a discussion point, does the community feel that … [View More]

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Tentative numpy tutorial
by Dan Patterson Sept. 23, 2015

Sept. 23, 2015

Bryan is this the tutorial to which you refer? http://docs.scipy.org/doc/scipy/reference/tutorial/index.html

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ANN: SfePy 2015.3
by Robert Cimrman Sept. 23, 2015

Sept. 23, 2015

I am pleased to announce release 2015.3 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (preliminary support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: http://groups.google.com/group/sfepy-devel Git (source) repository, issue tracker, wiki: http://github.com/sfepy Highlights of this … [View More]

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facebook, twitter, and g+
by Charles R Harris Sept. 22, 2015

Sept. 22, 2015

Hi All, Just posting to elicit thoughts about scipy.org having a presence in social media for announcements. Chuck

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Commit rights for Allan Haldane
by Charles R Harris Sept. 22, 2015

Sept. 22, 2015

Hi All, Allan Haldane has been given commit rights. Here's to the new member of the team. Chuck

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1.10.0rc1 coming tomorrow, 22 Sept.
by Charles R Harris Sept. 22, 2015

Sept. 22, 2015

Hi All, Just a heads up. The lack of reported problems in 1.10.0b1 has been stunning. Chuck

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ANN: Numpy 1.9.3 bugfix release
by Matthew Brett Sept. 21, 2015

Sept. 21, 2015

Hi, We have just released numpy 1.9.3, a small bugfix release to fix a bug on Python 3.5, as well as some build issues. You likely only need to upgrade from 1.9.2 if you are on Python 3.5. There are source and OSX wheels up on pypi. We currently have no plans to add 1.9.3 to the sourceforge site, because we cannot yet build a Python 3.5 installer with the standard mingw compiler. Issues fixed ============ * `#5866 <https://github.com/numpy/numpy/pull/5866>`__: fix error finding … [View More]

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Update on the recent mailing list outage
by Nathaniel Smith Sept. 21, 2015

Sept. 21, 2015

Hi all, I'm sure you all noticed that numpy-discussion was down for 4-5 days this week. Apparently this was due to some unnoticed misconfigurations that were triggered when moving it from the ancient and unmaintainable server that it's been on for the last umpteen years. Some more details are here: https://github.com/numpy/numpy/issues/6325 There's some hope that this should reduce the frequency of outages going forward, though it's still not entirely clear how stable this new setup is or … [View More]

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method to calculate the magnitude squared
by Phillip Feldman Sept. 20, 2015

Sept. 20, 2015

In communications and signal processing, it is frequently necessary to calculate the power of a signal. This can be done with a function like the following: def magsq(z): """ Return the magnitude squared of the real- or complex-valued input. """ return z.real**2 + z.imag**2 A high percentage of the scripts that I write contain or import a function like this. It would be great if there were a built-in method in NumPy, preferably with a name like `magsq`, `mag2`, or `msq`. Phillip

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Re: [Numpy-discussion] [Python-ideas] Should our default random number generator be secure?
by Robert Kern Sept. 20, 2015

Sept. 20, 2015

[Tim, ping me if you want to get dropped from the reply chain, as we are liable to get more into numpy decision-making. I've dropped python-ideas.] On Mon, Sep 14, 2015 at 4:34 AM, Tim Peters <tim.peters(a)gmail.com> wrote: > > [Robert Kern <robert.kern(a)gmail.com>] > >> ... > >> I'll also recommend the PCG paper (and algorithm) as the author's > >> cross-PRNGs comparisons have been bandied about in this thread already. The > >> paper lays … [View More]out a lot of the relevant issues and balances the various > >> qualities that are important: statistical quality, speed, and security (of > >> various flavors). > >> > >> http://www.pcg-random.org/paper.html > >> > >> I'm actually not that impressed with Random123. The core idea is nice and > >> clean, but the implementation is hideously complex. > > [Nathaniel Smith <njs(a)pobox.com>] > > I'm curious what you mean by this? In terms of the code, or...? In terms of the code that would be necessary to implement this in a simultaneously performant and cross-platform way in numpy.random. > > I haven't looked at the code, I recommend it! Approach it with the practical goal of "how do I stick this in as a new core PRNG for numpy.random?" > > but the simplest generator they > > recommend in the paper is literally just > > > > def rng_stream(seed): > > counter = 0 > > while True: > > # AES128 takes a 128 bit key and 128 bits of data and returns > > 128 bits of encrypted data > > val = AES128(key=seed, data=counter) > > yield low_64_bits(val) > > yield high_64_bits(val) > > counter += 1 > > I assume it's because if you expand what's required to _implement_ > AES128() in C, it does indeed look pretty hideously complex. On HW > implementing AES primitives, of course the code can be much simpler. > > But to be fair, if integer multiplication and/or addition weren't > implemented in HW, and we had to write to C code emulating them via > bit-level fiddling, the code for any of the PCG algorithms would look > hideously complex too. > > But being fair isn't much fun ;-) Actually, I meant all of the crap *around* it, the platform-compatibility testing to see if you have such a hardware instruction or not, and C++ template shenanigans in the surrounding code. It's possible that the complexity is only due to flexibility, but it was too complex for me to begin understanding *why* it's so complex before I succumbed to ennui and moved on to some other productive use of my free time. At least some of the complexity is due to needing software implementations of reduced-round crypto for decent performance in the absence of the hardware instruction. Performing well in the absence of the hardware instruction is very important to me as I do not seem to have the AES-NI instruction available on my mid-2012 Macbook Pro. Exposing counter-mode AES128 as a core PRNG is a nice idea, but it's just low on my wishlist. I want fast, multiple independent streams on my current hardware first, and PCG gives that to me. On the "if I had to implement all of the bit-fiddling myself" count, I'd still prefer PCG. It has a similar problem with 128-bit integers that may or may not be natively provided (should you want a 128-bit state), and I have to say that the software implementation of that bit-fiddling is much nicer to work with than reduced-round Threefry. It's reference implementation is also overly complex for practical use as it is showing off the whole parameterized PCG family to support the paper, even the explicitly unrecommended members. But it's relatively straightforward to disentangle the desirable members; their implementations, including the supporting 128-bit arithmetic code, are small and clean. -- Robert Kern [View Less]

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Re: [Numpy-discussion] method to calculate the magnitude squared
by R Schumacher Sept. 20, 2015

Sept. 20, 2015

At 10:01 AM 9/20/2015, you wrote: >Is that not the same as >Â Â np.abs(z)**2 ? > >It is, but since that involves taking sqrt, it is *much* slower. Even now, >``` >In [32]: r = np.arange(10000)*(1+1j) > >In [33]: %timeit np.abs(r)**2 >1000 loops, best of 3: 213 Âµs per loop > >In [34]: %timeit r.real**2 + r.imag**2 >10000 loops, best of 3: 47.5 Âµs per loop > >-- Marten Ahh yes, a full extra step, "back" Assuming these are spectra, how does timeit do … [View More]

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Compiling NumPy for iOS PyQt app?
by David Morris Sept. 20, 2015

Sept. 20, 2015

I have a PyQt app running on iOS and would like to add NumPy to improve calculation speed. I see a few python interpreters in the app store which use NumPy so it must be possible, however I have not been able to find any information on the build process for the iOS cross compile. We are building a python with all libraries static linked. Here is the environment: iOS 8.0+ Python 3.4 PyQt 5.5 Qt 5.5 pyqtdeploy Any help getting NumPy compiled into the iOS app? Thank you, David

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Re: [Numpy-discussion] method to calculate the magnitude squared
by R Schumacher Sept. 20, 2015

Sept. 20, 2015

At 09:16 PM 9/18/2015, you wrote: >In communications and signal processing, it is >frequently necessary to calculate the power of a >signal.Â This can be done with a function like the following: > >def magsq(z): >Â Â """ >Â Â Return the magnitude squared of the real- or complex-valued input. >Â Â """ >Â Â return z.real**2 + z.imag**2 Is that not the same as np.abs(z)**2 ? - Ray

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The process I intend to follow for any proposed changes to NumPy
by Travis Oliphant Sept. 18, 2015

Sept. 18, 2015

Hey all, I just wanted to clarify, that I am very excited about a few ideas I have --- but I don't have time myself to engage in the community process to get these changes into NumPy. However, those are real processes --- I've been coaching a few people in those processes for the past several years already. So, rather than do nothing, what I'm looking to do is to work with a few people who I can share my ideas with, get excited about the ideas, and then who will work with the community to … [View More]get them implemented. That's what I was announcing and talking about yesterday --- looking for interested people who want to work on NumPy *with* the NumPy community. In my enthusiasm, I realize that some may have mis-understood my intention. There is no 'imminent' fork, nor am I planning on doing some crazy amount of work that I then try to force on other developers of NumPy. What I'm planning to do is find people to train on NumPy code base (people to increase the diversity of the developers would be ideal -- but hard to accomplish). I plan to train them on NumPy based on my experience, and on what I think should be done --- and then have *them* work through the community process and engage with others to get consensus (hopefully not losing too much in translation in the process --- but instead getting even better). During that process I will engage as a member of the community and help write NEPs and other documents and help clarify where it makes sense as I can. I will be filtering for people that actually want to see NumPy get better. Until I identify the people and work with them, it will be hard to tell how this will best work. So, stay tuned. If all goes well, what you should see in a few weeks time are specific proposals, a branch or two, and the beginnings of some pull requests. If you don't see that, then I will not have found the right people to help me, and we will all continue to go back to searching. While I'm expecting the best, in the worst case, we get additional people who know the NumPy code base and can help squash bugs as well as implement changes that are desired. Three things are needed if you want to participate in this: 1) A willingness to work with the open source community, 2) a deep knowledge of C and in-particular CPython's brand of C, and 3) a willingness to engage with me, do a mind-meld and dump around the NumPy code base, and then improve on what is in my head with the rest of the community. Thanks, -Travis [View Less]

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OK to upload patched 1.9.2 for Python 3.5?
by Matthew Brett Sept. 18, 2015

Sept. 18, 2015

Hi, I'm just building numpy 1.9.2 for Python 3.5 (just released). In order to get the tests to pass on Python 3.5, I need to cherry pick commit 7d6aa8c onto the 1.9.2 tag position. Does anyone object to me uploading a wheel built from this patched version to pypi as 1.9.2 for Python 3.5 on OSX? It would help to get the ball rolling for Python 3.5 binary wheels. Cheers, Matthew

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testing
by Chip Parker Sept. 18, 2015

Sept. 18, 2015

Let's see if the instructions enclosed in http://www.jamesh.id.au/articles/mailman-spamassassin/ which was written in 2003 are still biting us. -- Chip Parker DevOps Engineer

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testing
by Chip Parker Sept. 18, 2015

Sept. 18, 2015

This would be an email. -- Chip Parker DevOps Engineer

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ANN: python-blosc 1.2.8 released
by Francesc Alted Sept. 16, 2015

Sept. 16, 2015

============================= Announcing python-blosc 1.2.8 ============================= What is new? ============ This is a maintenance release. Internal C-Blosc has been upgraded to 1.7.0 (although new bitshuffle support has not been made public, as it seems not ready for production yet). Also, there is support for bytes-like objects that support the buffer interface as input to ``compress`` and ``decompress``. On Python 2.x this includes unicode, on Python 3.x it doesn't. Thanks to … [View More]Valentin Haenel. Finally, a memory leak in ``decompress```has been hunted and fixed. And new tests have been added to catch possible leaks in the future. Thanks to Santi Villalba. For more info, you can have a look at the release notes in: https://github.com/Blosc/python-blosc/blob/master/RELEASE_NOTES.rst More docs and examples are available in the documentation site: http://python-blosc.blosc.org What is it? =========== Blosc (http://www.blosc.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 is the first compressor that is meant not only to reduce the size of large datasets on-disk or in-memory, but also to accelerate object manipulations that are memory-bound (http://www.blosc.org/docs/StarvingCPUs.pdf). See http://www.blosc.org/synthetic-benchmarks.html for some benchmarks on how much speed it can achieve in some datasets. 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 (http://python-blosc.blosc.org/) is the Python wrapper for the Blosc compression library. There is also a handy tool built on Blosc called Bloscpack (https://github.com/Blosc/bloscpack). It features a commmand line interface that allows you to compress large binary datafiles on-disk. It also comes with a Python API that has built-in support for serializing and deserializing Numpy arrays both on-disk and in-memory at speeds that are competitive with regular Pickle/cPickle machinery. Installing ========== python-blosc is in PyPI repository, so installing it is easy: $ pip install -U blosc # yes, you must omit the 'python-' prefix Download sources ================ The sources are managed through github services at: http://github.com/Blosc/python-blosc Documentation ============= There is Sphinx-based documentation site at: http://python-blosc.blosc.org/ Mailing list ============ There is an official mailing list for Blosc at: blosc(a)googlegroups.com http://groups.google.es/group/blosc Licenses ======== Both Blosc and its Python wrapper are distributed using the MIT license. See: https://github.com/Blosc/python-blosc/blob/master/LICENSES for more details. ---- **Enjoy data!** -- Francesc Alted [View Less]

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First release candidate for the 1.5.0 series
by Thomas Caswell Sept. 16, 2015

Sept. 16, 2015

Please give it a try! (linux64 conda builds are available on the tacaswell anaconda.org channel) https://github.com/matplotlib/matplotlib/releases/tag/v1.5.0rc1 This release contains many new features. The highlights include: - the object oriented API will now automatically re-draw the figure when working in the command line - automatic unpacking of labeled data into most plotting functions - arbitrary style cycling - four new perceptually linear color maps - mouse-over for pixel … [View More]

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new ufunc implementations for object arrays
by Allan Haldane Sept. 15, 2015

Sept. 15, 2015

Hello all, I've just submitted a PR which overhauls the implementation of ufuncs for object types. https://github.com/numpy/numpy/pull/6320 The motivation for this is that many ufuncs (eg all transcendental functions) can't handle objects. This PR will also make object arrays more customizable, as all ufuncs can now be overridden by the user by defining methods on the object. I'd like to see if there's enough interest/agreement for me to finish it, and also to have more eyes look over the … [View More]

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ANN: Numexpr 2.4.4 is out
by Francesc Alted Sept. 14, 2015

Sept. 14, 2015

========================= Announcing Numexpr 2.4.4 ========================= Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It wears multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the … [View More]

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