Mailman 3 September 2015 - NumPy-Discussion

Compiling NumPy for iOS PyQt app?
by David Morris 20 Sep '15

20 Sep '15

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 20 Sep '15

20 Sep '15

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 19 Sep '15

19 Sep '15

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 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

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OK to upload patched 1.9.2 for Python 3.5?
by Matthew Brett 19 Sep '15

19 Sep '15

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 19 Sep '15

19 Sep '15

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 19 Sep '15

19 Sep '15

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

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ANN: python-blosc 1.2.8 released
by Francesc Alted 16 Sep '15

16 Sep '15

============================= 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 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

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First release candidate for the 1.5.0 series
by Thomas Caswell 16 Sep '15

16 Sep '15

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 values with `imshow` - many new rcparams In addition there are significant improvements to `nbagg` and a complete overhaul of the c++ bindings to AGG. Please see the drafts of the [full whats new]( http://matplotlib.org/devdocs/users/whats_new.html#new-in-matplotlib-1-5) and [api changes]( http://matplotlib.org/devdocs/api/api_changes.html#changes-in-1-5-0)

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new ufunc implementations for object arrays
by Allan Haldane 15 Sep '15

15 Sep '15

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 pure-python ufunc implementations since I am sure I have missed some edge cases. I would love suggestions, and I need help for a few cases I couldn't figure out (eg np.nextafter, np.spacing, np.cbrt. Also, np.expm1 np.signbit and others are a bit suspect). Allan

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ANN: Numexpr 2.4.4 is out
by Francesc Alted 14 Sep '15

14 Sep '15

========================= 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 last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. What's new ========== This is a maintenance release which contains several bug fixes, like better testing on Python3 platform and some harmless data race. Among the enhancements, AppVeyor support is here and OMP_NUM_THREADS is honored as a fallback in case NUMEXPR_NUM_THREADS is not set. In case you want to know more in detail what has changed in this version, see: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst Where I can find Numexpr? ========================= The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted

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