Mailman 3 September 2014 - NumPy-Discussion

ANN: Bokeh 0.6 release
by Bryan Van de Ven 10 Sep '14

10 Sep '14

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6! Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with. This release includes many bug fixes and improvements over our most recent 0.5.2 release: * Abstract Rendering recipes for large data sets: isocontour, heatmap * New charts in bokeh.charts: Time Series and Categorical Heatmap * Full Python 3 support for bokeh-server * Much expanded User and Dev Guides * Multiple axes and ranges capability * Plot object graph query interface * Hit-testing (hover tool support) for patch glyphs See the CHANGELOG for full details. I'd also like to announce a new Github Organization for Bokeh: https://github.com/bokeh. Currently it is home to Scala and and Julia language bindings for Bokeh, but the Bokeh project itself will be moved there before the next 0.7 release. Any implementors of new language bindings who are interested in hosting your project under this organization are encouraged to contact us. In upcoming releases, you should expect to see more new layout capabilities (colorbar axes, better grid plots and improved annotations), additional tools, even more widgets and more charts, R language bindings, Blaze integration and cloud hosting for Bokeh apps. Don't forget to check out the full documentation, interactive gallery, and tutorial at http://bokeh.pydata.org as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at: http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/… If you are using Anaconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh BokehJS is also available by CDN for use in standalone javascript applications: http://cdn.pydata.org/bokeh-0.6.min.js http://cdn.pydata.org/bokeh-0.6.min.css Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh Questions can be directed to the Bokeh mailing list: bokeh(a)continuum.io If you have interest in helping to develop Bokeh, please get involved! Thanks, Bryan Van de Ven Continuum Analytics http://continuum.io

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numpy ignores OPT/FOPT under Python3
by Thomas Unterthiner 10 Sep '14

10 Sep '14

Hi! I want to use the OPT/FOPT environment viariables to set compiler flags when compiling numpy. However it seems that they get ignored under python3. Using Ubuntu 14.04 and numpy 1.9.0, I did the following: >export OPT="-march=native" >export FOPT = "-march=native" > python setup.py build # "python" executes python2.7 [...snip...] C compiler: x86_64-linux-gnu-gcc -pthread -fno-strict-aliasing -march=native -fPIC ^C obviously under python 2.7, the additional march flag works as expected. However, using python3: >export OPT="-march=native" >export FOPT = "-march=native" >python3 setup.py build [.... snip ...] C compiler: x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC ^C obviously, the flags aren't used in python 3. Did I overlook something here? Do $OPT/$FOPT only work in Python 2.7 by design, is this a bug or did I miss something? Cheers Thomas

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Weighted Covariance/correlation
by Sebastian Berg 08 Sep '14

08 Sep '14

Hi all, Tom Poole has opened pull request https://github.com/numpy/numpy/pull/4960 to implement weights into np.cov (correlation can be added), somewhat picking up the effort started by Noel Dawe in https://github.com/numpy/numpy/pull/3864. The pull request would currently implement an accuracy type `weights` keyword argument as default, but have a switch `repeat_weights` to use repeat type weights instead (frequency type are a special case of this I think). As far as I can see, the code is in a state that it can be tested. But since it is a new feature, the names/defaults are up for discussion, so maybe someone who might use such a feature has a preference. I know we had a short discussion about this before, but it was a while ago. For example another option would be to have the two weights as two keyword arguments, instead of a boolean switch. Regards, Sebastian

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numpy whois limit exceeded
by RayS 07 Sep '14

07 Sep '14

In looking up module info for company code policy, I noticed the page http://www.networksolutions.com/whois/results.jsp?domain=numpy.org gives "WHOIS LIMIT EXCEEDED - SEE WWW.PIR.ORG/WHOIS FOR DETAILS" So the domain has been getting a lot of attention today:http://pir.org/resources/faq/ "Public Interest Registry accredited registrars who submit queries through the web-based WHOIS search mechanism are limited to 50 queries per minute. " (Oddly, WWW.PIR.ORG/WHOIS is a bad link, and pir.org's page: http://pir.org/domains/org-domain/ is also non-functional, for this Firefox at least, so they are Bcc'd)

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ANN: NumPy 1.9.0 release
by Julian Taylor 07 Sep '14

07 Sep '14

Hello, We are proud to announce the 1.9.0 release of NumPy. This release includes numerous performance improvements, most significantly the indexing code has been rewritten be a lot times faster for most cases and performance of using small arrays and scalars has almost doubled. Plenty of other functions have been improved too, nonzero, where, bincount, searchsorted, count_nonzero, floating point min/max, boolean argmin/argmax, triu/tril, masked sorting can be expected to perform significantly better in many cases. Also NumPy 1.9.0 releases the GIL for more functions, most notably indexing now releases it and the random modules state object has a private lock instead of using the GIL. This allows leveraging pure python threads more efficiently. In order to make working with arrays containing NaN values easier nanmedian and nanpercentile have been added which ignore these values. These functions and the regular median and percentile now also support generalized axis arguments that ufuncs already have, these allow reducing along multiple axis in one call. Please see the release notes for all the details. Please also take not of the many small compatibility changes and deprecation in the notes. https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-not… The source tarballs and win32 binaries can be downloaded here: https://sourceforge.net/projects/numpy/files/NumPy/1.9.0 Cheers, The NumPy Development Team

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SFMT (faster mersenne twister)
by Neal Becker 05 Sep '14

05 Sep '14

http://www.math.sci.hiroshima-u.ac.jp/~%20m-mat/MT/SFMT/index.html -- -- Those who don't understand recursion are doomed to repeat it

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'norm' keyword for FFT functions
by Joseph Martinot-Lagarde 04 Sep '14

04 Sep '14

I have an old PR [1] to fix #2142 [2]. The idea is to have a new keyword for all fft functions to define the normalisation of the fft: - if 'norm' is None (the default), the normalisation is the current one: fft() is not normalized ans ifft is normalized by 1/n. - if norm is "ortho", the direct and inverse transforms are both normalized by 1/sqrt(n). The results are then unitary. The keyword name and value is consistent with scipy.fftpack.dct. Do you feel that it should be merged ? Joseph [1] https://github.com/numpy/numpy/pull/3883 [2] https://github.com/numpy/numpy/issues/2142

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Multiple comment tokens for loadtxt
by Joseph Martinot-Lagarde 04 Sep '14

04 Sep '14

loadtxt currently has a keyword to change the comment token. The PR #4612 [1] enables to define multiple comment token for a file. It is motivated by #2633 [2] What is your position on this one ? Joseph [1] https://github.com/numpy/numpy/pull/4612 [2] https://github.com/numpy/numpy/issues/2633

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Does a `mergesorted` function make sense?
by Jaime Fernández del Río 04 Sep '14

04 Sep '14

A request was open in github to add a `merge` function to numpy that would merge two sorted 1d arrays into a single sorted 1d array. I have been playing around with that idea for a while, and have a branch in my numpy fork that adds a `mergesorted` function to `numpy.lib`: https://github.com/jaimefrio/numpy/commit/ce5d480afecc989a36e5d2bf4ea1d1ba5… I drew inspiration from C++ STL algorithms, and merged into a single function what merge, set_union, set_intersection, set_difference and set_symmetric_difference do there. My first thought when implementing this was to not make it a public function, but use it under the hood to speed-up some of the functions of `arraysetops.py`, which are now merging two already sorted functions by doing `np.sort(np.concatenate((a, b)))`. I would need to revisit my testing, but the speed-ups weren't that great. One other thing I saw value in for some of the `arraysetops.py` functions, but couldn't fully figure out, was in providing extra output aside from the merged arrays, either in the form of indices, or of boolean masks, indicating which items of the original arrays made it into the merged one, and/or where did they end up in it. Since there is at least one other person out there that likes it, is there any more interest in such a function? If yes, any comments on what the proper interface for extra output should be? Although perhaps the best is to leave that out for starters and see what use people make of it, if any. Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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Give Jaime Fernandez commit rights.
by Charles R Harris 04 Sep '14

04 Sep '14

Hi All, I'd like to give Jaime commit rights. Having at three active developers with commit rights is the goal and Jaime has been pretty consistent with code submissions and discussion participation. Thoughts? Chuck

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