Mailman 3 November 2014 - NumPy-Discussion

FFTS for numpy's FFTs (was: Re: Choosing between NumPy and SciPy functions)
by Nathaniel Smith 11 Dec '14

11 Dec '14

On 28 Oct 2014 04:07, "Matthew Brett" <matthew.brett(a)gmail.com> wrote: > > Hi, > > On Mon, Oct 27, 2014 at 8:07 PM, Sturla Molden <sturla.molden(a)gmail.com> wrote: > > Sturla Molden <sturla.molden(a)gmail.com> wrote: > > > >> If we really need a > >> kick-ass fast FFT we need to go to libraries like FFTW, Intel MKL or > >> Apple's Accelerate Framework, > > > > I should perhaps also mention FFTS here, which claim to be faster than FFTW > > and has a BSD licence: > > > > http://anthonix.com/ffts/index.html > > Nice. And a funny New Zealand name too. > > Is this an option for us? Aren't we a little behind the performance > curve on FFT after we lost FFTW? It's definitely attractive. Some potential issues that might need dealing with, based on a quick skim: - seems to have a hard requirement for a processor supporting SSE, AVX, or NEON. No fallback for old CPUs or other architectures. (I'm not even sure whether it has x86-32 support.) - no runtime CPU detection, e.g. SSE vs AVX appears to be a compile time decision - not sure if it can handle non-power-of-two problems at all, or at all efficiently. (FFTPACK isn't great here either but major regressions would be bad.) - not sure if it supports all the modes we care about (e.g. rfft) This stuff is all probably solveable though, so if someone has a hankering to make numpy (or scipy) fft dramatically faster then you should get in touch with the author and see what they think. -n

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taking a 2D uneven surface slice
by Bryan Woods 04 Dec '14

04 Dec '14

I'm trying to do something that at first glance I think should be simple but I can't quite figure out how to do it. The problem is as follows: I have a 3D grid Values[Nx, Ny, Nz] I want to slice Values at a 2D surface in the Z dimension specified by Z_index[Nx, Ny] and return a 2D slice[Nx, Ny]. It is not as simple as Values[:,:,Z_index]. I tried this: >>> values.shape (4, 5, 6) >>> coords.shape (4, 5) >>> slice = values[:,:,coords] >>> slice.shape (4, 5, 4, 5) >>> slice = np.take(values, coords, axis=2) >>> slice.shape (4, 5, 4, 5) >>> Obviously I could create an empty 2D slice and then fill it by using np.ndenumerate to fill it point by point by selecting values[i, j, Z_index[i, j]]. This just seems too inefficient and not very pythonic.

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Setting up a "newcomers" label on the issue tracker ?
by David Cournapeau 01 Dec '14

01 Dec '14

Hi, Would anybody mind if I create a label "newcomers" on GH, and start labelling simple issues ? This is in anticipation to the bloomberg lab event in London this WE. I will try to give a hand to people interested in numpy/scipy, David

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ANN: Bokeh 0.6.1 release
by Damian Avila 01 Dec '14

01 Dec '14

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6.1! 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 point release includes several bug fixes and improvements over our most recent 0.6.0 release: * Toolbar enhancements * bokeh-server fixes * Improved documentation * Button widgets * Google map support in the Python side * Code cleanup in the JS side and examples * New examples See the CHANGELOG for full details. 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 or miniconda, 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-0.6.1.min.js http://cdn.pydata.org/bokeh-0.6.1.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! Cheers, Damián Avila damian.avila(a)continuum.io

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GDB macros for numpy
by David Cournapeau 01 Dec '14

01 Dec '14

Hi there, I remember having seen some numpy-aware gdb macros at some point, but cannot find any reference. Does anyone know of any ? David

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Maximum-margin linear classifier
by Mohan Radhakrishnan 30 Nov '14

30 Nov '14

Hi, I am a beginner. I have 4 points and wish to plot the classifier separation line. I believe this code without the plot is correct. Can I look at sample plotting code(with basic explanation) for this simple SVM ? X = [[3,3], [7,2], [5,4], [8,3]] Y = [-1, -1, 1, 1] clf = svm.SVC(kernel='linear',C=2) clf.fit(X,Y) for i in range(4): x = np.array(X[i])[0]*clf.coef_[0][0] + np.array(X[i])[1]*clf.coef_[0][1] + clf.intercept_; print x print clf.intercept_ print clf.coef_ Thanks, Mohan

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Re: [Numpy-discussion] packbits / unpackbits bugs or misfeatures?
by Alan G Isaac 30 Nov '14

30 Nov '14

Six years ago Zachary Pincus wrote: > it would kind of make more sense, at least to me, for > unpackbits to return a boolean array, and packbits to take > boolean arrays. I could try to look into fixing the code > instead of the docs, if others agree. I agree with this. (Better late than never?) More relevant at this point: it seems very odd that `packbits` does not *accept* a boolean array, even if it does not insist upon one. Might this be considered a bug? Cheers, Alan Isaac

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Finding values in an array
by Alexander Belopolsky 29 Nov '14

29 Nov '14

I probably miss something very basic, but how given two arrays a and b, can I find positions in a where elements of b are located? If a were sorted, I could use searchsorted, but I don't want to get valid positions for elements that are not in a. In my case, a has unique elements, but in the general case I would accept the first match. In other words, I am looking for an array analog of list.index() method.

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Updates on NA / NaN values for Ints?
by Nick Eubank 29 Nov '14

29 Nov '14

Hello Numpy! Curious if there were any timelines for when NA / NaN support might arrive for Ints. (I'm sure this is asked and answered many times, but what I can find seems very out of date.) I'm strongly considering migrating from R/Stata/Matlab to Pandas/Numpy, but the inability to handle Na in Int types seems like a big limitation for someone always working with dirty data like me (a social scientist), and the Pandas docs suggest they're waiting for Numpy to add support. Just curious when that might happen! Thanks! nick

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ANN: SfePy 2014.4
by Robert Cimrman 28 Nov '14

28 Nov '14

I am pleased to announce release 2014.4 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 release -------------------------- - preliminary support for 1D problems - data probes using pyVTK library For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Best regards, Robert Cimrman and Contributors (*) (*) Contributors to this release (alphabetical order): Lubos Kejzlar, Vladimir Lukes

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