Mailman 3 November 2016 - SciPy-Dev

FFTLog
by Dieter Werthmüller 17 Apr '17

17 Apr '17

Dear SciPy-devs, I recently had the need for a logarithmic FFT routine, and did a quick f2py around Andrew Hamilton's FFTLog: http://casa.colorado.edu/~ajsh/FFTLog I put my f2py-setup and my pyf-file up on GitHub: https://github.com/prisae/fftlog I thought it might be a useful addition to the SciPy FFTPack. Specifically as three out of the five fortran-files, of which FFTLog consists, are already in scipy/fftpack. I have never contributed to SciPy and therefore do not know how much work it would involve to get it into SciPy. However, I thought I ask. If there is interest I expect that it would not take long for an experienced person, as it is a fairly small addition. Or someone could point me to the right direction on what to do to get it in. I append some comments on the involved files of FFTLog and some comments regarding their licenses. I believe it is compatible with SciPy. Thanks for all your good work! Dieter Files and Licenses/Permissions ------------------------------ Files of FFTLog [1] cdgamma.f [2] drfftb.f [3] drfftf.f [4] drffti.f [5] fftlog.f (plus a test routine, fftlogtest.f) -- [1] -- The original FFTLog states about this file: FFTLog uses [...] and a modified version of the complex Gamma function from the gamerf package at momonga.t.u-tokyo.ac.jp/~ooura/gamerf.html. The original gamerf copyright statement states: Copyright(C) 1996 Takuya OOURA (email: ooura(a)mmm.t.u-tokyo.ac.jp). You may use, copy, modify this code for any purpose and without fee. You may distribute this ORIGINAL package. Permission to distribute the modified gamma function code with the FFTLog package has been granted (email from Takuya Ooura to Andrew Hamilton dated 16 March 1999). Hence I think it is compatible with SciPy, or am I wrong? Alternatively it could be replaced with scipy.special.loggamma, I think, but I do not know if it is possible to mix Fortran and Python code with f2py. -- [2], [3], [4] -- They are from the NCAR suite of FFT routines (Swarztrauber 1979). As far as I can see they are already in SciPy, in scipy/fftpack/src/dfftpack/ (dfftb.f, dfftf.f, and dffti.f). So there is definitely not a licensing problem here. -- [5] -- This is the actual FFTLog routine from Hamilton. I asked Hamilton for permission before publishing his source code at github.com/prisae/fftlog. His email response was: -- start email from 28/09/2016 -- Dieter, You are welcome to use fftlog in any way you choose. Please note the credits commented in the code: c FFTLog uses the NCAR suite of FFT routines, c and a modified version of the complex Gamma function c from the gamerf package at c http://momonga.t.u-tokyo.ac.jp/~ooura/gamerf.html . c The original gamerf copyright statement states: c Copyright(C) 1996 Takuya OOURA (email: ooura(a)mmm.t.u-tokyo.ac.jp). c You may use, copy, modify this code for any purpose and c without fee. You may distribute this ORIGINAL package. c c Permission to distribute the modified gamma function code c with the FFTLog package has been granted c (email from Takuya Ooura to Andrew Hamilton dated 16 March 1999). Andrew -- end email from 28/09/2016 --

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dense output option in integrate.ode
by Evgeni Burovski 20 Feb '17

20 Feb '17

Hi David, Hi Jim, > I am new to SciPy-dev. Will the dense output option in scipy.integrate.ode > become available in 0.17.0? This is a feature already available in the > original FORTAN, but wasn't implemented in the wrapper. If the feature is sent as a pull request against the scipy master branch, the PR is reviewed by the maintainers of the integrate package and merged into master before the release split, then yes, it would be available in 0.17.0. So far I do not see any progress towards it. > I wrote these dense output extensions that you listed: > > https://github.com/jddmartin/scipy/tree/dense_output_from_dopri5_and_dop853 > https://github.com/jddmartin/dense_output_example_usage > > but I didn't issue any pull request to scipy. I sent this message to the > scipy developers list: > http://article.gmane.org/gmane.comp.python.scientific.devel/19635/ > explaining the changes. I was hoping for some feedback before issuing a > pull request. Ah, I see that the email likely fell through the cracks back in April. Sorry about that. You might want to ping that email thread once more or send a pull request on github (or both). <snip> Cheers, Evgeni

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SciPy governance model
by Ralf Gommers 18 Jan '17

18 Jan '17

Hi all, Starting with the summary of my email of earlier today: I'd like to push on with agreeing on a governance model and document. We had some discussions and a hangout on that last year [1]. In the hangout we decided to give people some time to read up on provided info on how this worked in other projects, the Karl Fogel book, etc. I was supposed to organize a follow-up, but failed to do so until now. I will send a separate email about this shortly. We're now at a point where most other major projects in the scientific Python ecosystem have a formal governance model. Many are modeled after the Jupyter one [2], which defines a BDFL, a steering council and contributors, and a voting system to make decisions (simplified, there's much more - the whole document is worth reading). NumPy chose another model, with a steering council and consensus-based decision making [3]. >From the outside, the Jupyter model seems to be working for them. The NumPy model hasn't been exercised too much yet, but should work well too. There are variations on those two models in use as well (like the Jupyter model minus the BDFL). An email discussion on this topic without a concrete proposal or summary document is likely to go on for a long time and may not converge easily. A video conference is also tricky, with dates/timezones and discussion often going off on tangents. So I have the following proposal: - We start by drafting an extended summary of the various models and getting a sense of which model the core team prefers. - We then work out that model, and bring it back to this list for discussion/finetuning/acceptance. - "We" here is the group of people who indicate, on this list or to me off-list, that they want to participate by the end of this week. Thoughts? Volunteers? Ralf [1] https://mail.scipy.org/pipermail/scipy-dev/2015-April/020636.html [2] Jupyter governance doc: https://github.com/jupyter/governance [3] NumPy governance doc: http://docs.scipy.org/doc/numpy-dev/dev/governance/index.html [4] Producing Open Source software (Karl Fogel): http://producingoss.com/

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signal.spectrogram doesn't change nperseg to length of window?
by David Nicholson 27 Nov '16

27 Nov '16

Hi scipy-dev list, I'm using spectrogram from scipy.signal with a custom window. My understanding from the docs is that when I provide an array_like for the window, the value for nperseg will change from the default of 256 to whatever the length is of the window I provide. But instead I get an error saying 'noverlap must be less than nperseg' and when I check with pdb I can see that nperseg is still the default value. I'm using a value for noverlap that's larger than the default nperseg, but less than the length of the window. I don't get the error if I call the function with 'nperseg=window.shape[0]'. I skimmed the source and don't see anything in the _spect_helper function that would change nperseg to the length of window. Am I misunderstanding the docs, or am I calling the function wrong, or is this a bug? Here's my code: from scipy.io import wavfile from scipy.signal import slepian # i.e. 'DPSS' window used for FFT from scipy.signal import spectrogram FFT_SIZE = 512 FFT_OVERLAP = FFT_SIZE - 32 win_dpss = slepian(FFT_SIZE, 4/FFT_SIZE) #shape = [rate,data] = wavfile.read('a_file_you_have.wav') f,t,spec = spectrogram(data, rate, window=win_dpss, noverlap=FFT_OVERLAP) Thanks for your help --David -- David Nicholson, Ph.D. Candidate Sober Lab <http://www.biology.emory.edu/research/Sober/Home.html>, Emory Neuroscience Program. <http://www.emory.edu/NEUROSCIENCE/> www.nicholdav.info; <http://www.nicholdav.info>https://github.com/NickleDave

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Re: [SciPy-Dev] SciPy-Dev Digest, Vol 157, Issue 15
by David Nicholson 22 Nov '16

22 Nov '16

Hi Eric, I think I can do that over the holiday. --David On Tue, Nov 22, 2016 at 7:00 AM, <scipy-dev-request(a)scipy.org> wrote: > Send SciPy-Dev mailing list submissions to > scipy-dev(a)scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.scipy.org/mailman/listinfo/scipy-dev > or, via email, send a message with subject or body 'help' to > scipy-dev-request(a)scipy.org > > You can reach the person managing the list at > scipy-dev-owner(a)scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of SciPy-Dev digest..." > > > Today's Topics: > > 1. Re: signal.spectrogram doesn't change nperseg to length of > window? (Eric Larson) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 21 Nov 2016 16:15:19 -0500 > From: Eric Larson <larson.eric.d(a)gmail.com> > To: SciPy Developers List <scipy-dev(a)scipy.org> > Subject: Re: [SciPy-Dev] signal.spectrogram doesn't change nperseg to > length of window? > Message-ID: > <CAGu2niVW-MWH4h9Hk+tP7dssDv9YJ6JhJrQOvei8NrRxHQoK > HQ(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > It looks like you are reading the docs correctly. From looking at the code, > I can confirm that it doesn't change `nperseg` based on what is passed for > `window`. So it looks like a bug to me. > > Do you have time to look into making a Pull Request to fix it? > > Eric > > > On Sat, Nov 19, 2016 at 10:41 PM, David Nicholson <nicholdav(a)gmail.com> > wrote: > > > Hi scipy-dev list, > > > > I'm using spectrogram from scipy.signal with a custom window. My > > understanding from the docs is that when I provide an array_like for the > > window, the value for nperseg will change from the default of 256 to > > whatever the length is of the window I provide. > > > > But instead I get an error saying 'noverlap must be less than nperseg' > and > > when I check with pdb I can see that nperseg is still the default value. > > I'm using a value for noverlap that's larger than the default nperseg, > but > > less than the length of the window. I don't get the error if I call the > > function with 'nperseg=window.shape[0]'. > > > > I skimmed the source and don't see anything in the _spect_helper > function > > that would change nperseg to the length of window. > > > > Am I misunderstanding the docs, or am I calling the function wrong, or is > > this a bug? > > > > Here's my code: > > from scipy.io import wavfile > > from scipy.signal import slepian # i.e. 'DPSS' window used for FFT > > from scipy.signal import spectrogram > > > > FFT_SIZE = 512 > > FFT_OVERLAP = FFT_SIZE - 32 > > win_dpss = slepian(FFT_SIZE, 4/FFT_SIZE) #shape = > > > > [rate,data] = wavfile.read('a_file_you_have.wav') > > f,t,spec = spectrogram(data, rate, window=win_dpss, noverlap=FFT_OVERLAP) > > > > Thanks for your help > > --David > > > > -- > > David Nicholson, Ph.D. Candidate > > Sober Lab <http://www.biology.emory.edu/research/Sober/Home.html>, Emory > > Neuroscience Program. <http://www.emory.edu/NEUROSCIENCE/> > > www.nicholdav.info; <http://www.nicholdav.info> > > https://github.com/NickleDave > > > > _______________________________________________ > > SciPy-Dev mailing list > > SciPy-Dev(a)scipy.org > > https://mail.scipy.org/mailman/listinfo/scipy-dev > > > > >

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scipy.sparse.coo_matrix a new method drop_zero_columns?
by Evgeny Nekrasov 19 Nov '16

19 Nov '16

Hello, I am writing to follow up on the discussion https://github.com/scipy/ scipy/issues/6754 I would be happy to get your opinions. Thank you for your time, Evgeny

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Working on an issue
by Sourav Singh 18 Nov '16

18 Nov '16

Hello Everyone, I am writing this mail to ask about a pending Pull Request in SciPy. I am interested in working on this pull request https://github.com/scipy/scipy/pull/5775 which needs a contributor to finish up. I would appreciate it if someone can guide me on finishing up the pull request. Regards, Sourav

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NumPy 1.12.0b1 released.
by Charles R Harris 17 Nov '16

17 Nov '16

Hi All, I'm pleased to annouce the release of NumPy 1.12.0b1. This release supports Python 2.7 and 3.4 - 3.6 and is the result of 388 pull requests submitted by 133 contributors. It is quite sizeable and rather than put the release notes inline I've attached them as a file and they may also be viewed at Github <https://github.com/numpy/numpy/releases/tag/v1.12.0b1>. Zip files and tarballs may also be found the Github link. Wheels and source archives may be downloaded from PyPI, which is the recommended method. This release is a large collection of fixes, enhancements, and improvements and it is difficult to select just a few as highlights. However, the following enhancements may be of particular interest - Order of operations in ``np.einsum`` now can be optimized for large speed improvements. - New ``signature`` argument to ``np.vectorize`` for vectorizing with core dimensions. - The ``keepdims`` argument was added to many functions. - Support for PyPy 2.7 v5.6.0 has been added. While not complete, this is a milestone for PyPy's C-API compatibility layer. Thanks to all, Chuck

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Differentiate function
by Robert Rehammar 12 Nov '16

12 Nov '16

Dear all, I implemented a simple function to differentiate an array which seems other people might like to have. It looks like: def diff(f, x, retval = 'same'): """ Differentiate f w.r.t. x and return the diff with same samples, or different depending on retval. retval can be: same: return data for same data points as the indata. This is done through interpolation at internal points and extrapolation at endpoint simple: just return the scaled diff samesimple: return with same size vector, just shifted. Last data point is repeated. new: return at new datapoints and length - 1 """ df = f[1:] - f[:-1] dx = x[1:] - x[:-1] dfodx = df/dx if retval == 'new': return dfodx, x[:-1] + dx/2 elif retval == 'same': cdfodx = np.zeros(len(f)) cdfodx[1:-1] = (dfodx[:-1] + dfodx[1:])/2 cdfodx[0] = dfodx[0]-0.5*(dfodx[1] - dfodx[0])/dx[0] cdfodx[-1] = dfodx[-1]+0.5*(dfodx[-1] - dfodx[-2])/dx[-1] return cdfodx, x elif retval == 'simple': return dfodx, x[:-1] elif retval == 'samesimple': cdfodx = np.zeros(len(f)) cdfodx[:-1] = dfodx cdfodx[-1] = dfodx[-1] return cdfodx, x And generates the following results (top plot original function, bottom different differentiates) from the code blow: x = np.linspace(0,2*np.pi,10) f = np.sin(x) plt.subplot(211) plt.plot(x, f, marker="*") plt.ylabel('$\sin(y)$') plt.grid(True) plt.subplot(212) df,xp = diff(f, x, 'simple') plt.plot(xp, df, marker="*", label='simple', linewidth=4) df,xp = diff(f, x, 'new') plt.plot(xp, df, marker="*", label='new') df,xp = diff(f, x, 'same') plt.plot(xp, df, marker="*", label='same') df,xp = diff(f, x, 'samesimple') plt.plot(xp, df, marker="*", label='samesimple') plt.ylabel('$d\sin(x)/dx$') plt.xlabel('$x$') plt.grid(True) plt.legend(loc='lower right') What do you think about adding it to scipy? Best, Robert

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Jupyter Notebook for the beginner
by Evgeniy 10 Nov '16

10 Nov '16

My intention is to implement a web application with Jupyter Notebook, which will start by clicking a link on some page. To start with this I need clarifications from the experts: - is it possible to run the code in Jupyter Notebook immediately after connection. Say, when the user clicks a link, then Jupyter Notebook is opened in new tab and immediately shows the result. - is it possible to put matplotlib drawing (which shows the results) at the top of the notebook? Thus, after immediate execution the users will see the top part of the screen with the results and they should not care about the rest of the screen with Python code. - is it possible to make Python code invisible for users and leave only drawing (after releasing the notebook into production for the users)? - is it possible to create GUI for Jupyter Notebook (buttons, maybe sliders, readback fields)? What can be used for this? Thanks, Evgeniy

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