Mailman 3 August 2017 - SciPy-Dev

scipy.io.loadmat and matvec multiply
by Jonathan Taylor Aug. 28, 2017

Aug. 28, 2017

I've got a largeish array that I have saved in a .MAT file that I need to use for matvec multiply several times. It seems that if I copy the array before running the matvec I get a significant speedup. Is this known? I can attach a link to a particular .MAT file if helpful. -- Jonathan Taylor Dept. of Statistics Sequoia Hall, 137 390 Serra Mall Stanford, CA 94305 Tel: 650.723.9230 Fax: 650.725.8977 Web: http://www-stat.stanford.edu/~jtaylo

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Scipy Windows wheels
by Pauli Virtanen Aug. 25, 2017

Aug. 25, 2017

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Dear all, Prerelease binary wheels for Scipy on Windows 32-bit & 64-bit are now available in case you would like to test them. Currently, the plans are that binary wheels will also be provided for future releases on PyPi, so that you will be able to do simply "pip install scipy" also on Windows. At least, assuming we manage to test these wheels well enough for which help would be useful. You can install the scipy prerelease packages as shown below. Note that they are meant for testing only, and correspond to the current Scipy development version. Please report issues found on the Scipy issue tracker on github (be sure to mention how you installed scipy and python). The wheels are meant to be used with the Python obtained from https://p ython.org --- these are not meant to be used with e.g. Conda, although it may be they work. The work leading to a viable automatized compilation approach was done in https://github.com/scipy/scipy/pull/7616 https://github.com/numpy/numpy/pull/9431 Example: C:\Users\pauli\src\env2\Scripts>pip install -f https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackc… --pre scipy Collecting scipy Downloading https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackc… (26.0MB) 100% |████████████████████████████████| 26.0MB 47kB/s Collecting numpy>=1.8.2 (from scipy) Downloading https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackc… (6.8MB) 100% |████████████████████████████████| 6.9MB 168kB/s Installing collected packages: numpy, scipy Successfully installed numpy-1.14.0.dev0+707f33f scipy-1.0.0.dev0+2a1fdcf C:\Users\pauli\src\env2\Scripts>python Python 3.6.2 (v3.6.2:5fd33b5, Jul 8 2017, 04:14:34) [MSC v.1900 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import scipy.integrate, scipy.linalg, numpy as np >>> scipy.integrate.quad(lambda x: 1/(1 + x**2), -np.inf, np.inf) (3.141592653589793, 5.155583041103855e-10) >>> scipy.linalg.eigvals([[1,0],[0,2]]) array([ 1.+0.j, 2.+0.j]) >>> exit() - -- Pauli Virtanen -----BEGIN PGP SIGNATURE----- iQIzBAEBCAAdFiEEiNDtwDFNJaodBiJZHOiglY3YpVcFAlmfWaUACgkQHOiglY3Y pVfxSg//VvwYDanA/tam34I2hrwQQFtQaW3E/ikqNAM91XvQwluc9RsVVDx2zspk 9ywXyfFbnNpMK+/emow8o6kuaFes1NvutobbAOQ4L9jj0ofD9pCWVM6SLkRIkSea km7RdK13vpWtrghPkvkqGFNhY2eDSuV4S8qeR+78KwSUADYjB0m1Yfpfm6LtUKOX tKwGhDGWzi1vcBPJqgQQYJDjBbVNbY5aao6QnjLeNkgXW6RZYhxUyBeWph7GPrEL pNFvoOknjxa5nItZvt948+7PsgZZarHGlyqeAy8Nb0Bkukm1Uovo7V5gMfiDS6nT cA2xNkELh9Zoyr+9kRaaDh2B0U6qWPOiU/IE6VvCK72N70tzdi59a0GkzLzVen2b hgK5RBsa5fL9sxo4oN/bcApnUp6K98XAV4eJhIlZPbnnvSfqbKobX7D1G+qBokBN 90XnWLUjkJpzr1emqyrQPVbrd8OflIhs2aQv0l5gZKrXuBgGFgoCDwEJmrzd6K+n 1iLr73BuZEFN/jLvT9cx+XbAQkXhCbD2hL4ly0u7BuBzAbOE19iugSnap/sjueRW FlOKSddobW86TeOICKurH9TCcFRu6mu1tQvCkucqkY49gXpu3srzUcdog9gQe46H 2JFNQICFaYWhF7jVY9cwOXssEHOc6PCa0FdOxMX5W/p5k0xuqzA= =tjJg -----END PGP SIGNATURE-----

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NetworkX 2.0b1 released
by Jarrod Millman Aug. 18, 2017

Aug. 18, 2017

Hi All, I am happy to announce the **beta** release of NetworkX 2.0! NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. This release supports Python 2.7 and 3.4-3.6 and contains many new features. This release is the result of over two years of work with over 600 pull requests by 85 contributors. We have made **major changes** to the methods in the Multi/Di/Graph classes and before the 2.0 release we need feedback on those changes. If you have code that imports networkx, please take some time to check that you are able to update your code to work with the new release. Please see the draft of the 2.0 release announcement: http://networkx.readthedocs.io/en/latest/news.html#networkx-2-0 In particular, we would like feedback on the migration guide from 1.X to 2.0: http://networkx.readthedocs.io/en/latest/release/migration_guide_from_1.x_t… Since it is a beta release, pip won't automatically install it. So $ pip install networkx still installs networkx-1.11 still. But $ pip install --pre networkx will install networkx-2.0b1. If you already have networkx installed then you need to do $ pip install --pre --upgrade networkx For more information, please visit our `website <http://networkx.github.io/>`_ and our `gallery of examples <http://networkx.readthedocs.io/en/latest/auto_examples/index.html>`_. Please send comments and questions to the `networkx-discuss mailing list <http://groups.google.com/group/networkx-discuss>`_ or create an issue `here <https://github.com/networkx/networkx/issues>`_. Best regards, Jarrod

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1/f noise generation
by Phillip Feldman Aug. 11, 2017

Aug. 11, 2017

Firstly, white noise means only that the power spectral density is flat, or equivalently, that the autocorrelation function is zero everywhere except at lag zero.One can generate Gaussian noise with an arbitrary power spectral density by filtering white Gaussian noise with a suitable filter. The output of the filter is the convolution of the impulse response of the filter with the autocorrelation function of white noise, which gives us the impulse response of the filter. So, from the convolution theorem and the definition of the PSD, the PSD of the output is the squared magnitude of the frequency response of filter.An alternative method for synthesizing Gaussian noise with an arbitrary power spectral density is to generate tones with unity amplitude and random phase, where the probability of picking any particular tone frequency is proportional to the height of the PSD. The filter method tends to be more computationally efficient. Dr. Phillip M. Feldman *Suzen, Mehmet* msuzen at gmail.com <scipy-dev%40python.org?Subject=Re%3A%20%5BSciPy-Dev%5D%201/f%20noise%20generation&In-Reply-To=%3CCAPtbhHwp3dq6jiWEDhAbVn45ruUXKW%3D0%2BoD1PKvnHVdKjaw7GA%40mail.gmail.com%3E> *Tue Aug 8 08:15:33 EDT 2017* - Previous message (by thread): [SciPy-Dev] 1/f noise generation <https://mail.python.org/pipermail/scipy-dev/2017-August/022035.html> - *Messages sorted by:* [ date ] <https://mail.python.org/pipermail/scipy-dev/2017-August/date.html#22036> [ thread ] <https://mail.python.org/pipermail/scipy-dev/2017-August/thread.html#22036> [ subject ] <https://mail.python.org/pipermail/scipy-dev/2017-August/subject.html#22036> [ author ] <https://mail.python.org/pipermail/scipy-dev/2017-August/author.html#22036> ------------------------------ On 8 August 2017 at 12:58, Neal Becker <ndbecker2 at gmail.com <https://mail.python.org/mailman/listinfo/scipy-dev>> wrote: >* I think you mean non-white, not nongaussian * Excellent point. white noise means constant PSD, not only Gaussian.

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SciPy roadmap does not address limitations of current filter-related code
by Phillip Feldman Aug. 11, 2017

Aug. 11, 2017

Some of us in the engineering community are unhappy with the SciPy roadmap. >From our perspective, the main deficiency of scipy.signal is that the filtering functionality is of limited practical utility. We would like to see the following: (1) support for lowpass, bandpass, and bandstop FIR filter design, with the user specifying (a) the passband ripple, (b) the minimum stopband rejection, and (c) the design method, with this last being optional. Rather than forcing the user to specify the order of the filter, which requires many iterations to determine the minimum filter order that will do the job, we'd like to see the code automatically determine the minimum filter order that can meet the user's specs. (2) support for fixed-binary-point arithmetic. (3) support for filtering and the design of filters that use fixed-binary-point arithmetic. Such changes would be a big step in the direction of making Python+NumPy+SciPy a viable alternative to Matlab + the Matlab Signal Processing Toolbox. As an aside, I'd like to comment on the documentation for `scipy.signal.kaiserord`, which says the following: scipy.signal.kaiserord(ripple, width)[source] <snip> ripple : float Positive number specifying maximum ripple in passband (dB) and minimum ripple in stopband. When designing a lowpass digital filter, one normally specifies the maximum ripple in the passband and the minimum rejection in the stopband. With this function, there is no way to specify how much rejection one gets in the stopband, and the filter design code is also apparently trying to limit stopband ripple, which is something that no engineer would care about. The documentation can't just be badly worded, because there would have to be another parameter to specify the stopband rejection. Phillip -- Dr. Phillip M. Feldman

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1/f noise generation
by Timothy Mehay Aug. 8, 2017

Aug. 8, 2017

Dear All, During the course of using numpy/scipy for data processing I've noticed a lack of routines for generating non Gaussian noise processes. I'm interested in contributing code for the generation of 1/f (pink) noise, and perhaps other noise processes, similar to the matlab dsp.ColoredNoise routines. Any thoughts on whether such an addition would be worthwhile? Regards Tim

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Re: [SciPy-Dev] 1/f noise generation
by Andrew Nelson Aug. 8, 2017

Aug. 8, 2017

Don't forget the random number generation from rv_continuous in scipy.stats. On 8 Aug 2017 9:01 pm, "Neal Becker" <ndbecker2(a)gmail.com> wrote: I think you mean non-white, not nongaussian On Tue, Aug 8, 2017, 5:18 AM Timothy Mehay <446368(a)students.wits.ac.za> wrote: > Dear All, > > During the course of using numpy/scipy for data processing I've noticed a > lack of routines for generating non Gaussian noise processes. > > I'm interested in contributing code for the generation of 1/f (pink) > noise, and perhaps other noise processes, similar to the matlab > dsp.ColoredNoise routines. > > Any thoughts on whether such an addition would be worthwhile? > > Regards > Tim > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev(a)python.org > https://mail.python.org/mailman/listinfo/scipy-dev > _______________________________________________ SciPy-Dev mailing list SciPy-Dev(a)python.org https://mail.python.org/mailman/listinfo/scipy-dev

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