Mailman 3 - SciPy-User

Schumaker quadratic spline
by Mark Mikofski 10 May '19

10 May '19

Hi All, Sorry for this unresearched and inexperienced question. Does anyone have any experience with the "Schumaker quadratic spline" for 1-D interpolation? Is it similar enough to the BSpline already in SciPy? I have some source code that I can contribute if there's interest. As far as references, a google search turned up an R function: * https://cran.r-project.org/web/packages/schumaker/vignettes/schumaker.html -- Mark Mikofski, PhD (2005) *Fiat Lux*

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EuroSciPy 2019 - extension of deadline
by Pierre de Buyl 03 May '19

03 May '19

Hi all, The call for proposals for EuroSciPy 2019 is extended to 12 may 2019. https://pretalx.com/euroscipy-2019/cfp EuroSciPy 2019 takes place 2-6 September 2019 in Bilbao, Spain. There will be two days of tutorials, two days of conference, and one day of sprints! Find us on the web https://www.euroscipy.org/2019/ and on twitter https://twitter.com/EuroSciPy Regards, The EuroSciPy 2019 team

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NumPy 1.16. 3 release
by Charles R Harris 22 Apr '19

22 Apr '19

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.16.3 which contains several fixes for newly reported bugs. The Python versions supported in this release are 2.7 and 3.5-3.7. There are several notable changes. - Unpickling object arrays when loading *.npy or *.npz files now requires an explicit opt-in. This backwards incompatible change was made in response to CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>. - The covariance in 'random.mvnormal' is cast to double. This should make the tolerance more useful when checking the singular values of the covariance matrix. - The '__array_interface__' offset now works as documented. The offset value was being mistakenly ignored. Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.4. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.3/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.3>. If you are installing using pip, you may encounter a problem with older installed versions of NumPy that pip did not delete becoming mixed with the current version, resulting in an ``ImportError``. That problem is particularly common on Debian derived distributions due to a modified pip. The fix is to make sure all previous NumPy versions installed by pip have been removed. See #12736 <https://github.com/numpy/numpy/issues/12736> for discussion of the issue. *Contributors* A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Andreas Schwab - Bharat Raghunathan + - Bran + - Charles Harris - Eric Wieser - Jakub Wilk - Kevin Sheppard - Marten van Kerkwijk - Matti Picus - Paul Ivanov - Ralf Gommers - Sebastian Berg - Tyler Reddy - Warren Weckesser - Yu Feng - adeak + *Pull requests merged* A total of 26 pull requests were merged for this release. - gh-13072 : BUG: Fixes to numpy.distutils.Configuration.get_version (#13056) - gh-13082 : BUG: Fix errors in string formatting while producing an error - gh-13083 : BUG: Convert fortran flags in environment variable - gh-13084 : BUG: Remove error-prone borrowed reference handling - gh-13085 : BUG: Add error checks when converting integers to datetime types - gh-13091 : BUG: Remove our patched version of `distutils.split_quoted` - gh-13141 : BUG: Fix testsuite failures on ppc and riscv - gh-13142 : BUG: Fix parameter validity checks in ``random.choice`` - gh-13143 : BUG: Ensure linspace works on object input. - gh-13144 : BLD: fix include list for sdist building. - gh-13145 : BUG: __array_interface__ offset was always ignored - gh-13274 : MAINT: f2py: Add a cast to avoid a compiler warning. - gh-13275 : BUG, MAINT: fix reference count error on invalid input to ndarray.flat - gh-13276 : ENH: Cast covariance to double in random mvnormal - gh-13278 : BUG: Fix null pointer dereference in PyArray_DTypeFromObjectHelper - gh-13339 : BUG: Use C call to sysctlbyname for AVX detection on MacOS. - gh-13340 : BUG: Fix crash when calling savetxt on a padded array - gh-13341 : BUG: ufunc.at iteration variable size fix - gh-13342 : DOC: Add as_ctypes_type to the documentation - gh-13350 : BUG: Return the coefficients array directly - gh-13351 : BUG/MAINT: Tidy typeinfo.h and .c - gh-13359 : BUG: Make allow_pickle=False the default for loading - gh-13360 : DOC: fix some doctest failures - gh-13363 : BUG/MAINT: Tidy typeinfo.h and .c - gh-13381 : BLD: address mingw-w64 issue. Follow-up to gh-9977 - gh-13382 : REL: Prepare for the NumPy release. Cheers, Charles Harris

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Why stats.fisher_exact gives results different from R?
by Peng Yu 06 Apr '19

06 Apr '19

See below for the output of python and R. Why there is a difference in the odds ratio? Given that R is most widely used by statisticians, I'd prefer the python version print the same results. Also, confidence intervals are missing in the python's output. >>> stats.fisher_exact([[8, 2], [1, 5]]) (20.0, 0.03496503496503495) R> fisher.test(rbind(c(8, 2), c(1,5))) Fisher's Exact Test for Count Data data: rbind(c(8, 2), c(1, 5)) p-value = 0.03497 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 1.008849 1049.791446 sample estimates: odds ratio 15.46969 -- Regards, Peng

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EuroSciPy 2019 - CfP is open
by Alexander CS Hendorf 02 Apr '19

02 Apr '19

===================================================== *** Apologise for multiple posting *** Dear Colleagues, We are delighted to invite you to join us for the 12th European Conference on Python in Science. The EuroSciPy 2019 Conference will take place from September 2 to September 6 in Bilbao, Basque Country, Spain. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. The conference will be structured as it follows: Sep, 2-3 : Tutorials and Hands-on Sep, 4-5 : Main Conference Sep, 6 : Sprints ------------------------------------------------------------------ TOPICS OF INTEREST: Presentations of scientific tools and libraries using the Python language, including but not limited to: Algorithms implemented or exposed in Python Astronomy Data Visualisation Deep Learning & AI Earth, Ocean and Geo Science General-purpose Python tools that can be of special interest to the scientific community. Image Processing Materials Science Parallel computing Political and Social Sciences Project Jupyter Reports on the use of Python in scientific achievements or ongoing projects. Robotics & IoT Scientific data flow and persistence Scientific visualization Simulation Statistics Vector and array manipulation Web applications and portals for science and engineering 3D Printing ------------------------------------------------------------------- CALL FOR PROPOSALS: EuroScipy will accept three different kinds of contributions: Regular Talks: standard talks for oral presentations, allocated in time slots of `15`, or `30` minutes, depending on your preference and scheduling constraints. Each time slot considers a Q&A session at the end of the talk (at least, 5 mins). Hands-on Tutorials: These are beginner or advanced training sessions to dive into the subject with all details. These sessions are 90 minutes long, we expect participants to bring their own laptop for the tutorials. For a sneak peak of last years tutorials, here are the videos: https://www.youtube.com/channel/UCruMegFU9dg2doEGOUaAWTg Poster: EuroScipy will host two poster sessions during the two days of Main Conference. We warmly encourage students and participants to present their work and/or preliminary results as posters. Proposals should be submitted using the EuroScipy submission system at https://pretalx.com/euroscipy-2019/cfp. Submission deadline is April, 28 2019. ------------------------------------------------------------------ REGISTRATION & FEES: The registration fees will be as moderate as in previous years, we'll keep you posted. ------------------------------------------------------------------ IMPORTANT DATES: Mar 26 Call for talks, posters & tutorials Apr 28 Submission deadline for talks and tutorials May 20 Notifications of submission acceptance Sept 2 Start of EuroSciPy Tutorial Sept 4 Start of EuroSciPy Main Conference Sept 6 EuroSciPy Sprints ------------------------------------------------------------------ Best regards, EuroScipy 2019 Organising Committee, Email: info(a)euroscipy.org Website: http://www.euroscipy.org/2019 twitter: @euroscipy

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ANN: SfePy 2019.1
by Robert Cimrman 26 Mar '19

26 Mar '19

I am pleased to announce release 2019.1 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 (limited support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------- - automatic fallback for linear solvers - quadratic eigenvalue problem solver For full release notes see [1]. Cheers, Robert Cimrman [1] http://docs.sfepy.org/doc/release_notes.html#id1 --- Contributors to this release in alphabetical order: Robert Cimrman Lubos Kejzlar Vladimir Lukes

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Re: [SciPy-User] Discrete Fourier transforms (scipy.fftpack)- radially averaged power spectrum(RAPS)
by Eyal Saiet 22 Mar '19

22 Mar '19

Checking if this email made it through the scipy community!? On Sat, Mar 16, 2019 at 4:49 PM Eyal Saiet <ejsaiet(a)alaska.edu> wrote: > Hello, > The scipy.fftpack is quite a comprehensive toolset. Nonetheless, I am > scratching my head why I cannot find a radially averaged power > spectrum(RAPS) tool in scipy.fftpack which seems a natural complementary > tool for an FFT toolset. Do people just write the function for themselves? > Or is it somewhere else already? > > * The radially averaged power spectrum(RAPS) is s the > direction-independent mean spectrum, i.e. the average of all possible > directional power spectra. The radially averaged power spectrum provides a > convenient means to view and compare information contained in 2-D spectra > in 1-D. This function computes and plots the RAPS of an input matrix (does > not consider corner values outside averaging radius). The image can be > rectangular but must be 2-D (e.g., multi-color channel data is not > supported). The spatial resolution of the data is also specified (https://www.mathworks.com/matlabcentral/fileexchange/23636-radially-average… > ) > > *This tool was in Astropy 2.7 but I cannot find it Astropy 3.1.... > > Thanks > -- >

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Fwd: Discrete Fourier transforms (scipy.fftpack)- radially averaged power spectrum(RAPS)
by Eyal Saiet 17 Mar '19

17 Mar '19

Hello, The scipy.fftpack is quite a comprehensive toolset. Nonetheless, I am scratching my head why I cannot find a radially averaged power spectrum(RAPS) tool in scipy.fftpack which seems a natural complementary tool for an FFT toolset. Do people just write the function for themselves? Or is it somewhere else already? * The radially averaged power spectrum(RAPS) is s the direction-independent mean spectrum, i.e. the average of all possible directional power spectra. The radially averaged power spectrum provides a convenient means to view and compare information contained in 2-D spectra in 1-D. This function computes and plots the RAPS of an input matrix (does not consider corner values outside averaging radius). The image can be rectangular but must be 2-D (e.g., multi-color channel data is not supported). The spatial resolution of the data is also specified (https://www.mathworks.com/matlabcentral/fileexchange/23636-radially-average… ) *This tool was in Astropy 2.7 but I cannot find it Astropy 3.1.... Thanks -- Eyal

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Root Locus Analysis
by Fábio Molinar 09 Mar '19

09 Mar '19

Hi everyone, I am a Controls Engineer who used to use Matlab quite frequently at the University to analyze, among other things, system's stability. I am now reading again some theoretical books on control theory to refresh my mind again with some of the theories and concepts I haven't used for a while. But since I became a python programmer and enthusiast, I am doing this review while using SciPy to program some tests and examples. And I am really enjoying using SciPy to code some of my scripts. Although I just started using it, I can't stress enough how much fun and how useful the scipy.signal package is. But, during my theory review I came upon the Root Locus Analysis and, to my disappointment, I didn't find support for it on the scipy.signal package. *Is anyone aware if there is someone working on implementing Root Locus analysis into the scipy.signal package*? I know that there is another python package called "control" which has support for many control analysis tools, including Root Locus analysis, but I would like to avoid installing yet another package into my python environment. Especially because installing the "control" python package isn't that straight-forward and requires a bunch of other packages/libraries. Yours sincerely, *Fábio Thomaz Molinar*

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On numpy.pad(array, pad_width, mode, **kwargs)
by Virgil Stokes 05 Mar '19

05 Mar '19

One mode of padding is 'mean'. However, it does not return the actual mean of the array --- it returns the rounded value of the mean! example: a = np.array([1, 2, -1, 4, -3]) np.mean(a) = 0.6 np.pad(a, (2,), 'mean') = [ 1 1 1 2 -1 4 -3 1 1] The documentation that I found at: https://docs.scipy.org/doc/numpy/reference/generated/numpy.pad.html shows: ‘mean’ - Pads with the mean value of all or part of the vector along each axis. It actually Pads with the rounded mean value. IMHO it should return the "float_" value. If the SciPy team does not agree, then the documentation should be changed to show what it actually returns. --V IMHO it should När du har kontakt med oss på Uppsala universitet med e-post så innebär det att vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/ E-mailing Uppsala University means that we will process your personal data. For more information on how this is performed, please read here: http://www.uu.se/en/about-uu/data-protection-policy

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