Mailman 3 June 2020 - SciPy-User

How to display a RGB image (MFISH)?
by jp 02 Apr '23

02 Apr '23

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn 28 Jul '22

28 Jul '22

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Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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Does Lomb-Scargle reduce to FFT for uniform data?
by Milind R 30 Jun '20

30 Jun '20

Hi All, I am trying to use the Lomb-Scargle periodogram for a timeseries dataset of energy consumption with some missing values. In order to understand it better, I am trying to compare it to an FFT with linearly interpolated data. However, the values are not even in the same order of magnitude. According to [1] (see section 5), the Lomb-Scargle method reduces to a Discrete Fourier Transform when there are no missing values in the data. I have double-checked regarding normalisation which are not present in my invocations of `fft` and `lombscargle`. I have also tried running `lombscargle` with the interpolated dataset (i.e. invoking both `fft` and `lombscargle` for the exact same dataset), but still the values are at completely different scales (1e8 vs 7e3). Stripped down code with both methods running on the SAME dataset to illustrate the issue (the field "num" in tmdata dataframe is just a numeric index): import scipy.fftpack as spf import scipy.signal as sps mean_tmdata = tmdata['ActiveEnergy(kWh)'].mean() atmdata = tmdata['ActiveEnergy(kWh)'] - mean_tmdata frq = spf.fftfreq(17520, 0.5) psd_lomsca = sps.lombscargle((tmdata['num'])/2,atmdata,frq[1:]) coef_fft = spf.fft(atmdata) psd_fft = np.abs(coef_fft[1:])**2 I don't feel comfortable using this function until I get some understanding of how it relates to the FFT which I am obviously much more familiar with. Any help on this is greatly appreciated! Thanks Milind [1] Jacob T. VanderPlas, 2018, "Understanding the Lomb–Scargle Periodogram", https://doi.org/10.3847/1538-4365/aab766

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ANN: SfePy 2020.2
by Robert Cimrman 30 Jun '20

30 Jun '20

I am pleased to announce the release of SfePy 2020.2. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by finite element methods. It is distributed under the new BSD license. Home page: https://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 -------------------------- - discontinuous Galerkin method implementation and examples - new website look - memory usage improvements 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 Jan Heczko Lubos Kejzlar Vladimir Lukes Tomáš Zítka

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NumPy 1.19.0 released
by Charles R Harris 20 Jun '20

20 Jun '20

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.19.0 has been released. This NumPy release supports Python 3.6-3.8 and is marked by the removal of much technical debt: support for Python 2 has been removed, many deprecations have been expired, and documentation has been improved. The polishing of the random module continues apace with bug fixes and better usability from Cython. Perhaps the most interesting thing for users will be the availability of wheels for aarch64 and PyPY. Downstream developers should use Cython >= 0.29.16 for Python 3.8 support and OpenBLAS >= 3.7 to avoid wrong results on the Skylake architecture. The NumPy Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.19.0/>, source archives, release notes, and wheel hashes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.19.0>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 126 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alex Henrie - Alexandre de Siqueira + - Andras Deak - Andrea Sangalli + - Andreas Klöckner + - Andrei Shirobokov + - Anirudh Subramanian + - Anne Bonner - Anton Ritter-Gogerly + - Benjamin Trendelkamp-Schroer + - Bharat Raghunathan - Brandt Bucher + - Brian Wignall - Bui Duc Minh + - Changqing Li + - Charles Harris - Chris Barker - Chris Holland + - Christian Kastner + - Chunlin + - Chunlin Fang + - Damien Caliste + - Dan Allan - Daniel Hrisca - Daniel Povey + - Dustan Levenstein + - Emmanuelle Gouillart + - Eric Larson - Eric M. Bray - Eric Mariasis + - Eric Wieser - Erik Welch + - Fabio Zeiser + - Gabriel Gerlero + - Ganesh Kathiresan + - Gengxin Xie + - Guilherme Leobas - Guillaume Peillex + - Hameer Abbasi - Hao Jin + - Harshal Prakash Patankar + - Heshy Roskes + - Himanshu Garg + - Huon Wilson + - John Han + - John Kirkham - Jon Dufresne - Jon Morris + - Josh Wilson - Justus Magin - Kai Striega - Kerem Hallaç + - Kevin Sheppard - Kirill Zinovjev + - Marcin Podhajski + - Mark Harfouche - Marten van Kerkwijk - Martin Michlmayr + - Masashi Kishimoto + - Mathieu Lamarre - Matt Hancock + - MatteoRaso + - Matthew Harrigan - Matthias Bussonnier - Matti Picus - Max Balandat + - Maximilian Konrad + - Maxwell Aladago - Maxwell Bileschi + - Melissa Weber Mendonça + - Michael Felt - Michael Hirsch + - Mike Taves - Nico Schlömer - Pan Jan + - Paul Rougieux + - Pauli Virtanen - Peter Andreas Entschev - Petre-Flaviu Gostin + - Pierre de Buyl - Piotr Gaiński + - Przemyslaw Bartosik + - Raghuveer Devulapalli - Rakesh Vasudevan + - Ralf Gommers - RenaRuirui + - Robert Kern - Roman Yurchak - Ross Barnowski + - Ryan + - Ryan Soklaski - Sanjeev Kumar + - SanthoshBala18 + - Sayed Adel + - Sebastian Berg - Seth Troisi - Sha Liu + - Siba Smarak Panigrahi + - Simon Gasse + - Stephan Hoyer - Steve Dower + - Thomas A Caswell - Till Hoffmann + - Tim Hoffmann - Tina Oberoi + - Tirth Patel - Tyler Reddy - Warren Weckesser - Wojciech Rzadkowski + - Xavier Thomas + - Yilin LI + - Zac Hatfield-Dodds + - Zé Vinícius + - @Adam + - @Anthony + - @Jim + - @bartosz-grabowski + - @dojafrat + - @gamboon + - @jfbu + - @keremh + - @mayeut + - @ndunnewind + - @nglinh + - @shreepads + - @sslivkoff + Cheers, Charles Harris

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NumPy 1.18.5 released
by Charles R Harris 03 Jun '20

03 Jun '20

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.18.5. This is a short release to enable pickle protocol=5 to be used in Python3.5 and is motivated by the recent backport of pickle5 to Python3.5. The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.18.5>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.18.5>. *Contributors* A total of 3 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Charles Harris - Matti Picus - Siyuan Zhuang + *Pull requests merged* A total of 2 pull requests were merged for this release. - #16439: ENH: enable pickle protocol 5 support for python3.5 - #16441: BUG: relpath fails for different drives on windows Cheers, Charles Harris

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