Mailman 3 February 2022 - SciPy-User

How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

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 July 28, 2022

July 28, 2022

LinkedIn ------------ Jelle Feringa requested to add you as a connection on LinkedIn: ------------------------------------------ Jose, I'd like to add you to my professional network on LinkedIn. - Jelle Accept invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… View invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… ------------------------------------------ DID YOU KNOW your LinkedIn profile helps you control your public image when people search for you? Setting your profile as public means your LinkedIn profile will come up when people enter your name in leading search engines. Take control of your image! http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/ewp/inv-22/ -- (c) 2011, LinkedIn Corporation

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

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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Results from the Kolmogorov-Smirnov test
by emanuele.leonardi＠roma1.infn.it Feb. 25, 2022

Feb. 25, 2022

I am having problems in interpreting the results from the Kolmogorov Smrinov test function ks_2samp. The results I get from this function are not in agreement with the other implementations of this test I am using (ROOT, wikipedia formulas, several on-line calculators). As an example, I am using the following data samples: s1 = [7,4,2,4] s2 = [3,5,7,9,28,35,40,22,33,55] From ks_2samp I get: print(ks_2samp(s1,s2)) Ks_2sampResult(statistic=0.69999999999999996, pvalue=0.063697238353882635) While the KolmogorovSmirnov2SamplesTest from ROOT returns: Int_t n1 = 4; Double_t sample1[] = {7.,4.,2.,4.}; Int_t n2 = 10; Double_t sample2[] = {3.,5.,7.,9.,28.,35.,40.,22.,33.,55.}; ROOT::Math::GoFTest* goftest_2 = new ROOT::Math::GoFTest(n1, sample1, n2, sample2); Double_t Dn_1 = goftest_2->KolmogorovSmirnov2SamplesTest("t"); Double_t pvalueKS_1 = goftest_2-> KolmogorovSmirnov2SamplesTest(); printf("Dn = %6.4f p = %6.4f\n",Dn_1,pvalueKS_1); Dn = 0.7000 p = 0.1216 I obtained the same result by using the algorithm described in the Kolmogorov Smirnov test page on Wikipedia (https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test). Also the same result is obtained from several on-line calculators for this test, e.g. https://www.aatbio.com/tools/kolmogorov-smirnov-k-s-test-calculator https://scistatcalc.blogspot.com/2013/11/kolmogorov-smirnov-test-calculator… I noticed that the discrepancy tends to become smaller if the data samples sizes grow. Am I doing something wrong or there is an explanation for this discrepnacy? For completeness, I am using python3-3.6.8-18 and python36-scipy-0.18.1-2. Thank you Emanuele Leonardi

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14ᵗʰ Advanced Scientific Programming in Python in Bilbao Spain, 5–11 September, 2022
by Tiziano Zito Feb. 21, 2022

Feb. 21, 2022

ASPP2022: 14ᵗʰ Advanced Scientific Programming in Python ======================================================== a Summer School by the ASPP faculty and the Faculty of Engineering of the Mondragon University, Bilbao https://aspp.school Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. Date & Location =============== 5–11 September, 2022. Bilbao, Spain. Application =========== You can apply online: https://aspp.school Application deadline: 23:59 UTC, Sunday 1 May, 2022. There will be no deadline extension, so be sure to apply on time. Be sure to read the FAQ before applying: https://aspp.school/wiki/faq Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. We are in the process of securing some funds for supporting students with accommodation and living costs. Program ======= • Version control with git and how to contribute to open source projects with GitHub • Best practices in data visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Advanced scientific Python: context managers and generators • Writing parallel applications in Python • Profiling and speeding up scientific code with Cython and numba • Programming in teams Faculty ======= • Jakob Jordan, Department of Physiology, University of Bern Switzerland • Jenni Rinker, Department of Wind Energy, Technical University of Denmark, Roskilde Denmark • Lisa Schwetlick, Experimental and Biological Psychology, Universität Potsdam Germany • Nicolas Rougier, Inria Bordeaux Sud-Ouest, Institute of Neurodegenerative Diseases, University of Bordeaux France • Pamela Hathway, GfK, Nuremberg Germany • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers ========== Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin Germany Organization team in Bilbao: • Aitor Morales-Gregorio, Theoretical Neuroanatomy, Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich, Germany • Carlos Cernuda, Data Analysis & Cybersecurity, Faculty of Engineering, Mondragon Unibertsitatea, Bilbao Spain Website: https://aspp.school Contact: info(a)aspp.school

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NumPy 1.22.2 released
by Charles R Harris Feb. 4, 2022

Feb. 4, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.2. NumPy 1.22.2 fixes several bugs discovered after the 1.22.1 release. Notable fixes are: - Build related fixes for downstream projects and other platforms. - Various annotation fixes/additions. - Fixes for the CVE-2021-41495 complaint. - Toolchain fixes on Windows. Numpy wheels now use the 1.41 tool chain, fixing downstream link problems for projects using NumPy provided libraries on Windows. Note that OSX wheels are provided for both the x86_64 and arm64 architectures, but there are no universal2 wheels. The minimum supported OS X version is now 10.14 for the former and 11.0 for the latter. The Python versions supported in this release are 3.8-3.10. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.22.2>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.2>. Linux users will need pip >= 0.19.3 in order to install the manylinux2014 wheels. *Contributors* A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Andrew J. Hesford + - Bas van Beek - Brénainn Woodsend + - Charles Harris - Hood Chatham - Janus Heide + - Leo Singer - Matti Picus - Mukulika Pahari - Niyas Sait - Pearu Peterson - Ralf Gommers - Sebastian Berg - Serge Guelton *Pull requests merged* A total of 21 pull requests were merged for this release. - #20842: BLD: Add NPY_DISABLE_SVML env var to opt out of SVML - #20843: BUG: Fix build of third party extensions with Py_LIMITED_API - #20844: TYP: Fix pyright being unable to infer the ``real`` and ``imag``... - #20845: BUG: Fix comparator function signatures - #20906: BUG: Avoid importing ``numpy.distutils`` on import numpy.testing - #20907: MAINT: remove outdated mingw32 fseek support - #20908: TYP: Relax the return type of ``np.vectorize`` - #20909: BUG: fix f2py's define for threading when building with Mingw - #20910: BUG: distutils: fix building mixed C/Fortran extensions - #20912: DOC,TST: Fix Pandas code example as per new release - #20935: TYP, MAINT: Add annotations for ``flatiter.__setitem__`` - #20936: MAINT, TYP: Added missing where typehints in ``fromnumeric.pyi`` - #20937: BUG: Fix build_ext interaction with non numpy extensions - #20938: BUG: Fix missing intrinsics for windows/arm64 target - #20945: REL: Prepare for the NumPy 1.22.2 release. - #20982: MAINT: f2py: don't generate code that triggers ``-Wsometimes-uninitialized``. - #20983: BUG: Fix incorrect return type in reduce without initial value - #20984: ENH: review return values for PyArray_DescrNew - #20985: MAINT: be more tolerant of setuptools >= 60 - #20986: BUG: Fix misplaced return. - #20992: MAINT: Further small return value validation fixes Cheers, Charles Harris

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