Mailman 3 April 2017 - 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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Install Scipy with Anaconda's MKL libraries
by David Hagen May 18, 2017

May 18, 2017

Anaconda now comes with free MKL libraries. I am trying to install the development branch of Scipy into an Anaconda environment, which means running the full Scipy install, not just the wheel. Scipy needs BLAS/LAPACK in order to compile, so the built in MKL seems to make sense. However, it seems that Scipy can't find the MKL libraries because it gives the "no lapack/blas resources found" error during setup.py when running a pip install. Is there an environment variable I need to set in order to tell Scipy's setup where my LAPACK library lives? Here is exactly what I am running: conda create --name scipy_master python=3.6 numpy cython activate scipy_master pip install https://github.com/scipy/scipy/tarball/master I asked this on Stack Overflow <https://stackoverflow.com/questions/43559959/compiling-scipy-with-anacondas…> last week but didn't get an answer.

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EuroSciPy 2017
by Robert Pollak May 10, 2017

May 10, 2017

Dear EuroSciPy team, I have noticed that EuroSciPy 2017 in Erlangen is announced as "upcoming" on https://conference.scipy.org/ . Is there already a date fixed? Best regards, Robert

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[ANN] 10ᵀᴴ Advanced Scientific Programming in Python in Nikiti, Greece, August 28—September 2, 2017
by Tiziano Zito April 28, 2017

April 28, 2017

10ᵀᴴ Advanced Scientific Programming in Python ============================================== a Summer School by the G-Node and the Municipality of Sithonia 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 Mathematica 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. You can apply online: https://python.g-node.org Application deadline: 23:59 UTC, May 31, 2017. There will be no deadline extension, so be sure to apply on time ;-) Be sure to read the FAQ before applying. Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Date & Location =============== August 28—September 2, 2017. Nikiti, Sithonia, Halkidiki, Greece Program ======= → Best Programming Practices • Best practices for scientific programming • Version control with git and how to contribute to open source projects with GitHub • Best practices in data visualization → Software Carpentry • Test-driven development • Debugging with a debuggger • Profiling code → Scientific Tools for Python • Advanced NumPy → Advanced Python • Decorators • Context managers • Generators → The Quest for Speed • Writing parallel applications • Interfacing to C with Cython • Memory-bound problems and memory profiling • Data containers: storage and fast access to large data → Practical Software Development • Group project Preliminary Faculty =================== • Francesc Alted, freelance consultant, author of Blosc, Castelló de la Plana, Spain • Pietro Berkes, NAGRA Kudelski, Lausanne, Switzerland • Zbigniew Jędrzejewski-Szmek, Krasnow Institute, George Mason University, Fairfax, VA USA • Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne Switzerland • Juan Nunez-Iglesias, Victorian Life Sciences Computation Initiative, University of Melbourne, Australia • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Nicolas P. Rougier, Inria Bordeaux Sud-Ouest, Institute of Neurodegenerative Disease, University of Bordeaux, France • Bartosz Teleńczuk, European Institute for Theoretical Neuroscience, CNRS, Paris, France • Stéfan van der Walt, Berkeley Institute for Data Science, UC Berkeley, CA USA • Nelle Varoquaux, Berkeley Institute for Data Science, UC Berkeley, CA USA • Tiziano Zito, freelance consultant, Berlin, Germany Organizers ========== For the German Neuroinformatics Node of the INCF (G-Node) Germany: • Tiziano Zito, freelance consultant, Berlin, Germany • Zbigniew Jędrzejewski-Szmek, Krasnow Institute, George Mason University, Fairfax, USA • Jakob Jordan, Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich GmbH, Germany • Etienne Roesch, Centre for Integrative Neuroscience and Neurodynamics, University of Reading, UK Website: https://python.g-node.org Contact: python-info(a)g-node.org

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SciPy special function pro_rad1( )
by Nitish Ragoomundun April 28, 2017

April 28, 2017

Hello all, I need to compute values for the prolate spheroidal function and compare values between different orders. From the reference guide, the function is described as *scipy.special.pro_rad1(m, n, c, x) = <ufunc 'pro_rad1'>* *Prolate spheroidal radial function of the first kind and its derivative* *Computes the prolate spheroidal radial function of the first kind and its derivative (with respect to x) for mode parameters m>=0 and n>=m, spheroidal parameter c and* *|x| < 1.0.* This tells me that x is the independent variable and that it is between -1.0 and +1.0. But, this is not the case. When I put x between 0.0 and 1.0 for example, the output is NaN. Also, I would like to know what are m and n. It is not clearly defined. I need to know which one is the order. Thanks.

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Convert data to image and then image to numpy.ndarray
by Carlton Banks April 27, 2017

April 27, 2017

Is it possible to convert a data set stored as a numpy.ndarray -> plot the data with a colormap , possibly cm.jet. -> and then store the image pixels to a numpy.ndarray. The data array of the image should have the same shape of the data array used to make the array. The image conversion is only used for getting a visual representation of the data, and normalise it using the cm.jet. So I actually just interested in the RGB values of the colormap normalised input data. IS this possible?

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why scipy.special.mathieu_cem is not periodic?
by Ming Lu April 26, 2017

April 26, 2017

Dear all: I have a question regarding the special function scipy.special.mathieu_cem, why this function not periodic? I thought this function should have period pi when m is even, and 2*pi when m is odd, however this is not the case: x = np.linspace(0,10*np.pi,100) plt.plot(x,sp.mathieu_cem(3,3,x)[0]) plt.show() compare with mathematica, it clearly shows a period graph. To see the graph, I have the detailed post on stackoverflow. http://stackoverflow.com/questions/43615531/why-mathieu-function-in-scipy-n… Best regards, Ming

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Re: [SciPy-User] Multivariate linear (bilinear) fit (Charles R Harris)
by Justus Schwabedal April 20, 2017

April 20, 2017

Indeed, in statistics it's a linear model. Try r2py. R's really good in linear models and will give you additional information on significance tests. Best, Jus On Apr 20, 2017 02:14, <scipy-user-request(a)python.org> wrote: Send SciPy-User mailing list submissions to scipy-user(a)python.org To subscribe or unsubscribe via the World Wide Web, visit https://mail.python.org/mailman/listinfo/scipy-user or, via email, send a message with subject or body 'help' to scipy-user-request(a)python.org You can reach the person managing the list at scipy-user-owner(a)python.org When replying, please edit your Subject line so it is more specific than "Re: Contents of SciPy-User digest..." Today's Topics: 1. Re: Multivariate linear (bilinear) fit (Charles R Harris) ---------------------------------------------------------------------- Message: 1 Date: Wed, 19 Apr 2017 18:12:49 -0600 From: Charles R Harris <charlesr.harris(a)gmail.com> To: SciPy Users List <scipy-user(a)python.org> Subject: Re: [SciPy-User] Multivariate linear (bilinear) fit Message-ID: <CAB6mnxKo15OS45qnj6TvXxoea2fQcre3og3vxCjDFNDEH=Lakg(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" On Wed, Apr 19, 2017 at 4:38 PM, Joe Kington <joferkington(a)gmail.com> wrote: > They're relatively recent additions, but numpy.polynomial.polyvander2d and > numpy.polynomial.polyval2d should also do what you want, unless I'm > misunderstanding the problem. > > https://docs.scipy.org/doc/numpy/reference/generated/ > numpy.polynomial.polynomial.polyvander2d.html#numpy.polynomial.polynomial. > polyvander2d > https://docs.scipy.org/doc/numpy/reference/generated/ > numpy.polynomial.polynomial.polyval2d.html#numpy.polynomial.polynomial. > polyval2d > > You can also do things like (you could generalize this to N-dimensions, as > well): > > def polyfit2d(x, y, z, order=3): > ncols = (order + 1)**2 > G = np.zeros((x.size, ncols)) > ij = itertools.product(range(order+1), range(order+1)) > for k, (i,j) in enumerate(ij): > G[:,k] = x**i * y**j > m, _, _, _ = np.linalg.lstsq(G, z) > return m > def polyval2d(x, y, m): > order = int(np.sqrt(len(m))) - 1 > ij = itertools.product(range(order+1), range(order+1)) > z = np.zeros_like(x) > for a, (i,j) in zip(m, ij): > z += a * x**i * y**j > return z > > > I think the "bilinear" is a mistake, as bilinear usually means terms of degree two. AFAICT, this question is just about multivariate linear fits only <snip> Chuck > > >

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