Mailman 3 March 2016 - SciPy-User

How to display a RGB image (MFISH)?
by jp 03 Apr '23

03 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

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 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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inverse filter design, remez?
by R Schumacher 31 Mar '16

31 Mar '16

We have sets of ADC data collected with different front-end filters at acquisition; given an empirically determined roll off in dB/octave or dB/Hz, I'd like to perform equalization (in this case the low frequencies) and I know from sweeps that roll off begins under ~200Hz and -3dB is ~15Hz. One general example is Bank's algorithm: <http://nullege.com/codes/show/src@p@o@porc-HEAD@porc.py>http://nullege.com/codes/show/src@p@o@porc-HEAD@porc.py where people correct sound for their room acoustics. There is also the Nelson-Kirkeby inverse filter methodology. Our specific case is flattening the response due to a single-pole RC high-pass filter so I think remez() might be just fine; can anyone here supply links or examples in scipy to this application of remez()? I'm guessing scipy.signal.remez( 2, range(.625, 512, .625), desired= <inverse of measured gain, range(.625, 256, .625)>, weight=<unsure of this...>, Hz=1024, type=<unsure, what to use for high-pass?>, maxiter=25, grid_density=16 ) - Ray

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Sampling from an arbitrary distribution.
by Andrew Nelson 30 Mar '16

30 Mar '16

Hi all, I wish to sample from an arbitrary probability density distribution. At the moment I don't have a functional form for the density distribution (so I'm not sure if I can subclass scipy.stats.rv_continuous) What I do have is an array containing (finely) histogrammed samples of the distribution. I found some pre-existing code ( http://www.nehalemlabs.net/prototype/blog/2013/12/16/how-to-do-inverse-tran…) which I successfully modified to deal with pre-histogrammed data. Q1: Can I still subclass scipy.stats.rv_continuous to sample from my arbitrary distribution? e.g. use the histogram to construct the object and refer to this histogram when overriding _pdf. Q2: If not, then is it worth adding functionality for this kind of sampling? Where would it belong scipy.stats or numpy.random? A.

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Numpy 1.11.0 released
by Charles R Harris 27 Mar '16

27 Mar '16

Hi All, I'm pleased to announce the release of Numpy 1.11.0. This release is the result of six months of work comprising 371 merged pull requests submitted by 112 authors containing many bug fixes and improvements. Highlights are: - The datetime64 type is now timezone naive. - A dtype parameter has been added to ``randint``. - Improved detection of two arrays possibly sharing memory. - Automatic bin size estimation for ``np.histogram``. - Speed optimization of A @ A.T and dot(A, A.T). - New function ``np.moveaxis`` for reordering array axes. Source files and documentation can be found on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0/>, while source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be installed from Pypi. Note that this is the last release to support Python 2.6, 3.2, and 3.3. Contributors are listed below in alphabetical order with the names of new contributors starred. Abdullah Alrasheed* Aditya Panchal* Alain* Alex Griffing Alex Rogozhnikov* Alex Willmer Alexander Heger* Allan Haldane Anatoly Techtonik* Andrew Nelson Anne Archibald Antoine Pitrou Antony Lee* Behzad Nouri Bertrand Lefebvre Blake Griffith Boxiang Sun* Brigitta Sipocz* Carl Kleffner Charles Harris Chris Hogan* Christoph Gohlke Colin Jermain* Cong Ma* Daniel David Freese David Sanders* Dmitry Odzerikho* Dmitry Zagorny* Eric Larson* Eric Moore Ethan Kruse* Eugene Krokhalev* Evgeni Burovski* Francis T. O'Donovan* François Boulogne* Gabi Davar* Gerrit Holl Gopal Singh Meena* Greg Yang* Greg Young* Gregory R. Lee Griffin Hosseinzadeh* Hassan Kibirige* Holger Kohr* Ian Henriksen Iceman9* Jaime Fernandez James Camel* Jason King* John Bjorn Nelson* John Kirkham Jonathan Helmus Jonathan Underwood* Joseph Fox-Rabinovitz* Julian Taylor Julien Dubois* Julien Lhermitte* Julien Schueller* Jörn Hees* Konstantinos Psychas* Lars Buitinck Luke Zoltan Kelley* MaPePeR* Mad Physicist* Mark Wiebe Marten van Kerkwijk Matthew Brett Matthias Geier* Maximilian Trescher* Michael K. Tran* Michael Behrisch* Michael Currie* Michael Löffler* Nathaniel Hellabyte* Nathaniel J. Smith Nick Papior* Nicolas Calle* Nicolás Della Penna* Olivier Grisel Pauli Virtanen Peter Iannucci Phaiax* Ralf Gommers Rehas Sachdeva* Ronan Lamy Ruediger Meier* Ryan Grout* Ryosuke Okuta* Rémy Léone* Sam Radhakrishnan* Samuel St-Jean* Sebastian Berg Simon Conseil* Stephan Hoyer Stuart Archibald* Stuart Berg Sumith* Tapasweni Pathak* Thomas Robitaille Tobias Megies* Tushar Gautam* Varun Nayyar* Vincent Legoll* Warren Weckesser Wendell Smith Yash Mehrotra* Yifan Li* endolith floatingpointstack* ldoddema* yolanda15* Thanks to all who worked on this release. Chuck

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Scientific citation numpy.random.multinomial
by Johannes Radinger 21 Mar '16

21 Mar '16

Hi all, I am using the function numpy.random.multinomial in my code and want to explicitly cite this function/method in a scientific (peer reviewed) article. What is the appropriate way to cite numpy.random.multinomial? I am aware that SciPy can be cited as: Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific Tools for Python, 2001-, http://www.scipy.org/ [Online; accessed 2016-03-21]. However, this is not specifically for the "random" library of numpy. Is there any other reference one can use? Best regards, Johannes

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Scipy2016: call for proposals
by Nelle Varoquaux 21 Mar '16

21 Mar '16

Dear all, SciPy 2016, the Fifteenth Annual Conference on Python in Science, takes place in Austin, TX on July, 11th to 17th. The conference features two days of tutorials by followed by three days of presentations, and concludes with two days of developer sprints on projects of interest to attendees. . The topics presented at SciPy are very diverse, with a focus on advanced software engineering and original uses of Python and its scientific libraries, either in theoretical or experimental research, from both academia and the industry. This year we are happy to announce two specialized tracks that run in parallel to the general conference (Data Science , High Performance Computing) and 8 mini-symposia (Earth and Space Science, Biology and Medicine, Engineering, Social Sciences, Special Purpose Databases, Case Studies in Industry, Education, Reproducibility) Submissions for talks and posters are welcome on our website ( http://scipy2016.scipy.org). In your abstract, please provide details on what Python tools are being employed, and how. The talk and poster submission deadline is March 25th, 2016, while the tutorial submission deadline is March, 21st, 2016. Important dates: Mar 21: Tutorial Proposals Due Mar 25: Talk and Poster Proposals Due May 11: Plotting Contest Submissions Due Apr 22: Tutorials Announced Apr 22: Financial Aid Submissions Due May 4: Talk and Posters Announced May 11: Financial Aid Recipients Notified May 22: Early Bird Registration Deadline Jul 11-12: SciPy 2016 Tutorials Jul 13-15: SciPy 2016 General Conference Jul 16-17: SciPy 2016 Sprints We look forward to an exciting conference and hope to see you in Austin in July! The Scipy 2016 http://scipy2016.scipy.org/ Conference Chairs: Aric Hagberg, Prabhu Ramachandran Tutorial Chairs: Justin Vincent, Ben Root Program Chair: Serge Rey, Nelle Varoquaux Proceeding Chairs: Sebastian Benthall

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minimize reporting successful but not searching (with default method)
by David Mikolas 21 Mar '16

21 Mar '16

I have a situation where scipy.optimize.minimize returns the initial starting value as the answer without really iterating, and returns "successful" status, using default method. It works fine if I specify Nelder-Mead, but my question is "why does minimize return successful when it isn's, and what can I do (if anything) to flag this behavior? The question is also here http://stackoverflow.com/q/36110998 and if someone can also leave an answer there as well, that would be great! but not necessary. One particular - I'm using calls to Skyfield, which may or may not have something to do with it. (http://rhodesmill.org/skyfield/) FULL OUTPUT using DEFAULT METHOD: status: 0 success: True njev: 1 nfev: 3 hess_inv: array([[1]]) fun: 1694.98753895812 x: array([ 10000.]) message: 'Optimization terminated successfully.' jac: array([ 0.]) nit: 0 FULL OUTPUT using Nelder-Mead METHOD: status: 0 nfev: 63 success: True fun: 3.2179306044608054 x: array([ 13053.81011963]) message: 'Optimization terminated successfully.' nit: 28

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(no subject)
by David Mikolas 21 Mar '16

21 Mar '16

I have a situation where scipy.optimize.minimize returns the initial starting value as the answer without really iterating, and returns "successful" status, using default method. It works fine if I specify Nelder-Mead, but my question is "why does minimize return successful when it isn's, and what can I do (if anything) to flag this behavior? The question is also here http://stackoverflow.com/q/36110998 and if someone can also leave an answer there as well, that would be great! but not necessary. One particular - I'm using calls to Skyfield, which may or may not have something to do with it. (http://rhodesmill.org/skyfield/) FULL OUTPUT using DEFAULT METHOD: status: 0 success: True njev: 1 nfev: 3 hess_inv: array([[1]]) fun: 1694.98753895812 x: array([ 10000.]) message: 'Optimization terminated successfully.' jac: array([ 0.]) nit: 0 FULL OUTPUT using Nelder-Mead METHOD: status: 0 nfev: 63 success: True fun: 3.2179306044608054 x: array([ 13053.81011963]) message: 'Optimization terminated successfully.' nit: 28

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