Mailman 3 September 2014 - 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

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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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Normalization for optimization in python
by Yuxiang Wang 28 Apr '15

28 Apr '15

Dear all, During optimization, it is often helpful to normalize the input parameters to make them on the same order of magnitude, so the convergence can be much better. For example, if we want to minimize f(x), while a reasonable approximation is x0=[1e3, 1e-4], it might be helpful to normalize x0[0] and x0[1] to about the same order of magnitude (often O(1)). My question is, I have been using scipy.optimize and specifically, the L-BFGS-B algorithm. I was wondering that, do I need to normalize that manually by writing a function, or the algorithm already did it for me? Thank you! -Shawn -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw5aj(a)virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/

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Does SciPy already supersed NumPy?
by Sergio Rojas 01 Oct '14

01 Oct '14

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Minimizing Monte Carlo simulation function
by Jeremy Sanders 01 Oct '14

01 Oct '14

I have a function which returns a value computed using a Monte Carlo simulation. I'd like to minimize this function. It's also hard to compute the gradient for this function as the parameters are converted to integers internally (basically they are converted to array indices). I'd have thought that simulated annealing might be the best way to minimize this function. However, this is now deprecated in scipy. The replacement, basinhopping, appears to use scipy.minimize internally, so I think this relies on a function where the gradient can be computed. Is there are real replacement for annealing in this scenario? basinhopping doesn't seem to work very well when I tried it. It seems that it is wrong to deprecate simulated annealing, as it is a widely understood algorithm. Doing some tests, the most robust way of finding the minimum I have found so far is to use the MCMC emcee module. Thanks Jeremy Sanders.

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ANN: SfePy 2014.3
by Robert Cimrman 25 Sep '14

25 Sep '14

I am pleased to announce release 2014.3 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 (preliminary support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: http://groups.google.com/group/sfepy-devel Git (source) repository, issue tracker, wiki: http://github.com/sfepy Highlights of this release -------------------------- - isogeometric analysis (IGA) speed-up by C implementation of NURBS basis evaluation - generalized linear combination boundary conditions that work between different fields/variables and support non-homogeneous periodic conditions - non-constant essential boundary conditions given by a function in IGA - reorganized and improved documentation For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Best regards, Robert Cimrman and Contributors (*) (*) Contributors to this release (alphabetical order): Vladimir Lukes, Matyas Novak, Zhihua Ouyang, Jaroslav Vondrejc

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Re: [SciPy-User] Minimizing Monte Carlo simulation function
by Andrew Nelson 25 Sep '14

25 Sep '14

>On 24 September 2014 16:07, Matt Newville <newville(a)cars.uchicago.edu> wrote: >That said, the comparison above implies that the differential evolution algorithm is not better (in either success rate or number of evaluations) than basinhopping, but it certainly seems better than anneal was. I used the benchmarks in the master branch of scipy to obtain the results in the gist linked below. The results indicate that deprecating anneal may not be a bad idea. In 9 tests of 'as implemented in scipy' - the score was: differential_evolution 6 basinhopping 3 anneal 0. https://gist.github.com/andyfaff/24c96a3d5dbc7b0272b2. You have to bear in mind that the benchmark functions (contained in https://github.com/scipy/scipy/blob/master/scipy/optimize/benchmarks/test_f…) are quite hard. As such, they aren't approachable on the 'normal' minimizers. -- _____________________________________ Dr. Andrew Nelson _____________________________________

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Re: [SciPy-User] Minimizing Monte Carlo simulation function
by Andrew Nelson 24 Sep '14

24 Sep '14

Jeremy Sanders <jeremy(a)jeremysanders.net> wrote: >I have a function which returns a value computed using a Monte Carlo simulation. I'd like to minimize this function. It's also hard to compute the gradient for this function as the parameters are converted to integers internally (basically they are converted to array indices). >I'd have thought that simulated annealing might be the best way to minimize this function. However, this is now deprecated in scipy. The replacement, basinhopping, appears to use scipy.minimize internally, so I think this relies on a function where the gradient can be computed. >Is there are real replacement for annealing in this scenario? basinhopping doesn't seem to work very well when I tried it. It seems that it is wrong to deprecate simulated annealing, as it is a widely understood algorithm. >Doing some tests, the most robust way of finding the minimum I have found so far is to use the MCMC emcee module. The 0.15 release of scipy will have the 'scipy.optimize.differential_evolution' minimizer. This optimizer uses Differential Evolution (http://en.wikipedia.org/wiki/Differential_evolution), which is a stochastic method and does not use gradients at all. Internal conversion of parameters to integers will not be problem. But you do have to specify a range, (min, max), for each parameter. The minimizer stays strictly within the bounds of those ranges. >From tests on my problems (high dimensional non-linear least squares) the algorithm is streets ahead of simulated annealing. I also prefer differential evolution over scipy.optimize.curve_fit, because it is totally insensitive to the starting parameters used. Andrew. -- _____________________________________ Dr. Andrew Nelson _____________________________________

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ANN: Lmfit 0.8.0
by Matt Newville 23 Sep '14

23 Sep '14

Hi Folks, Lmfit 0.8.0 has been released, and is available from PyPI and github: https://pypi.python.org/pypi/lmfit/ http://lmfit.github.io/lmfit-py/ Lmfit provides a high level approach of least squares minimization and curve fitting based on the routines from scipy.optimize. The key idea is to use Parameter objects that can be bounded, fixed, or algebraically constrained in place of floating point variables. Many additional features to make minimization problems easier and better are included. Lmfit is MIT-licensed and a pure Python module. It requires scipy version 0.13 or later. Lmfit version 0.8.0 includes several bug fixes and improvements. The most important new feature is a Model class for high level curve-fitting problems, currently emphasizing 1-D functions. The Model class, largely the work of Daniel Allen with substantial input from Antonino Ingargiola, wraps a model function that simulates some data. It includes methods to create parameters from function arguments, to fit to data, and to evaluate models. More than 20 pre-built models for line shapes such as Gaussian and Exponential are included. An important feature of Model is that they can be added together, making it very easy to construct complex models. Automated testing with nose and Travis-CI is greatly improved. There are over 100 tests, many of these checking the numerical results for non-trivial fits. All of the NIST StRD datasets are tested, requiring that NIST certified values be found (with fairly loose precision) from at least one of the NIST-provided starting values. Daniel Allen also started an IPython GUI Fitter, providing a very nice tool for fitting simple line-shapes to 1 dimensional data. Finally, we've added a lmfit-py google group for questions about usage, discussions of design, and announcements. We're happy to have conversations about ideas for improving lmfit, or minimization and curve-fitting routines with scipy in general, on any appropriate forum. Thanks, --Matt Newville

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