Mailman 3 March 2013 - 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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Allan Variance
by Daniele Nicolodi Feb. 4, 2014

Feb. 4, 2014

Hello, does anyone know or have written and is willing to share some code to compute the Allan variance, modified Allan variance and friends, ideally both from frequency than from phase data? I'm writing my own code, it is not that difficult, but I would prefer the time required for testing and validation doing something more fun. If there is no available code implementing Allan variance, would it be interesting to include in in SciPy? Thank you so much. Best, Daniele

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Sparse Matricies and NNLS
by Calvin Morrison June 14, 2013

June 14, 2013

Hi! I have a very large matrix that I am using with the scipy.optimize.nnls function, however the matrix is so large that it takes 30Gb of memory to load with python! I was thinking about using a sparse matrix since it is relatively sparse, but the problem is that even if I was to use the sparse matricies, the nnls function only accepts a ndarray, not a sparse matrix. (when I try and throw a sparse matrix at it I get an error.) So of course I'd need to convert it to a dense array before passing it into nnls, but that would totally void the whole point of a sparse array, because then python would still have to allocate the full dense matrix. Does anyone have an idea about how to efficiently pass a matrix and store it without blowing up the memory usage? Thank you, Calvin

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Linear interpolation in 3D
by Hardock, Andreas June 13, 2013

June 13, 2013

Hi, I'm trying to interpolate a 3D data (from the pic attached) with the interp2d command. What I have, are three vectors f, z, A (x, y, z respectively, A is the percentage data given on the isolines). I first put the f and z in a meshgrid and afterwards in the griddata to get a 3D-grid then started the interpolateion. I plotted the the data after gridding, and I observed that almost all nodes are ignored. Do you have any idea how to prepare data to the interp2d command? Don't hesitate to suggest any other solution. my code so far is: import numpy as np from mpl_toolkits.mplot3d import axes3d from scipy.interpolate import interp2d import matplotlib.pyplot as plt from matplotlib import mlab plt.clf() fig = plt.figure(1) ax = axes3d.Axes3D(fig) #read data (ff,ZZ,A,a) = np.loadtxt("accuracy-map.txt", unpack=True) f=np.log10(ff) z=np.log10(ZZ) ##grid everything fgrid, zgrid=np.meshgrid(f,z) #define grid ef=np.linspace(min(f), max(f), len(f)) ez=np.linspace(min(z), max(z), len(f)) Agrid=mlab.griddata(f,z,A, ef,ez) int2d=interp2d(fgrid, zgrid, Agrid, kind='linear') ax.plot(f, z, A, 'ok', markerfacecolor='w') ax.plot_surface(fgrid, zgrid, Agrid) ax.set_xlim3d((min(f), max(f))) ax.set_ylim3d(min(z), max(z)) ax.set_zlim3d(0,100) plt.show()

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efficiency of the simplex routine: R (optim) vs scipy.optimize.fmin
by servant mathieu May 10, 2013

May 10, 2013

Dear scipy users, Again a question about optimization. I've just compared the efficiency of the simplex routine in R (optim) vs scipy (fmin), when minimizing a chi-square. fmin is faster than optim, but appears to be less efficient. In R, the value of the function is always minimized step by step (there are of course some exceptions) while there is lot of fluctuations in python. Given that the underlying simplex algorithm is supposed to be the same, which mechanism is responsible for this difference? Is it possible to constrain fmin so it could be more rigorous? Cheers, Mathieu

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scipy sprint @ EuroSciPy '12?
by Ralf Gommers May 1, 2013

May 1, 2013

Hi all, Would people be interested in having a scipy sprint at EuroSciPy this year? Last year we tried to do a last minute mini-sprint and ended up hunting for wifi access for half the time, so it would be good to organize things better this time around. I'm thinking a one-day sprint on Wed Aug 22nd would be good. If there's interest, I'm happy to organize (contact the conference organizers for a room, create a wiki page, etc.). Ralf

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ANN: SciPy 0.12.0 release candidate 1
by Ralf Gommers April 6, 2013

April 6, 2013

Hi, I am pleased to announce the availability of the first release candidate of SciPy 0.12.0. This is shaping up to be another solid release, with some cool new features (see highlights below) and a large amount of bug fixes and maintenance work under the hood. The number of contributors also keeps rising steadily, we're at 74 so far for this release. Sources and binaries can be found at http://sourceforge.net/projects/scipy/files/scipy/0.12.0rc1/, release notes are copied below. Please try this release and report any problems on the mailing list. If no issues are found, the final release will be in one week. Cheers, Ralf ========================== SciPy 0.12.0 Release Notes ========================== .. note:: Scipy 0.12.0 is not released yet! .. contents:: SciPy 0.12.0 is the culmination of 7 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.12.x branch, and on adding new features on the master branch. Some of the highlights of this release are: - Completed QHull wrappers in scipy.spatial. - cKDTree now a drop-in replacement for KDTree. - A new global optimizer, basinhopping. - Support for Python 2 and Python 3 from the same code base (no more 2to3). This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or greater. Support for Python 2.4 and 2.5 has been dropped as of this release. New features ============ ``scipy.spatial`` improvements ------------------------------ cKDTree feature-complete ^^^^^^^^^^^^^^^^^^^^^^^^ Cython version of KDTree, cKDTree, is now feature-complete. Most operations (construction, query, query_ball_point, query_pairs, count_neighbors and sparse_distance_matrix) are between 200 and 1000 times faster in cKDTree than in KDTree. With very minor caveats, cKDTree has exactly the same interface as KDTree, and can be used as a drop-in replacement. Voronoi diagrams and convex hulls ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ `scipy.spatial` now contains functionality for computing Voronoi diagrams and convex hulls using the Qhull library. (Delaunay triangulation was available since Scipy 0.9.0.) Delaunay improvements ^^^^^^^^^^^^^^^^^^^^^ It's now possible to pass in custom Qhull options in Delaunay triangulation. Coplanar points are now also recorded, if present. Incremental construction of Delaunay triangulations is now also possible. Spectral estimators (``scipy.signal``) -------------------------------------- The functions ``scipy.signal.periodogram`` and ``scipy.signal.welch`` were added, providing DFT-based spectral estimators. ``scipy.optimize`` improvements ------------------------------- Callback functions in L-BFGS-B and TNC ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A callback mechanism was added to L-BFGS-B and TNC minimization solvers. Basin hopping global optimization (``scipy.optimize.basinhopping``) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A new global optimization algorithm. Basinhopping is designed to efficiently find the global minimum of a smooth function. ``scipy.special`` improvements ------------------------------ Revised complex error functions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The computation of special functions related to the error function now uses a new `Faddeeva library from MIT <http://ab-initio.mit.edu/Faddeeva>`__ which increases their numerical precision. The scaled and imaginary error functions ``erfcx`` and ``erfi`` were also added, and the Dawson integral ``dawsn`` can now be evaluated for a complex argument. Faster orthogonal polynomials ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Evaluation of orthogonal polynomials (the ``eval_*`` routines) in now faster in ``scipy.special``, and their ``out=`` argument functions properly. ``scipy.sparse.linalg`` features -------------------------------- - In ``scipy.sparse.linalg.spsolve``, the ``b`` argument can now be either a vector or a matrix. - ``scipy.sparse.linalg.inv`` was added. This uses ``spsolve`` to compute a sparse matrix inverse. - ``scipy.sparse.linalg.expm`` was added. This computes the exponential of a sparse matrix using a similar algorithm to the existing dense array implementation in ``scipy.linalg.expm``. Listing Matlab(R) file contents in ``scipy.io`` ----------------------------------------------- A new function ``whosmat`` is available in ``scipy.io`` for inspecting contents of MAT files without reading them to memory. Documented BLAS and LAPACK low-level interfaces (``scipy.linalg``) ------------------------------------------------------------------ The modules `scipy.linalg.blas` and `scipy.linalg.lapack` can be used to access low-level BLAS and LAPACK functions. Polynomial interpolation improvements (``scipy.interpolate``) ------------------------------------------------------------- The barycentric, Krogh, piecewise and pchip polynomial interpolators in ``scipy.interpolate`` accept now an ``axis`` argument. Deprecated features =================== `scipy.lib.lapack` ------------------ The module `scipy.lib.lapack` is deprecated. You can use `scipy.linalg.lapack` instead. The module `scipy.lib.blas` was deprecated earlier in Scipy 0.10.0. `fblas` and `cblas` ------------------- Accessing the modules `scipy.linalg.fblas`, `cblas`, `flapack`, `clapack` is deprecated. Instead, use the modules `scipy.linalg.lapack` and `scipy.linalg.blas`. Backwards incompatible changes ============================== Removal of ``scipy.io.save_as_module`` -------------------------------------- The function ``scipy.io.save_as_module`` was deprecated in Scipy 0.11.0, and is now removed. Its private support modules ``scipy.io.dumbdbm_patched`` and ``scipy.io.dumb_shelve`` are also removed. Other changes ============= Authors ======= * Anton Akhmerov + * Alexander Eberspächer + * Anne Archibald * Jisk Attema + * K.-Michael Aye + * bemasc + * Sebastian Berg + * François Boulogne + * Matthew Brett * Lars Buitinck * Steven Byrnes + * Tim Cera + * Christian + * Keith Clawson + * David Cournapeau * Nathan Crock + * endolith * Bradley M. Froehle + * Matthew R Goodman * Christoph Gohlke * Ralf Gommers * Robert David Grant + * Yaroslav Halchenko * Charles Harris * Jonathan Helmus * Andreas Hilboll * Hugo + * Oleksandr Huziy * Jeroen Demeyer + * Johannes Schönberger + * Steven G. Johnson + * Chris Jordan-Squire * Jonathan Taylor + * Niklas Kroeger + * Jerome Kieffer + * kingson + * Josh Lawrence * Denis Laxalde * Alex Leach + * Lorenzo Luengo + * Stephen McQuay + * MinRK * Sturla Molden + * Eric Moore + * mszep + * Matt Newville + * Vlad Niculae * Travis Oliphant * David Parker + * Fabian Pedregosa * Josef Perktold * Zach Ploskey + * Alex Reinhart + * Richard Lindsley + * Gilles Rochefort + * Ciro Duran Santillli + * Jan Schlueter + * Jonathan Scholz + * Anthony Scopatz * Skipper Seabold * Fabrice Silva + * Scott Sinclair * Jacob Stevenson + * Sturla Molden + * Julian Taylor + * thorstenkranz + * John Travers + * True Price + * Nicky van Foreest * Jacob Vanderplas * Patrick Varilly * Daniel Velkov + * Pauli Virtanen * Stefan van der Walt * Warren Weckesser A total of 74 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

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assignment optimization problem
by Neal Becker April 2, 2013

April 2, 2013

Are there python tools for addressing problems like assignment? At this point, I don't fully understand my problem, but I believe it is a mixture of discrete assignment together with some continuous variables. My son suggests coding it by hand using some kind of simple hill climbing, but maybe I could leverage existing code for this?

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