Mailman 3 February 2016 - 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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Scipy2016: call for proposals
by Nelle Varoquaux March 21, 2016

March 21, 2016

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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Is this weird answer a bug in optimize.newton?
by Thomas Baruchel Feb. 25, 2016

Feb. 25, 2016

Hi, with my students, this afternoon, I encountered the following annoying behaviour of Scipy: >>> from scipy import optimize >>> optimize.newton(lambda x: x**3-x+1, 0) 0.999999989980082 This is very weird, because the equation is rather simple with simple coefficients, and the initial point is simple also. Any teacher or student could fall into this case when trying to use optimize.newton. Of course, the answer is absolutely wrong. First, some facts: * using the function with the derivative gives the correct answer (we are not in some tricky case); * my students wrote pieces of code with Newton-Raphson algorithm returning the expected answer even without the derivative; they guessed the value of the derivative either by some (f(x+eps)-f(x-eps))/(2*eps) or (f(x+eps)-f(x))/eps, etc. All these methods worked well. I had a glance in the source code and found that: * the function is actually thinking the answer is correct (the answer has nothing to do with iterating too many times) because two consecutive values of x are close "enough". * the answer is returned after only 3 iterations (correct answer requires about 20 iterations). I think it has to do with the way the derivative is computed by Scipy (in order to call the f function only once in each iteration, the secant is computed by using f(x) for consecutive values of x (rather than by using some epsilon); furthermore, in order to start this method the function has to provide an arbitrary "second" value of x). Is it a bug? Regards. -- Thomas Baruchel

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ANN: Stop plotting your data -- HoloViews 1.4 released!
by James A. Bednar Feb. 25, 2016

Feb. 25, 2016

We are pleased to announce the fifth public release of HoloViews, a Python package for exploring and visualizing numerical data: http://holoviews.org HoloViews provides composable, sliceable, declarative data structures for building even complex visualizations easily. Instead of you having to explicitly and laboriously plot your data, HoloViews lets you simply annotate your data so that any part of it visualizes itself automatically. You can now work with large datasets as easily as you work with simple datatypes at the Python prompt. The new version can be installed using conda: conda install -c ioam holoviews Release 1.4 introduces major new features, incorporating over 1700 new commits and closing 142 issues: - Now supports both Bokeh (bokeh.pydata.org) and matplotlib backends, with Bokeh providing extensive interactive features such as panning and zooming linked axes, and customizable callbacks - DynamicMap: Allows exploring live streams from ongoing data collection or simulation, or parameter spaces too large to fit into your computer's or your browser's memory, from within a Jupyter notebook - Columnar data support: Underlying data storage can now be in Pandas dataframes, NumPy arrays, or Python dictionaries, allowing you to define HoloViews objects without copying or reformatting your data - New Element types: Area (area under or between curves), Spikes (sequence of lines, e.g. spectra, neural spikes, or rug plots), BoxWhisker (summary of a distribution), QuadMesh (nonuniform rasters), Trisurface (Delaunay-triangulated surface plots) - New Container type: GridMatrix (grid of heterogenous Elements) - Improved layout handling, with better support for varying aspect ratios and plot sizes - Improved help system, including recursively listing and searching the help for all the components of a composite object - Improved Jupyter/IPython notebook support, including improved export using nbconvert, and standalone HTML output that supports dynamic widgets even without a Python server - Significant performance improvements for large or highly nested data And of course we have fixed a number of bugs found by our very dedicated users; please keep filing Github issues if you find any! For the full list of changes, see: https://github.com/ioam/holoviews/releases HoloViews is now supported by Continuum Analytics, and is being used in a wide range of scientific and industrial projects. HoloViews remains freely available under a BSD license, is Python 2 and 3 compatible, and has minimal external dependencies, making it easy to integrate into your workflow. Try out the extensive tutorials at holoviews.org today! Jean-Luc R. Stevens Philipp Rudiger James A. Bednar Continuum Analytics, Inc., Austin, TX, USA School of Informatics, The University of Edinburgh, UK -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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ANN: SfePy 2016.1
by Robert Cimrman Feb. 24, 2016

Feb. 24, 2016

I am pleased to announce release 2016.1 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 -------------------------- - major simplification of finite element field code - automatic checking of shapes of term arguments - improved mesh parametrization code and documentation - support for fieldsplit preconditioners of PETSc For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Best regards, Robert Cimrman on behalf of the SfePy development team --- Contributors to this release in alphabetical order: Robert Cimrman Vladimir Lukes

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Numpy 1.11.0rc1 released.
by Charles R Harris Feb. 23, 2016

Feb. 23, 2016

Hi All, I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the issues discovered in 1.11.0b3 have been dealt with and this release can go on to become the official release. Source files and documentation can be found on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc1/>, while source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be installed from Pypi. Please test thoroughly. Chuck

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3D scatter data interpolate
by Vasco Gervasi Feb. 22, 2016

Feb. 22, 2016

Hi all, I would like to interpolate 3D scatter data. I have some 3D points, each point has an associated value, I would like to interpolate these point to calculate the value of a random point. This example may help to understand: > from math import cos, sin > from scipy.interpolate import Rbf > from numpy import linspace, pi > from mpl_toolkits.mplot3d import Axes3D > import matplotlib.pyplot as plt > #%% Data > R = 10.0 > x = list() > y = list() > z = list() > c = list() > for i in linspace(0.0,10.0,11): > for t in linspace(0.0,2*pi,90): > x.append(R*cos(t)) > y.append(R*sin(t)) > z.append(i) > c.append(i) > #%% Plot > fig = plt.figure() > ax = fig.add_subplot(111, projection='3d') > sc = ax.scatter(x, y, z, c=z) > ax.set_xlabel('X') > ax.set_ylabel('Y') > ax.set_zlabel('Z') > plt.colorbar(sc) > #%% > rbfi = Rbf(x, y, z, c) > xi = [0,0] > yi = [0,0] > zi = [0,1] > print rbfi(xi, yi, zi) As you can see here we have some points over a cylinder, each point has associate a value (color), which in this case is the z-coordinate. scipy.interpolate.Rbf should answer my problem, to better understsand how it works I gave it some points [[0,0,0],[0,0,1]], but the result of rbfi(xi, yi, zi) is [-1.04833984 -0.10205078]. I expected that a point along the axis, aligned with some point should return the z-coordinate of this point [0, 1] but got some negative number. Any ideas? Do you have any alternative that I can use to have a linear interpolation or by the nearest? Thanks

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Cython-izing calls to scipy.integrate.odeint: is my minimal example a good way to go about it?
by Brian Merchant Feb. 21, 2016

Feb. 21, 2016

Hi all, The major bottleneck in my simulation code is the function supplied to `scipy.integrate.odeint` (call it `f`). `f` requires a lot of additional parameters (which are usually passed in as a tuple to the args keyword of `odeint`). Based on some stuff I found on the internet, I am thinking of making `f` a Cython extension type with the methods __init__ and __call__. Here's a minimal example: https://gist.github.com/bmer/10e6ec268677288b2d60 Now, I can pass `f` to `odeint` after I initialize it, and everything works okay (tried it out). Is this the best I can do in terms of efficiency? Kind regards, Brian

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