Mailman 3 June 2006 - SciPy-User

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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3D plotting
by Robert Cimrman Sept. 10, 2009

Sept. 10, 2009

Hi, I need to do some simple 3D plots, so I tried the two Cookbook recipes (xplt, mplot3d) and failed: * xplt: does not work when using a custom installation directory such as python setup.py install --root=/home/share/software apparently the gistdata directory path is wrong: gist.error: failed to create drawing -- Gist work.gs style sheet missing * mplot3d: does not work with my version of matplotlib (0.80). I have made the changes mentioned in the Cookbook to no avail. (Axes.__init__() args apparently changed, as well as some other matplotlib object attributes) Any ideas? mplot3d looks great, I would really like to use it! thanks, r.

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Triangulation of L-shaped domains
by Nils Wagner Sept. 6, 2006

Sept. 6, 2006

Hi all, I have installed delaunay from the sandbox. How can I triangulate L-shaped domains ? My first try is somewhat unsatisfactory (delaunay.png) ? How can I remove the unwanted triangles down to the right ? Nils

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Mesh generator
by Nils Wagner Aug. 4, 2006

Aug. 4, 2006

Hi all, Is there something similar available for scipy ? http://www-math.mit.edu/~persson/mesh/ Nils

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updated Ubuntu Dapper packages for numpy, matplotlib, and scipy online
by Andrew Straw July 18, 2006

July 18, 2006

I have updated the apt repository I maintain for Ubuntu's Dapper, which now includes: numpy matplotlib scipy Each package is from a recent SVN checkout and should thus be regarded as "bleeding edge". The repository has a new URL: http://debs.astraw.com/dapper/ I intend to keep this repository online for an extended duration. If you want to put this repository in your sources list, you need to add the following lines to /etc/apt/sources.list:: deb http://debs.astraw.com/ dapper/ deb-src http://debs.astraw.com/ dapper/ I have not yet investigated the use of ATLAS in building or using the numpy binaries, and if performance is critical for you, please evaluate speed before using it. I intend to visit this issue, but I cannot say when. The Debian source packages were generated using stdeb, [ http://stdeb.python-hosting.com/ ] a Python to Debian source package conversion utility I wrote. stdeb does not build packages that follow the Debian Python Policy, so the packages here may be slighly unusual compared to Python packages in the official Debian or Ubuntu repositiories. For example, example scripts do not get installed, and no documentation is installed. Future releases of stdeb may resolve these issues. As always, feedback is very appreciated. Cheers! Andrew

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"Approximately equal to" operator?
by Martin Spacek July 7, 2006

July 7, 2006

Hello, I'm comparing two numpy 1d arrays of the same length. One has ints, the other floats. From what I can tell, using == isn't very reliable when mixing ints and floats. Sometimes 1.0 == 1, and sometimes it doesn't. For example: [mspacek]|71> arange(10, 13) <71> array([10, 11, 12]) [mspacek]|72> arange(10, 13, 0.1)[[0, 10, 20]] <72> array([ 10., 11., 12.]) [mspacek]|73> arange(10, 13, 0.1)[[0, 10, 20]] == arange(10,13) <73> array([True, False, False], dtype=bool) In this case, 10 == 10.0, but 11 != 11.0 and 12 != 12.0 I guess comparing ints to floats is a touchy thing, and depends on the system's C libraries. Maybe minuscule errors accumulate when building up a float array using arange(). Anyways, rounding and then converting the float array into an int array doesn't seem very Pythonic. I've noticed there's a numpy function allclose(), but this doesn't return a boolean array, just a single True if all the elements are equal. Is there a "~=" or "approximately equal to" operator that does an elementwise comparison of two arrays, and given a certain tolerance, returns a boolean array? Cheers, Martin

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Sparse matrix usage, documentation.
by Neilen Marais July 7, 2006

July 7, 2006

Hi The documentation for the sparse matrix classes seem to be, well, sparse. Does documentation exist somewhere I'm missing, or is it simply to early in the development process for much documentation to be around? I may be able to commit some once I understand what's going on though :) Right now I'd like to know more about the different sparse matrix types. I understand pretty much what coo, csc, and csr matrices are. From the docstrings I can more or less figure out what dok_matrix is, but what are dod_matrix and lnk_matrix? For naive matrix filling (i.e. if you don't know the sparsity pattern before hand) dok_matrix seems to be the best choice. Are there other useful formats for this purpose? When solving the matrix using iterative techniques, would it be better to convert it to one of the other sparse formats first, or can the dok matrix be solved efficiently itself? Thanks Neilen -- you know its kind of tragic we live in the new world but we've lost the magic -- Battery 9 (www.battery9.co.za)

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Struggling to make use of sparse
by William Hunter July 1, 2006

July 1, 2006

Old Matlab user here, I need some help on using 'sparse'. Not a lot of documentation on it, and I suck at programming, so there you go... I have a (sparse) array [K] and vector {F}. I need to solve for {U}. If these were 'normal' matrices, I would do the following: >>> import numpy as N >>> U = N.linalg.solve(K,F) I know how to get the matrices (both K and F) in sparse format with 'lil_matrix', but I get an error if I try the following: >>> import scipy.spare as SS >>> U = SS.sparse.solve(K,F) What am I doing wrong? Somebody who's done FEA type stuff will be able to answer. Thanks, William

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Modelica and Python
by Srijit Kumar Bhadra July 1, 2006

July 1, 2006

Hello, What are the possible options to use numpy and scipy with Modelica? Best Regards, Srijit __________________________________________________________ Yahoo! India Answers: Share what you know. Learn something new http://in.answers.yahoo.com/

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ANN: SciPy 2006 Conference Reminder
by Travis N. Vaught June 30, 2006

June 30, 2006

The *SciPy 2006 Conference* is scheduled for Thursday and Friday, August 17-18, 2006 at CalTech with Sprints and Tutorials Monday-Wednesday, August 14-16. Conference details are at http://www.scipy.org/SciPy2006 The deadlines for submitting abstracts and early registration are approaching... Call for Presenters ------------------- If you are interested in presenting at the conference, you may submit an abstract in Plain Text, PDF or MS Word formats to abstracts at scipy.org <http://mail.python.org/mailman/listinfo/python-announce-list> -- the deadline for abstract submission is July 7, 2006. Papers and/or presentation slides are acceptable and are due by August 4, 2006. Registration: ------------- Early registration ($100.00) is still available through July 14. You may register online at http://www.enthought.com/scipy06. Registration includes breakfast and lunch Thursday & Friday and a very nice dinner Thursday night. After July 14, 2006, registration will cost $150.00. Tutorials and Sprints --------------------- This year the Sprints (Monday and Tuesday, August 14-15) and Tutorials (Wednesday, August 16) are no additional charge (you're on your own for food on those days, though). Remember to include these days in your travel plans. The following topics are presented as Tutorials Wednesday (more info here: http://www.scipy.org/SciPy2006/TutorialSessions): - "3D visualization in Python using tvtk and MayaVi" - "Scientific Data Analysis and Visualization using IPython and Matplotlib." - "Building Scientific Applications using the Enthought Tool Suite (Envisage, Traits, Chaco, etc.)" - "NumPy (migration from Numarray & Numeric, overview of NumPy)" The Sprint topics are under discussion here: http://www.scipy.org/SciPy2006/CodingSprints See you in August! Travis

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