[Numpy-discussion] ANN: SfePy 2014.2

2014-05-23 Thread Robert Cimrman
I am pleased to announce release 2014.2 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. The code is based on NumPy and SciPy packages. It is distributed
under the new BSD license.

This release brings a preliminary support for isogeometric analysis - a
recently developed computational approach that allows using the NURBS-based
domain description from CAD design tools also for approximation purposes
similar to the finite element method.

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
--
- preliminary support for isogeometric analysis
- improved post-processing and visualization script for time-dependent problems
   with adaptive time steps
- three new terms

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):

Vladimír Lukeš
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Re: [Numpy-discussion] Possible bug in linalg.matrix_rank

2014-05-23 Thread Darlan Cavalcante Moreira
argri...@ncsu.edu writes:

 On Thu, May 22, 2014 at 1:50 PM, Darlan Cavalcante Moreira
 darc...@gmail.com wrote:

 After updating Ubuntu to 14.04 and thus numpy to version 1.8.1 I'm
 having problems with the linalg.matrix_rank function that I didn't have
 before (as far as I know).

 More specifically, I get the error
 TypeError: No loop matching the specified signature was found for ufunc 
 svd_m
 when I try to calculate the rank of a non-square complex matrix, but it does 
 not
 happen for any non-square matrix.


 Below you can find the cases tyhat work and the case that doesn't work.

 --8---cut here---start-8---
 # Real matrices
 A = np.random.randn(3,3);
 A = np.random.randn(4,3);
 A = np.random.randn(3,4);
 np.linalg.matrix_rank(A) # Works for any of the previous 'A' matrices

 # Complex matrices
 A = np.random.randn(3,3) + 1j*np.random.randn(3,3);
 A = np.random.randn(4,3) + 1j*np.random.randn(4,3);
 np.linalg.matrix_rank(A) # Works for any of the previous 'A' matrices

 # For the matrix below I get the error
 A = np.random.randn(3,4) + 1j*np.random.randn(3,4);
 np.linalg.matrix_rank(A) # Does not work and gives me the TypeError
 --8---cut here---end---8---


 --
 Darlan Cavalcante Moreira
 darc...@gmail.com
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 This regression should now be fixed in the numpy development version.
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Wow, that was fast. Thanks!

I just cloned the git repo and installed the development version and it
is working now.


-- 
Darlan Cavalcante Moreira
darc...@gmail.com
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