Blog entries

  • Reinteract: un outil intéressant pour faire du numpy/scipy

    2008/05/27 by Arthur Lutz

    Il existe un outil, Reinteract, qui permet d'avoir une sorte de d'éditeur/shell Python, où l'on peut aisément modifier et réinterpreter une ligne de code.

    Sachant qu'il sait aussi afficher des plots, etc, il est possible de s'en servir avantageusement pour faire des sessions Matlab-like.

    Je pense donc que c'est un outil à présenter à nos chers apprenants qui sont intéressés par le couple python/numpy comme substitut à Matlab ©®.


    écrit par David Douard

  • SciLab passe en logiciel libre

    2008/06/16 by Arthur Lutz

    Bienvenue à SciLab version 5.0 dans le monde du logiciel libre. SciLab 5.0, plateforme open source de calcul scientifique sous licence CeCill, est une alternative crédible et maintenant reconnue comme telle à Matlab. Pour assurer le développement pérenne de Scilab, le consortium Scilab rejoint DIGITEO, parc de recherche d'envergure mondiale dans le domaine des sciences et technologies de l'information en Île-de-France.

  • Python for applied Mathematics

    2008/07/29 by Nicolas Chauvat

    The presentation of Python as a tool for applied mathematics got highlighted at the 2008 annual meeting of the american Society for Industrial and Applied Mathematics (SIAM). For more information, read this blogpost and the slides.

  • SciPy and TimeSeries

    2008/08/04 by Nicolas Chauvat

    We have been using many different tools for doing statistical analysis with Python, including R, SciPy, specific C++ code, etc. It looks like the growing audience of SciPy is now in movement to have dedicated modules in SciPy (lets call them SciKits). See this thread in SciPy-user mailing-list.

  • Reading SPE files

    2009/05/11 by Andre Espaze

    If you would like to read SPE files from charge-coupled device (CCD) cameras, I have contributed a recipe to the SciPy cookbook, see Reading SPE files.

  • First contact with pupynere

    2009/11/06 by Pierre-Yves David

    I spent some time this week evaluating Pupynere, the PUre PYthon NEtcdf REader written by Roberto De Almeida. I see several advantages in pupynere.

    First it's a pure Python module with no external dependency. It doesn't even depend on the NetCDF lib and it is therefore very easy to deploy.

    Second, it offers the same interface as Scientific Python's NetCDF bindings which makes transitioning from one module to another very easy.

    Third pupynere is being integrated into Scipy as the module. Once integrated, this could ensure a wide adoption by the python community.

    Finally it's easy to dig in this clear and small code base of about 600 lines. I have just sent several fixes and bug reports to the author.

    However pupynere isn't mature yet. First it seems pupynere has been only used for simple cases so far. Many common cases are broken. Moreover there is no support for new NetCDF formats such as long-NetCDF and NetCDF4, and important features such as file update are still missing. In addition, The lack of a test suite is a serious issue. In my opinion, various bugs could already have been detected and fixed with simple unit tests. Contributions would be much more comfortable with the safety net offered by a test suite. I am not certain that the fixes and improvements I made this week did not introduce regressions.

    To conclude, pupynere seems too young for production use. But I invite people to try it and provide feedback and fixes to the author. I'm looking forward to using this project in production in the future.